AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The gcp-to-aws skill provides a deterministic, multi-phase process to migrate Google Cloud Platform (GCP) workloads to AWS. It supports infrastructure migration via Terraform analysis and AI workload migration by mapping models to Amazon Bedrock. The skill enforces a strict state machine, requiring discovery, clarification, design, estimation, and generation phases to ensure a reliable migration path. It includes specific logic for handling hybrid stacks, AI provider transitions, and complex infrastructure clustering.
What it does
- Discovers GCP resources from Terraform, application code, or billing data.
- Clarifies migration requirements through a structured questionnaire.
- Designs AWS architecture based on GCP workload types and AI model requirements.
- Estimates costs for the proposed AWS infrastructure and AI services.
- Generates migration artifacts including Terraform configurations and AI provider adapters.
- Provides specialized guidance for migrating AI workloads from Gemini or OpenAI to Amazon Bedrock.
When to use it
- When you need to migrate existing GCP infrastructure to AWS.
- When you are looking to move AI-driven applications from GCP or OpenAI to Amazon Bedrock.
- When you have Terraform files or billing exports and need a structured migration plan.
- When you require a step-by-step, validated approach to re-platforming cloud services.
Advantages
- Enforces a rigorous, fail-closed state machine to prevent skipped steps.
- Provides intelligent mapping of GCP services to AWS equivalents.
- Includes specific logic for AI workload migration and model compatibility.
- Supports multi-signal discovery using IaC, code, and billing data.
- Ensures consistent artifact generation through a structured 6-phase process.
Usage examples
- “Migrate my GKE and Cloud SQL setup to AWS.”
- The agent initiates the discovery phase, analyzing your Terraform files to identify GKE and Cloud SQL resources, then guides you through the clarification and design phases for EKS and RDS.
- “I need to move my LangChain app from OpenAI to Bedrock.”
- The agent identifies the AI workload, selects the best-fit Bedrock model family, and generates the necessary provider adapters and migration documentation.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/cline/gcp-to-aws/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip gcp-to-aws.zip -d .agents/skills
Documentation by Cline
GCP-to-AWS Migration Skill
Philosophy
Re-platform by default: Select AWS services that match GCP workload types (e.g., Cloud Run → Fargate, Cloud SQL → RDS).
Dev sizing unless specified: Default to development-tier capacity (e.g., db.t4g.micro, single AZ). Upgrade only on user direction.
No human one-time migration costs: Do not present human labor, professional services, or people-time work as dollar estimates or "one-time migration cost" budget categories. Vendor charges grounded in data (for example GCP data transfer egress in the infra estimate when billing exists) are allowed.
Multi-signal approach: Design phase adapts based on available inputs — Terraform IaC for infrastructure, billing data for service mapping, and app code for AI workload detection.
BigQuery /
google_bigquery_*: The skill does not recommend a specific AWS analytics or warehouse service. During Clarify, if discovery shows BigQuery (IaCgoogle_bigquery_*and/or billing rows for BigQuery), you must surface the specialist advisory before Design (seereferences/phases/clarify/clarify.md). Design output usesDeferred — specialist engagement; keep directing the user to their AWS account team and/or a data analytics migration partner through Design, Estimate, and docs (seereferences/phases/design/design-infra.mdBigQuery specialist gate).
Definitions
"Load" = Read the file using the Read tool and follow its instructions. Do not summarize or skip sections.
$MIGRATION_DIR= The run-specific directory under.migration/(e.g.,.migration/0226-1430/). Set during Phase 1 (Discover).
Context Loading Rules
Each phase loads reference files on demand. To keep per-turn context manageable and prevent instruction-following degradation:
Budget: Each phase should load no more than ~800 lines of instructions (excluding user artifacts like JSON profiles and MCP tool results).
Conditional loading: Reference files with trigger conditions (e.g.,
agentic_profile.is_agentic == true) MUST NOT be loaded unless the condition is met. Do not speculatively load files.No duplication: Model mapping tables, pricing data, and shared warnings exist in one canonical file. Other files reference them; they do not copy them inline.
Progressive depth: Phase orchestrators (
design.md,generate.md) contain short routing logic that points to detailed sub-files. Load the sub-file only when its path is selected.
Conditional reference files (load ONLY when condition is true):
| File | Condition |
|---|---|
design-refs/ai-gemini-to-bedrock.md | ai-workload-profile.json exists AND summary.ai_source = "gemini" or "both" |
design-refs/ai-openai-to-bedrock.md | ai-workload-profile.json exists AND summary.ai_source = "openai" or "both" |
design-refs/ai-anthropic-to-bedrock.md | ai-workload-profile.json exists AND summary.ai_source = "anthropic" |
design-refs/ai.md | ai-workload-profile.json exists AND summary.ai_source = "other" |
design-refs/design-ref-harness.md | agentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "harness" |
design-refs/design-ref-agentic-to-agentcore.md | agentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "strands" |
shared/retarget-gotchas.md | agentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "retarget" |
When adding new reference files, verify the phase's total loaded instructions remain under budget. If a new file would exceed ~800 lines when combined with other loaded refs, split it or make it conditional.
Hybrid stack budget warning:
When both gcp-resource-inventory.json AND ai-workload-profile.json exist, the combined design refs will approach the ~800-line budget. Output this warning to the user before loading the AI design refs:
"⚠️ This is a large hybrid stack (infrastructure + AI workloads). To ensure complete and accurate recommendations, consider running the migration in two separate passes:
Pass 1 — Infrastructure: Run with only your Terraform files to get infra mapping, Terraform generation, and cost estimates.
Pass 2 — AI workloads: Run with only your application code to get Bedrock model recommendations, provider adapters, and AI migration artifacts.
Continue with the combined run? (Y/N)"
If the user chooses to continue, proceed with the combined run. Load AI refs after infra refs to preserve infra instruction fidelity. If the user declines, stop and instruct them to re-run with a single input source type.
This warning is advisory only — it does not block the run.
Prerequisites
User must provide at least one GCP source:
Terraform IaC:
.tffiles (with optional.tfvars,.tfstate)Application code: Source files with GCP SDK or AI framework imports
Billing data: GCP billing/cost/usage export files (CSV or JSON)
If none of the above are found, stop and ask user to provide at least one source type.
State Machine
This is the execution controller. After completing each phase, consult this table to determine the next action.
| Current State | Condition | Next Action |
|---|---|---|
discover | phases.discover != "completed" | Load references/phases/discover/discover.md |
clarify | phases.discover == "completed" AND phases.clarify != "completed" | Load references/phases/clarify/clarify.md |
design | phases.clarify == "completed" AND phases.design != "completed" | Load references/phases/design/design.md |
estimate | phases.design == "completed" AND phases.estimate != "completed" | Load references/phases/estimate/estimate.md |
generate | phases.estimate == "completed" AND phases.generate != "completed" | Load references/phases/generate/generate.md |
complete | phases.generate == "completed" AND phases.feedback == "pending" | Set phases.feedback to "completed" (user had two chances), then migration complete |
complete | phases.generate == "completed" AND phases.feedback == "completed" | Migration planning complete |
How to determine current state (deterministic):
Read
$MIGRATION_DIR/.phase-status.jsonIf
current_phaseexists, use it (must match one of: discover, clarify, design, estimate, generate, complete)Otherwise use ordered phase evaluation:
discover→clarify→design→estimate→generatePick the first phase in that order where
phases.<phase> != "completed"; if none, state iscomplete
Phase gate checks: If prior phase incomplete, do not advance (e.g., cannot enter estimate without completed design).
Clarify is mandatory: Do not load references/phases/design/design.md, references/phases/estimate/estimate.md, or references/phases/generate/generate.md unless $MIGRATION_DIR/.phase-status.json exists and phases.clarify is exactly "completed". A preferences.json file alone is not sufficient proof that Clarify ran. If the user asks to skip Clarify or jump straight to Design, cost estimate, or artifact generation, refuse briefly, then load references/phases/clarify/clarify.md and run Phase 2. There is no exception for "quick" or "obvious" migrations.
Feedback checkpoints: Feedback is not a sequential phase — it is offered at two interleaved checkpoints (after Discover and after Estimate). See the Feedback Checkpoints section below for details.
Handoff Gate Orchestration (Fail Closed)
Load references/shared/handoff-gates.md when executing any phase completion step.
Single
$MIGRATION_DIR: Use one run directory for the entire migration. Do not mix artifacts across.migration/*/sessions.Re-read from disk: Before each phase (and before each handoff gate), Read required artifacts from
$MIGRATION_DIR/. Do not rely on chat memory.Advance only on
HANDOFF_OK: A phase is complete only when its orchestrator emitsHANDOFF_OK | phase=<name> | artifacts=.... Do not load the next phase without it.On
GATE_FAIL: Output the failure line(s) to the user in plain language. Do NOT modify artifacts to pass the gate. Do NOT continue to the next phase. Tell the user which phase to re-run.Re-entry: Re-running an earlier phase after downstream phases completed requires explicit user confirmation; downstream phases must be reset to
"pending". Seehandoff-gates.mdre-entry table.
Generate phase additionally loads references/shared/validate-artifacts.md before writing migration-report.html.
State Validation
When reading $MIGRATION_DIR/.phase-status.json, validate before proceeding:
Multiple sessions: If multiple directories exist under
.migration/, list them with their phase status and ask: [A] Resume latest, [B] Start fresh, [C] Cancel.Invalid JSON: If
.phase-status.jsonfails to parse, STOP. Output: "State file corrupted (invalid JSON). Delete the file and restart the current phase."Unrecognized phase: If
phasesobject contains a phase not in {discover, clarify, design, estimate, generate, feedback}, STOP. Output: "Unrecognized phase: [value]. Valid phases: discover, clarify, design, estimate, generate, feedback."Unrecognized status: If any
phases.*value is not in {pending, in_progress, completed}, STOP. Output: "Unrecognized status: [value]. Valid values: pending, in_progress, completed."Invalid
current_phase(if present): Ifcurrent_phaseis not in {discover, clarify, design, estimate, generate, complete}, STOP. Output: "Unrecognized current_phase: [value]. Valid values: discover, clarify, design, estimate, generate, complete."Out-of-order completion: For ordered phases [discover, clarify, design, estimate, generate], if any later phase is
"completed"while an earlier phase is not"completed", STOP. Output: "Inconsistent phase ordering detected. Reconcile.phase-status.jsonbefore resuming."Multiple active phases: Across core phases {discover, clarify, design, estimate, generate}, at most one phase may be
"in_progress". If >1, STOP. Output: "Multiple phases are in_progress. Keep only one active phase before resuming."
State Management
Migration state lives in $MIGRATION_DIR (.migration/[MMDD-HHMM]/), created by Phase 1 and persisted across invocations.
.phase-status.json schema:
{
"migration_id": "0226-1430",
"last_updated": "2026-02-26T15:35:22Z",
"current_phase": "design",
"phases": {
"discover": "completed",
"clarify": "completed",
"design": "in_progress",
"estimate": "pending",
"generate": "pending",
"feedback": "pending"
}
}Status values: "pending" → "in_progress" → "completed". Never goes backward.
For core phases (discover, clarify, design, estimate, generate), at most one phase may be "in_progress" at any time.
current_phase is optional but recommended; when present it is authoritative.
The .migration/ directory is automatically protected by a .gitignore file created in Phase 1.
Phase Status Update Protocol
Use read-merge-write updates for .phase-status.json:
Read the current file before every update.
Change only the phase keys being advanced and
last_updated.Keep prior completed phases unchanged.
Set
current_phaseto the next deterministic phase (orcompleteafter generate).Write the full file in the same turn as your final phase work message.
Example — after completing the Clarify phase, write $MIGRATION_DIR/.phase-status.json with:
{
"migration_id": "MMDD-HHMM",
"last_updated": "2026-02-26T15:35:22Z",
"current_phase": "design",
"phases": {
"discover": "completed",
"clarify": "completed",
"design": "pending",
"estimate": "pending",
"generate": "pending",
"feedback": "pending"
}
}Replace MMDD-HHMM with the actual migration ID, generate the last_updated ISO 8601 UTC timestamp yourself, and set each phase to its correct status at that point.
Phase Summary Table
| Phase | Inputs | Outputs | Reference |
|---|---|---|---|
| Discover | .tf files, app source code, and/or billing exports (at least one required) | gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json, .phase-status.json updated (outputs vary by input) | references/phases/discover/discover.md |
| Clarify | Discovery artifacts (gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json — whichever exist) | preferences.json, .phase-status.json updated | references/phases/clarify/clarify.md |
| Design | preferences.json + discovery artifacts | aws-design.json (infra), aws-design-ai.json (AI), aws-design-billing.json (billing-only) | references/phases/design/design.md |
| Estimate | aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.json | estimation-infra.json or estimation-ai.json or estimation-billing.json, .phase-status.json updated | references/phases/estimate/estimate.md |
| Generate | estimation-infra.json or estimation-ai.json or estimation-billing.json, aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.json | generation-infra.json or generation-ai.json or generation-billing.json + terraform/, scripts/, ai-migration/, validation-report.json (when infra route active), MIGRATION_GUIDE.md, README.md, .phase-status.json updated | references/phases/generate/generate.md |
| Feedback | .phase-status.json (discover completed minimum), all existing migration artifacts | feedback.json, trace.json, .phase-status.json updated | references/phases/feedback/feedback.md |
MCP Servers
awspricing (for cost estimation):
Provides
get_pricing,get_pricing_service_codes,get_pricing_service_attributestoolsOnly needed during Estimate phase. Discover and Design do not require it.
Primary pricing source:
references/shared/pricing-cache.md(cached 2026 rates, ±5-10% for infrastructure, ±15-25% for AI models). MCP is secondary — used only for services not found in the cache.
Files in This Skill
gcp-to-aws/
├── SKILL.md ← You are here (orchestrator + state machine)
│
├── references/
│ ├── phases/
│ │ ├── discover/
│ │ │ ├── discover.md # Phase 1: Discover orchestrator
│ │ │ ├── discover-iac.md # Terraform/IaC discovery
│ │ │ ├── discover-app-code.md # App code discovery
│ │ │ └── discover-billing.md # Billing data discovery
│ │ ├── clarify/
│ │ │ ├── clarify.md # Phase 2: Clarify orchestrator
│ │ │ ├── clarify-global.md # Category A: Global/Strategic (Q1-Q7)
│ │ │ ├── clarify-compute.md # Categories B+C: Config Gaps + Compute (Q8-Q11)
│ │ │ ├── clarify-database.md # Category D: Database (Q12–Q13b)
│ │ │ ├── clarify-ai.md # Category F: AI/Bedrock (Q14-Q22)
│ │ │ └── clarify-ai-only.md # Standalone AI-only migration flow
│ │ ├── design/
│ │ │ ├── design.md # Phase 3: Design orchestrator
│ │ │ ├── design-infra.md # Infrastructure design (IaC-based)
│ │ │ ├── design-ai.md # AI workload design (Bedrock)
│ │ │ └── design-billing.md # Billing-only design (fallback)
│ │ ├── estimate/
│ │ │ ├── estimate.md # Phase 4: Estimate orchestrator
│ │ │ ├── estimate-infra.md # Infrastructure cost analysis
│ │ │ ├── estimate-ai.md # AI workload cost analysis
│ │ │ └── estimate-billing.md # Billing-only cost analysis
│ │ ├── generate/
│ │ │ ├── generate.md # Phase 5: Generate orchestrator
│ │ │ ├── generate-infra.md # Infrastructure migration plan
│ │ │ ├── generate-ai.md # AI migration plan
│ │ │ ├── generate-billing.md # Billing-only migration plan
│ │ │ ├── generate-artifacts-infra.md # Terraform configurations
│ │ │ ├── generate-artifacts-scripts.md # Migration scripts
│ │ │ ├── generate-artifacts-ai.md # Provider adapter + test harness
│ │ │ ├── generate-artifacts-billing.md # Skeleton Terraform
│ │ │ └── generate-artifacts-docs.md # MIGRATION_GUIDE.md + README.md
│ │ └── feedback/
│ │ ├── feedback.md # Phase 6: Feedback orchestrator
│ │ └── feedback-trace.md # Anonymized trace builder
│ │
│ ├── design-refs/
│ │ ├── index.md # Lookup table: GCP type → design-ref file
│ │ ├── fast-path.md # Deterministic 1:1 mappings (Pass 1)
│ │ ├── compute.md # Compute mappings (Cloud Run, GCE, GKE, etc.)
│ │ ├── database.md # Database mappings (Cloud SQL, Spanner, etc.)
│ │ ├── storage.md # Storage mappings (GCS, Filestore, etc.)
│ │ ├── networking.md # Networking mappings (VPC, LB, DNS, etc.)
│ │ ├── messaging.md # Messaging mappings (Pub/Sub, etc.)
│ │ └── ai.md # AI mappings (Vertex AI → Bedrock)
│ │
│ ├── clustering/terraform/
│ │ ├── classification-rules.md # Primary/secondary classification
│ │ ├── clustering-algorithm.md # Cluster formation rules
│ │ ├── depth-calculation.md # Topological depth calculation
│ │ └── typed-edges-strategy.md # Edge type assignment
│ │
│ └── shared/
│ ├── schema-phase-status.md # .phase-status.json schema (canonical reference)
│ ├── schema-discover-iac.md # gcp-resource-inventory + clusters schemas (loaded by discover-iac.md)
│ ├── schema-discover-ai.md # ai-workload-profile schema (loaded by discover-app-code.md and discover-iac.md Step 7d)
│ ├── schema-discover-billing.md # billing-profile schema (loaded by discover-billing.md)
│ ├── schema-estimate-infra.md # estimation-infra.json schema (loaded by estimate-infra.md at write time)
│ ├── handoff-gates.md # Fail-closed phase handoff protocol (GATE_FAIL / HANDOFF_OK)
│ ├── validate-artifacts.md # Pre-report validation (Generate Step 0; read-only)
│ ├── migration-complexity.md # Complexity tier definitions (small/medium/large) for timeline scaling
│ ├── pricing-cache.md # Cached AWS + source provider pricing (±5-25%, primary source)
│ └── bedrock-quotas.md # Bedrock TPM/RPM quota awareness, burndown rates, capacity planning| Condition | Action |
|---|---|
No GCP sources found (no .tf, no app code, no billing data) | Stop. Output: "No GCP sources detected. Provide at least one source type (Terraform files, application code, or billing exports) and try again." |
.phase-status.json missing phase gate | Stop. Output: "Cannot enter Phase X: Phase Y-1 not completed. Start from Phase Y or resume Phase Y-1." |
| awspricing unavailable after 3 attempts | Display user warning about ±5-25% accuracy. Use pricing-cache.md. Add pricing_source: "cached_fallback" to the applicable estimation-*.json file. |
| User skips questions or says "use defaults for the rest" | Apply documented defaults for remaining questions in the current batch and all subsequent batches. Phase 2 completes either way. |
aws-design.json missing required clusters | Stop Phase 4. Output: "Re-run Phase 3 to generate missing cluster designs." |
Defaults
IaC output: Terraform configurations, migration scripts, AI migration code, and documentation
Region:
us-east-1(unless user specifies, or GCP region → AWS region mapping suggests otherwise)Sizing: Development tier (e.g.,
db.t4g.microfor databases, 0.5 CPU for Fargate)Migration mode: Adapts based on available inputs (infrastructure, AI, or billing-only)
Cost currency: USD
Timeline assumption: 2-16 weeks depending on migration complexity — small (2-6 weeks), medium (6-12 weeks), large (12-18 weeks). See
references/shared/migration-complexity.mdfor tier definitions.
Workflow Execution
When invoked, the agent MUST follow this exact sequence:
Load phase status: Read
.phase-status.jsonfrom.migration/*/.If missing: Initialize for Phase 1 (Discover)
If exists: Determine current phase using deterministic rules in State Machine
Determine phase to execute:
If
current_phaseexists: execute that phase.Otherwise execute the first non-completed phase in ordered list: discover → clarify → design → estimate → generate.
If all ordered phases are completed: migration is complete (with feedback finalization rule).
Read phase reference: Load the full reference file for the target phase.
Execute ALL steps in order: Follow every numbered step in the reference file. Do not skip, optimize, or deviate.
Validate outputs: Confirm all required output files exist with correct schema before proceeding. Phase orchestrators run Completion Handoff Gate checks per
shared/handoff-gates.md.Handoff gate: Emit
HANDOFF_OKorGATE_FAILpershared/handoff-gates.md. OnGATE_FAIL, stop — do not update phase status or load the next phase.Update phase status: Only after
HANDOFF_OK. Use the Phase Status Update Protocol (read-merge-write) in the same turn as the phase's final output message.Feedback checkpoint: After a phase completes, check if feedback is due (see rules below). This runs before advancing to the next phase.
After Discover (if
phases.feedbackis"pending"): Output to user: "Would you like to share quick feedback (5 optional questions + anonymized usage data) to help improve this tool? Your data never includes resource names, file paths, or account IDs. [A] Send feedback now [B] Wait until after the Estimate phase"If user picks A → Load
references/phases/feedback/feedback.md, execute it, then continue to Clarify.If user picks B → Continue to Clarify (feedback stays
"pending").
After Estimate (if
phases.feedbackis"pending"): Output to user: "Would you like to share quick feedback now? (5 optional questions + anonymized usage data) [A] Yes, share feedback [B] No thanks, continue to Generate"If user picks A → Load
references/phases/feedback/feedback.md, execute it, then continue to Generate.If user picks B → Use the Phase Status Update Protocol to set
phases.feedbackto"completed". Continue to Generate.
After Generate: No feedback offer. If
phases.feedbackis still"pending", use the Phase Status Update Protocol to set it to"completed"(user had two chances and chose to defer/skip).
Display summary: Show user what was accomplished, highlight next phase, or confirm migration completion.
Critical constraint: Agent must strictly adhere to the reference file's workflow. If unable to complete a step, stop and report the exact step that failed.
User can invoke the skill again to resume from current_phase (or deterministic ordered evaluation when current_phase is absent).
Scope Notes
v1.0 includes:
Terraform infrastructure discovery
App code scanning (AI workload detection)
Billing data import from GCP
User requirement clarification (adaptive questions by category)
Multi-path Design (infrastructure, AI workloads, billing-only fallback)
AWS cost estimation (from pricing API or fallback)
Migration artifact generation (Terraform, scripts, AI adapters, documentation)
Optional feedback collection with anonymized telemetry
Files
- ATTRIBUTION.mdAdded by AutoGineer826 B
- LICENSELicence file added by AutoGineer10.5 KiB
- SKILL.md29.6 KiB
- data/sdk-capability-map.json1.1 KiB
- references/clustering/terraform/classification-rules.md6.5 KiB
- references/clustering/terraform/clustering-algorithm.md7.9 KiB
- references/clustering/terraform/depth-calculation.md4.5 KiB
- references/clustering/terraform/typed-edges-strategy.md4.6 KiB
- references/design-refs/ai-anthropic-to-bedrock.md2.9 KiB
- references/design-refs/ai-gemini-to-bedrock.md15.4 KiB
- references/design-refs/ai-openai-to-bedrock.md17.5 KiB
- references/design-refs/ai.md3.4 KiB
- references/design-refs/compute.md6.8 KiB
- references/design-refs/database.md9.1 KiB
- references/design-refs/design-ref-agentic-to-agentcore.md18.8 KiB
- references/design-refs/design-ref-harness.md7.9 KiB
- references/design-refs/fast-path.md10.3 KiB
- references/design-refs/index.md6.2 KiB
- references/design-refs/messaging.md3.5 KiB
- references/design-refs/networking.md5.5 KiB
- references/design-refs/security.md3.8 KiB
- references/design-refs/storage.md4.7 KiB
- references/phases/clarify/clarify-ai-only.md16.4 KiB
- references/phases/clarify/clarify-ai.md57.7 KiB
- references/phases/clarify/clarify-compute.md10.0 KiB
- references/phases/clarify/clarify-database.md10.8 KiB
- references/phases/clarify/clarify-global.md23.7 KiB
- references/phases/clarify/clarify.md50.5 KiB
- references/phases/design/design-ai.md29.6 KiB
- references/phases/design/design-billing.md12.7 KiB
- references/phases/design/design-infra.md12.8 KiB
- references/phases/design/design.md4.5 KiB
- references/phases/discover/discover-app-code.md41.7 KiB
- references/phases/discover/discover-billing.md7.2 KiB
- references/phases/discover/discover-iac.md33.0 KiB
- references/phases/discover/discover-preview.md19.9 KiB
- references/phases/discover/discover.md11.5 KiB
- references/phases/estimate/estimate-ai.md17.2 KiB
- references/phases/estimate/estimate-billing.md12.4 KiB
- references/phases/estimate/estimate-infra.md46.6 KiB
- references/phases/estimate/estimate.md8.1 KiB
- references/phases/feedback/feedback-trace.md4.5 KiB
- references/phases/feedback/feedback.md3.2 KiB
- references/phases/generate/generate-ai.md13.5 KiB
- references/phases/generate/generate-artifacts-ai.md33.6 KiB
- references/phases/generate/generate-artifacts-billing.md6.1 KiB
- references/phases/generate/generate-artifacts-docs.md21.0 KiB
- references/phases/generate/generate-artifacts-infra.md43.2 KiB
- references/phases/generate/generate-artifacts-report.md40.1 KiB
- references/phases/generate/generate-artifacts-scripts.md14.5 KiB
- references/phases/generate/generate-billing.md16.5 KiB
- references/phases/generate/generate-infra.md22.0 KiB
- references/phases/generate/generate.md7.9 KiB
- references/shared/ai-migration-guardrails.md6.6 KiB
- references/shared/ai-model-lifecycle.md8.8 KiB
- references/shared/bedrock-quotas.md7.1 KiB
- references/shared/handoff-gates.md4.7 KiB
- references/shared/migration-complexity.md11.0 KiB
- references/shared/pricing-cache.md41.2 KiB
- references/shared/pricing-fallback.md1.5 KiB
- references/shared/retarget-gotchas.md10.9 KiB
- references/shared/schema-discover-ai.md16.7 KiB
- references/shared/schema-discover-billing.md5.0 KiB
- references/shared/schema-discover-iac.md6.9 KiB
- references/shared/schema-estimate-infra.md15.6 KiB
- references/shared/schema-phase-status.md1.2 KiB
- references/shared/terraform-validation.md10.4 KiB
- references/shared/validate-artifacts.md6.2 KiB
references/shared/pricing-cache.md
AWS Pricing Cache
Last updated: 2026-05-20 Region: us-east-1 Currency: USD Accuracy: ±5-10% for infrastructure services (sourced from AWS Price List API), ±15-25% for AI models (sourced from public pricing pages)
Prices may vary by region and change over time. Use for estimation only. For real-time pricing, fall back to the AWS Pricing MCP server. Amazon Nova figures in the Bedrock subsection often reference US East (Ohio) and inference mode (global vs geo); other services in this file default to us-east-1 unless noted. Staleness warning: If today's date is more than 30 days after the Last updated date above, treat AI model prices as potentially stale (±15-25% accuracy may widen). Infrastructure prices (Fargate, RDS, S3, etc.) change rarely and remain reliable longer. When staleness is detected, set
pricing_source: "cached_stale"in the estimate output and note: "Pricing cache is more than 30 days old — AI model prices may have changed. Verify via the AWS Pricing MCP server or aws.amazon.com/bedrock/pricing."
Compute
Fargate
| Metric | Rate |
|---|---|
| Per vCPU-hour | $0.04048 |
| Per GB memory-hour | $0.004445 |
Linux/x86, on-demand.
Lambda
| Metric | Rate |
|---|---|
| Per request | $0.0000002 |
| Per GB-second (first 6B) | $0.0000166667 |
| Per GB-second (over 6B) | $0.000015 |
| Free tier | 1M requests + 400K GB-sec/month |
EKS
| Metric | Rate |
|---|---|
| Cluster fee per hour | $0.10 |
| Cluster fee per month | $73.00 |
Worker nodes billed separately as EC2 or Fargate.
EC2 (On-Demand, Linux)
| Instance | $/hour | $/month |
|---|---|---|
| t3.micro | 0.0104 | 7.58 |
| t3.small | 0.0208 | 15.17 |
| t3.medium | 0.0416 | 30.34 |
| t3.large | 0.0832 | 60.68 |
| m5.large | 0.096 | 70.08 |
| m5.xlarge | 0.192 | 140.16 |
| c5.large | 0.085 | 62.05 |
| c5.xlarge | 0.17 | 124.10 |
Database
Aurora PostgreSQL (On-Demand)
Aurora replicates across 3 AZs by default. Pricing is listed as Single-AZ — do NOT use Multi-AZ filter with MCP.
| Instance | $/hour |
|---|---|
| db.t4g.medium | 0.073 |
| db.t4g.large | 0.146 |
| db.r6g.large | 0.26 |
| db.r6i.large | 0.29 |
| db.r7g.xlarge | 0.553 |
| db.r8g.large | 0.276 |
| db.r8g.xlarge | 0.552 |
| db.r8g.2xlarge | 1.104 |
| db.r8g.4xlarge | 2.208 |
| db.r8g.8xlarge | 4.416 |
| Storage/IO | Rate |
|---|---|
| Storage per GB-month | $0.10 |
| I/O per million requests | $0.20 |
Aurora MySQL (On-Demand)
Same Multi-AZ note as Aurora PostgreSQL.
| Instance | $/hour |
|---|---|
| db.t4g.medium | 0.073 |
| db.t4g.large | 0.146 |
Storage and I/O same as Aurora PostgreSQL.
Aurora Serverless v2
Scales between min and max ACU. Both PostgreSQL and MySQL cost the same per ACU.
| Metric | Rate |
|---|---|
| Standard per ACU-hour | $0.12 |
| I/O Optimized per ACU-hour | $0.16 |
| Storage per GB-month | $0.10 |
| I/O per million requests | $0.20 |
Min ACU = 0.5, scales to 256 ACU.
RDS PostgreSQL (On-Demand, Multi-AZ)
| Instance | $/hour |
|---|---|
| db.t4g.micro | 0.032 |
| db.t4g.small | 0.065 |
| db.t4g.medium | 0.129 |
| db.t4g.large | 0.258 |
| db.t4g.xlarge | 0.517 |
| db.t4g.2xlarge | 1.034 |
| Storage | Rate |
|---|---|
| Per GB-month | $0.23 |
RDS MySQL (On-Demand, Single-AZ)
For Multi-AZ, approximately double these rates.
| Instance | $/hour | $/month |
|---|---|---|
| db.t3.small | 0.034 | 24.82 |
| db.t3.medium | 0.068 | 49.64 |
| db.t3.large | 0.136 | 99.28 |
| db.t4g.micro | 0.016 | 11.68 |
| db.t4g.small | 0.032 | 23.36 |
| db.t4g.medium | 0.065 | 47.45 |
| db.m5.large | 0.171 | 124.83 |
| Storage | Rate |
|---|---|
| Per GB-month | $0.23 |
| Backup per GB-month | $0.023 |
DynamoDB (On-Demand)
| Metric | Rate |
|---|---|
| Read per million RRU | $0.125 |
| Write per million WRU | $0.625 |
| Storage per GB-month | $0.25 |
ElastiCache Redis (On-Demand)
Single-AZ pricing. For Multi-AZ, approximately double.
| Node | $/hour | $/month |
|---|---|---|
| cache.t3.micro | 0.017 | 12.41 |
| cache.t3.small | 0.034 | 24.82 |
| cache.t3.medium | 0.068 | 49.64 |
| cache.t4g.micro | 0.016 | — |
| cache.t4g.small | 0.032 | — |
| cache.t4g.medium | 0.065 | — |
| cache.r6g.large | 0.206 | 150.38 |
Storage
S3
| Tier | Rate per GB-month |
|---|---|
| Standard (first 50 TB) | $0.023 |
| Standard (next 450 TB) | $0.022 |
| Standard (over 500 TB) | $0.021 |
| Standard-IA | $0.0125 |
| Glacier Flexible Retrieval | $0.0036 |
| Requests | Rate |
|---|---|
| PUT per 1K | $0.005 |
| GET per 1K | $0.0004 |
| S3-IA retrieval per GB | $0.01 |
| Glacier retrieval per GB | $0.01 |
Networking
Application Load Balancer
| Metric | Rate |
|---|---|
| Per ALB-hour | $0.0225 |
| Per LCU-hour | $0.008 |
| Monthly fixed | $16.43 |
Network Load Balancer
| Metric | Rate |
|---|---|
| Per NLB-hour | $0.0225 |
| Per LCU-hour | $0.006 |
| Monthly fixed | $16.43 |
NAT Gateway
| Metric | Rate |
|---|---|
| Per hour | $0.045 |
| Per GB processed | $0.045 |
| Monthly fixed | $32.85 |
VPC
VPC itself is free. Add-ons:
| Component | Rate |
|---|---|
| VPN connection per hour | $0.05 |
| VPN monthly | $36.50 |
| Interface endpoint per hour | $0.01 |
| Interface endpoint monthly | $7.30 |
Route 53
| Metric | Rate |
|---|---|
| Hosted zone per month | $0.50 |
| Per million standard queries | $0.40 |
| Per million latency queries | $0.60 |
| Health check per month | $0.50 |
CloudFront (US/Europe)
| Metric | Rate |
|---|---|
| Per GB transfer (first 10 TB) | $0.085 |
| Per 10K HTTPS requests | $0.01 |
| Free tier | 1 TB transfer/month |
Supporting Services
Secrets Manager
| Metric | Rate |
|---|---|
| Per secret per month | $0.40 |
| Per 10K API calls | $0.05 |
CloudWatch
| Metric | Rate | Notes |
|---|---|---|
| Log ingestion per GB (Standard) | $0.50 | |
| Log ingestion per GB (Infrequent Access) | $0.25 | 50% cheaper than Standard; no Live Tail, subscription filters, or metric filters |
| Log storage per GB-month | $0.03 | Same for both Standard and Infrequent Access |
| Insights query per GB scanned | $0.005 | Same for both log classes |
| Custom metric per month (≤10K) | $0.30 | Flat rate at startup scale; $0.10 for 10K–250K, $0.05 for 250K+ |
| Standard alarm per month | $0.10 | |
| High-resolution alarm per month | $0.30 | |
| Dashboard per month (first 3 free) | $3.00 |
Free tier (not subtracted in estimates — startup apps often exceed quickly):
5 GB log ingestion + archive + Insights queries
10 custom metrics + 10 standard alarms
3 dashboards (50 metrics each)
X-Ray
| Metric | Rate | Notes |
|---|---|---|
| Traces recorded per million | $5.00 | First 100K free/month |
| Traces retrieved per million | $0.50 | First 1M free/month |
| Traces scanned per million | $0.50 | First 1M free/month |
CloudWatch Container Insights (ECS/Fargate)
| Metric | Rate | Notes |
|---|---|---|
| Per-task performance log/GB | $0.50 | Same as standard log ingestion |
| Cluster/service/task metrics | $0.30 | Per custom metric — can accumulate quickly |
SQS
| Metric | Rate |
|---|---|
| Standard per million requests | $0.40 |
| FIFO per million requests | $0.50 |
| Free tier | 1M requests/month |
SNS
| Metric | Rate |
|---|---|
| Per million publishes | $0.50 |
| SQS delivery per million | $0.00 |
| HTTP delivery per million | $0.60 |
| Free tier | 1M publishes/month |
EventBridge
| Metric | Rate |
|---|---|
| Per million events | $1.00 |
Analytics
Redshift Serverless
| Metric | Rate |
|---|---|
| Per RPU-hour | $0.375 |
| Storage per GB-month | $0.024 |
Minimum 8 RPU base capacity.
Athena
| Metric | Rate |
|---|---|
| Per TB scanned | $5.00 |
Columnar formats (Parquet, ORC) and partitioning reduce scan volume.
SageMaker
| Training Instance | $/hour |
|---|---|
| ml.m5.large | 0.115 |
| ml.m5.xlarge | 0.23 |
| ml.g4dn.xlarge (GPU) | 0.736 |
| Inference Instance | $/hour | $/month |
|---|---|---|
| ml.t3.medium | 0.05 | 36.50 |
| ml.m5.large | 0.115 | 83.95 |
Serverless inference: $0.0000200 per second per GB memory.
Bedrock Models (On-Demand)
Anthropic Claude (Standard on-demand) figures below match US East (N. Virginia) on Amazon Bedrock pricing as of cache refresh. Claude Opus 4.7 lists the same headline on-demand input/output as Opus 4.6 on that page; confirm batch availability per model (Opus 4.7 batch was not listed on the global cross-region table when this row was added). Batch, prompt cache (5m / 1h write + cache read), and geo / in-region cross-region rows on that page can differ; e.g. US East (Ohio) cross-region inference for Claude Sonnet 4.6 is listed at $3.30 / $16.50 per 1M input/output (≈10% above N. Virginia). Long-context SKUs do not all use the same multiplier: Sonnet 4.6 and Opus 4.6 long-context modes share the same on-demand rates as the non–long-context rows on the standard table; Sonnet 4.5 and Sonnet 4 long-context rows are priced higher on that same table.
Multi-provider quick reference (per 1M tokens)
See shared/ai-model-lifecycle.md for lifecycle details. Do not recommend Legacy models for new migrations.
| Model | Model ID | Provider | Input $/1M | Output $/1M | Context | Tier | Status |
|---|---|---|---|---|---|---|---|
| Claude Sonnet 4.6 | anthropic.claude-sonnet-4-6 | Anthropic | 3.00 | 15.00 | 200K | flagship | active |
| Claude Sonnet 4.6 — Long Context | anthropic.claude-sonnet-4-6 | Anthropic | 3.00 | 15.00 | 200K+ | flagship | active |
| Claude Opus 4.6 | anthropic.claude-opus-4-6-v1 | Anthropic | 5.00 | 25.00 | 200K | premium | active |
| Claude Opus 4.6 — Long Context | anthropic.claude-opus-4-6-v1 | Anthropic | 5.00 | 25.00 | 200K+ | premium | active |
| Claude Opus 4.5 | — | Anthropic | 5.00 | 25.00 | 200K | premium | active |
| Claude Sonnet 4.5 | — | Anthropic | 3.00 | 15.00 | 200K | flagship | active |
| Claude Sonnet 4.5 — Long Context | — | Anthropic | 6.00 | 22.50 | 200K+ | flagship | active |
| Claude Haiku 4.5 | anthropic.claude-haiku-4-5-20251001-v1:0 | Anthropic | 1.00 | 5.00 | 200K | fast | active |
| Llama 4 Maverick | meta.llama4-maverick-17b-instruct-v1:0 | Meta | 0.24 | 0.97 | 1M | mid | active |
| Llama 4 Scout | meta.llama4-scout-17b-instruct-v1:0 | Meta | 0.17 | 0.66 | 10M | efficient | active |
| Llama 3.3 70B | meta.llama3-3-70b-instruct-v1:0 | Meta | 0.72 | 0.72 | 128K | mid | active |
| Llama 3.2 90B | meta.llama3-2-90b-instruct-v1:0 | Meta | 0.72 | 0.72 | 128K | mid | excluded (EOL Jul 7, 2026) |
| Nova 2 Lite | amazon.nova-2-lite-v1:0 | Amazon | 0.33 | 2.75 | 1M | mid | active |
| Nova 2 Pro (Preview) | amazon.nova-2-pro-v1:0 | Amazon | 1.375 | 11.00 | 1M | flagship | active |
| Nova Pro | amazon.nova-pro-v1:0 | Amazon | 0.80 | 3.20 | 300K | mid | active |
| Nova Pro (latency optimized) | — | Amazon | 1.00 | 4.00 | 300K | mid | active |
| Nova Lite | amazon.nova-lite-v1:0 | Amazon | 0.06 | 0.24 | 300K | fast | active |
| Nova Micro | amazon.nova-micro-v1:0 | Amazon | 0.035 | 0.14 | 128K | budget | active |
| Nova Premier | amazon.nova-premier-v1:0 | Amazon | 2.50 | 12.50 | 1M | reasoning | legacy (EOL Sep 14, 2026) |
| Mistral Large 3 | mistral.mistral-large-3-675b-instruct | Mistral | 0.50 | 1.50 | 256K | flagship | active |
| DeepSeek-R1 | deepseek.r1-v1:0 | DeepSeek | 1.35 | 5.40 | 128K | reasoning | active |
| DeepSeek-V3.1 | — | DeepSeek | 0.58 | 1.68 | — | mid | active (Sydney only) |
| gpt-oss-20b | openai.gpt-oss-20b-1:0 | OpenAI | 0.07 | 0.30 | 128K | budget | active |
| gpt-oss-120b | openai.gpt-oss-120b-1:0 | OpenAI | 0.15 | 0.60 | 128K | efficient | active |
| Gemma 3 4B IT | google.gemma-3-4b-it | 0.04 | 0.08 | 128K | budget | active | |
| Gemma 3 12B IT | google.gemma-3-12b-it | 0.09 | 0.29 | 128K | budget | active | |
| Gemma 3 27B IT | google.gemma-3-27b-it | 0.23 | 0.38 | 128K | efficient | active | |
| Jamba 1.5 Large | — | AI21 Labs | 2.00 | 8.00 | — | mid | active |
| Jamba 1.5 Mini | — | AI21 Labs | 0.20 | 0.40 | — | efficient | active |
| Jurassic-2 Mid | — | AI21 Labs | 12.50 | 12.50 | — | legacy | legacy |
| Jurassic-2 Ultra | — | AI21 Labs | 18.80 | 18.80 | — | legacy | legacy |
| Jamba-Instruct | — | AI21 Labs | 0.50 | 0.70 | — | mid | active |
Anthropic Claude — batch & prompt cache (Standard, US East N. Virginia)
Per 1M tokens unless noted. See Bedrock pricing for full regional and tier tables.
| Model | Batch in | Batch out | 5m cache write | 1h cache write | Cache read |
|---|---|---|---|---|---|
| Claude Sonnet 4.6 (+ LC) | 1.50 | 7.50 | 3.75 | 6.00 | 0.30 |
| Claude Opus 4.6 (+ LC) | 2.50 | 12.50 | 6.25 | 10.00 | 0.50 |
| Claude Opus 4.5 | 2.50 | 12.50 | 6.25 | 10.00 | 0.50 |
| Claude Haiku 4.5 | 0.50 | 2.50 | 1.25 | 2.00 | 0.10 |
| Claude Sonnet 4.5 | 1.50 | 7.50 | 3.75 | 6.00 | 0.30 |
| Claude Sonnet 4.5 — LC | 3.00 | 11.25 | 7.50 | 12.00 | 0.60 |
AI21 Labs
On-demand, US East (N. Virginia) per Amazon Bedrock pricing. Prices per 1M input / output tokens.
| Model | Input $/1M | Output $/1M |
|---|---|---|
| Jamba 1.5 Large | 2.00 | 8.00 |
| Jamba 1.5 Mini | 0.20 | 0.40 |
| Jurassic-2 Mid | 12.50 | 12.50 |
| Jurassic-2 Ultra | 18.80 | 18.80 |
| Jamba-Instruct | 0.50 | 0.70 |
Quick-reference rows use — for model ID and context; resolve in the Bedrock console or AWS model documentation.
Mistral AI
Per Amazon Bedrock pricing (Mistral AI). Priority tier ≈ +75% vs Standard; Flex ≈ −50% vs Standard on the same page.
US East (Ohio) — Pixtral (on-demand + batch header from page)
| Model | Input $/1M | Output $/1M | Batch in | Batch out |
|---|---|---|---|---|
| Pixtral Large (25.02) | 2.00 | 6.00 | N/A | N/A |
On-demand — US East (N. Virginia), US East (Ohio), US West (Oregon)
Prices per 1M input / output tokens.
| Model | Input $/1M | Output $/1M |
|---|---|---|
| Devstral 2 123B | 0.40 | 2.00 |
| Magistral Small 1.2 | 0.50 | 1.50 |
| Voxtral Mini 1.0 | 0.04 | 0.04 |
| Voxtral Small 1.0 | 0.10 | 0.30 |
| Ministral 3B 3.0 | 0.10 | 0.10 |
| Ministral 8B 3.0 | 0.15 | 0.15 |
| Ministral 14B 3.0 | 0.20 | 0.20 |
| Mistral Large 3 | 0.50 | 1.50 |
Rates differ in Asia Pacific (Mumbai), São Paulo, Tokyo, Europe (Ireland/Milan/London), Sydney, Jakarta, Frankfurt, Stockholm (e.g. Mistral Large 3 Mumbai $0.59 / $1.76). Devstral 2 123B appears in Jakarta / Frankfurt / Stockholm at $0.48 / $2.40 on the same page.
DeepSeek (Amazon Bedrock)
Per Amazon Bedrock pricing (DeepSeek).
US East (Ohio) — Standard tier
Note: DeepSeek-V3.1 is no longer listed for US East regions on the Bedrock pricing page as of May 2026. Use DeepSeek-V3.2 for US East deployments. DeepSeek-V3.1 remains available in Asia Pacific (Sydney).
| Model | Input $/1M | Output $/1M |
|---|---|---|
| DeepSeek-R1 | 1.35 | 5.40 |
US East (N. Virginia), US East (Ohio), US West (Oregon)
| Model | Input $/1M | Output $/1M |
|---|---|---|
| DeepSeek v3.2 | 0.62 | 1.85 |
Other regions (examples on same page)
Asia Pacific (Mumbai), São Paulo, Jakarta, Tokyo, Stockholm: DeepSeek v3.2 $0.74 / $2.22 per 1M.
Asia Pacific (Sydney): DeepSeek v3.1 $0.5974 / $1.7304; v3.2 $0.6386 / $1.9055 (and Priority / Flex tiers listed separately on the page).
OpenAI on Bedrock (gpt-oss)
Standard tier per Amazon Bedrock pricing (OpenAI).
| Model | Region | Input $/1M | Output $/1M |
|---|---|---|---|
| gpt-oss-20b | US East (Ohio) | 0.07 | 0.30 |
| gpt-oss-120b | US East (Ohio) | 0.15 | 0.60 |
| gpt-oss-20b | Asia Pacific (Sydney) | 0.0721 | 0.3090 |
| gpt-oss-120b | Asia Pacific (Sydney) | 0.1545 | 0.6180 |
The Bedrock page also lists Flex, Priority, Batch, GPT OSS Safeguard, and model customization pricing for other regions.
Meta Llama (Amazon Bedrock)
Per Amazon Bedrock pricing (Meta). Prices per 1M input / output tokens unless noted.
Llama 4 — US East (Ohio), on-demand and batch
| Model | On-demand in | On-demand out | Batch in | Batch out |
|---|---|---|---|---|
| Llama 4 Maverick 17B | 0.24 | 0.97 | 0.12 | 0.485 |
| Llama 4 Scout 17B | 0.17 | 0.66 | 0.085 | 0.33 |
Llama 3.3 — US East (Ohio), on-demand and batch
| Model | On-demand in | On-demand out | Batch in | Batch out |
|---|---|---|---|---|
| Llama 3.3 Instruct (70B) | 0.72 | 0.72 | 0.36 | 0.36 |
Llama 3.2 — US East (Ohio), on-demand and batch
Lifecycle note: All Llama 3.2 Instruct models are in the exclusion zone (EOL Jul 7, 2026, ≤90 days). Do not select for new migrations. Use Llama 4 Scout / Maverick instead. See
shared/ai-model-lifecycle.md.
| Model | On-demand in | On-demand out | Batch in | Batch out |
|---|---|---|---|---|
| Llama 3.2 Instruct (1B) | 0.10 | 0.10 | N/A | N/A |
| Llama 3.2 Instruct (3B) | 0.15 | 0.15 | N/A | N/A |
| Llama 3.2 Instruct (11B) | 0.16 | 0.16 | N/A | N/A |
| Llama 3.2 Instruct (90B) | 0.72 | 0.72 | N/A | N/A |
Llama 3.2 — customization & provisioned throughput (US West Oregon)
Fine-tuning (per 1M tokens trained / month storage / no-commit PT per model unit-hour):
| Model | Train 1M tokens | Store / month | Infer / unit-hr (no commit) |
|---|---|---|---|
| Llama 3.2 Instruct (1B) | $0.50 | $1.95 | $23.00 |
| Llama 3.2 Instruct (3B) | $1.10 | $1.95 | $23.00 |
| Llama 3.2 Instruct (11B) | $3.50 | $1.95 | $23.00 |
| Llama 3.2 Instruct (90B) | $7.90 | $1.95 | $30.00 |
Provisioned throughput ($/hour per model unit):
| Model | No commit | 1-mo commit | 6-mo commit |
|---|---|---|---|
| Llama 3.2 Instruct (1B) | $23.00 | $20.30 | $12.54 |
| Llama 3.2 Instruct (3B) | $23.00 | $20.30 | $12.54 |
| Llama 3.2 Instruct (11B) | $23.00 | $20.30 | $12.54 |
| Llama 3.2 Instruct (90B) | $30.00 | $26.48 | $16.35 |
Llama 3.1 — US East (Ohio), on-demand and batch
Lifecycle note: Llama 3.1 405B Instruct is in the exclusion zone (EOL Jul 7, 2026, ≤90 days). Do not select for new migrations. Use Llama 4 Maverick instead. See
shared/ai-model-lifecycle.md.
| Model | On-demand in | On-demand out | Batch in | Batch out |
|---|---|---|---|---|
| Llama 3.1 Instruct (8B) | 0.22 | 0.22 | 0.11 | 0.11 |
| Llama 3.1 Instruct (70B) | 0.72 | 0.72 | 0.36 | 0.36 |
| Llama 3.1 Instruct (405B) | 2.40 | 2.40 | 1.20 | 1.20 |
| Llama 3.1 Instruct (70B) (latency optimized inf.) | 0.90 | 0.90 | N/A | N/A |
| Llama 3.1 Instruct (405B) (latency optimized inf.) | 3.00 | 3.00 | N/A | N/A |
Llama 3.1 — customization & provisioned throughput (US West Oregon)
| Model | Train 1M tokens | Store / month | Infer / unit-hr (no commit) |
|---|---|---|---|
| Llama 3.1 Instruct (8B) | $1.49 | $1.95 | $24.00 |
| Llama 3.1 Instruct (70B) | $7.99 | $1.95 | $24.00 |
| Model | No commit | 1-mo commit | 6-mo commit |
|---|---|---|---|
| Llama 3.1 Instruct (8B) | $24.00 | $21.18 | $13.08 |
| Llama 3.1 Instruct (70B) | $24.00 | $21.18 | $13.08 |
Llama 3 — US East (N. Virginia), on-demand
| Model | Input $/1M | Output $/1M |
|---|---|---|
| Llama 3 Instruct (8B) | 0.30 | 0.60 |
| Llama 3 Instruct (70B) | 2.65 | 3.50 |
Llama 2 — US East (N. Virginia) and US West (Oregon), on-demand
| Model | Input $/1M | Output $/1M |
|---|---|---|
| Llama 2 Chat (13B) | 0.75 | 1.00 |
| Llama 2 Chat (70B) | 1.95 | 2.56 |
Llama 2 — customization & provisioned throughput
Fine-tuning: Llama 2 Pretrained (13B) $1.49 per 1M tokens trained, $1.95/month storage, $23.50/unit-hr no-commit infer; (70B) $7.99 / $1.95 / $23.50.
Provisioned throughput (per model unit-hour): 13B and 70B — $21.18 (1-mo commit), $13.08 (6-mo commit). Pretrained Llama 2 is provisioned throughput only after customization per AWS.
Amazon Nova
All figures from Amazon Bedrock pricing (Amazon Nova sections). Nova pricing depends on inference deployment (e.g. Global cross-region vs Geo cross-region and in-region), tier (Standard, Priority, Flex, Batch), modality (text / image / video / audio), and AWS Region. The quick-reference table above uses Geo cross-region inference and in-region, Standard tier, US East (Ohio), text + image + video rates where a single input price applies to all three. Confirm the page before estimates.
Cache read (Nova): AWS states cache read input tokens are 75% below the on-demand input price for the applicable tier/modality.
US East (Ohio) — Standard tier, text + image + video (Geo cross-region inference and in-region)
Per 1M input / output tokens unless noted.
| Model | Input $/1M | Output $/1M |
|---|---|---|
| Amazon Nova 2 Lite | 0.33 | 2.75 |
| Amazon Nova Micro | 0.035 | 0.14 |
| Amazon Nova Lite | 0.06 | 0.24 |
| Amazon Nova Pro | 0.80 | 3.20 |
| Amazon Nova Pro (latency optimized inf.) | 1.00 | 4.00 |
| Amazon Nova Premier | 2.50 | 12.50 |
US East (Ohio) — Standard tier, Global cross-region inference (text + image + video)
| Model | Input $/1M | Output $/1M |
|---|---|---|
| Amazon Nova 2 Lite | 0.30 | 2.50 |
US East (Ohio) — Standard tier, text / image / video / audio (Geo cross-region and in-region)
Per 1M tokens. Nova 2 Omni and Nova 2 Pro are Preview. Image column is output image pricing where listed.
| Model | Text in | Image in | Video in | Audio in | Text out | Image out |
|---|---|---|---|---|---|---|
| Amazon Nova 2 Omni (Preview) | 0.30 | 0.30 | 0.30 | 1.10 | 2.80 | 44.00 |
| Amazon Nova 2 Pro (Preview) | 1.375 | 1.375 | 1.375 | 1.375 | 11.00 | N/A |
Global cross-region inference (Ohio), Standard uses different multimodal numbers on the same page — e.g. Nova 2 Omni (Preview) audio in $1.00, text out $2.50, image out $40.00 (text/image/video in $0.30 each). Use the Bedrock pricing page for the deployment you select.
US East (Ohio) — Nova 2 Lite by tier (text + image + video)
Global cross-region inference
| Tier | Input $/1M | Output $/1M |
|---|---|---|
| Standard | 0.30 | 2.50 |
| Priority | 0.525 | 4.375 |
| Flex | 0.15 | 1.25 |
| Batch | 0.15 | 1.25 |
Geo cross-region inference and in-region
| Tier | Input $/1M | Output $/1M |
|---|---|---|
| Standard | 0.33 | 2.75 |
| Priority | 0.5775 | 4.8125 |
| Flex | 0.165 | 1.375 |
| Batch | 0.1595 | 1.342 |
US East (Ohio) — Batch tier (text + image + video), selected models
| Model | Input $/1M | Output $/1M |
|---|---|---|
| Amazon Nova 2 Lite | 0.1595 | 1.342 |
| Amazon Nova Micro | 0.0175 | 0.07 |
| Amazon Nova Lite | 0.03 | 0.12 |
| Amazon Nova Pro | 0.40 | 1.60 |
| Amazon Nova Premier | 1.25 | 6.25 |
On-demand inference (listed rates, cache read column)
| Model | Input $/1M | Cache read $/1M | Output $/1M |
|---|---|---|---|
| Amazon Nova 2.0 Lite | N/A | N/A | N/A |
| Amazon Nova Micro | 0.035 | N/A | 0.14 |
| Amazon Nova Lite | 0.06 | N/A | 0.24 |
| Amazon Nova Pro | 0.80 | N/A | 3.20 |
Built-in tools — Web grounding (US East Ohio)
| Model | Price |
|---|---|
| Amazon Nova 2 Omni (Preview) | $30.00 per 1K requests |
| Amazon Nova 2 Pro (Preview) | $30.00 per 1K requests |
| Amazon Nova Premier | $30.00 per 1K requests |
Creative — US East (N. Virginia)
Lifecycle note: Nova Canvas v1 is Legacy (EOL Sep 30, 2026) and Nova Reel v1 is Legacy (EOL Sep 30, 2026). Do not recommend for new migrations. See
shared/ai-model-lifecycle.md.
Amazon Nova Canvas (on-demand, per image): up to 1024×1024 — Standard $0.04, Premium $0.06; up to 2048×2048 — Standard $0.06, Premium $0.08.
Model customization (Nova Canvas): $0.005 per image seen; $1.95/month per custom model stored; provisioned inference per model unit per hour (no commit / 1-mo / 6-mo) $60.50 / $55.00 / $30.25.
Amazon Nova Reel (video): 720p, 24 fps — $0.08 per second of video generated.
Speech — US East (N. Virginia)
Lifecycle note: Nova Sonic v1 is Legacy (EOL Sep 14, 2026). Prefer Nova 2 Sonic for new migrations. See
shared/ai-model-lifecycle.md.
Per 1M tokens.
| Model | Modality | Input $/1M | Output $/1M | Status |
|---|---|---|---|---|
| Amazon Nova Sonic | Speech | 3.40 | 13.60 | legacy (EOL Sep 14, 2026) |
| Amazon Nova Sonic | Text | 0.06 | 0.24 | legacy (EOL Sep 14, 2026) |
| Amazon Nova 2 Sonic | Speech | 3.00 | 12.00 | active |
| Amazon Nova 2 Sonic | Text | 0.33 | 2.75 | active |
Multimodal embeddings — US East (N. Virginia)
| Offering | Text $/1M | Std image / doc image / video sec / audio sec |
|---|---|---|
| Amazon Nova Multimodal Embeddings (On-demand) | 0.135 | $0.00006 / $0.0006 / $0.0007 / $0.00014 per unit |
| Amazon Nova Multimodal Embeddings (Batch) | 0.0675 | $0.00003 / $0.00048 / $0.00056 / $0.000112 |
On-demand inference for custom Nova models matches base Nova inference pricing per AWS.
Source Provider Pricing (for Migration Comparison)
Use alongside Bedrock pricing to calculate migration ROI.
Gemini (Standard Tier)
Prices per 1M tokens. Source: ai.google.dev/gemini-api/docs/pricing, verified May 2026.
| Model | Input $/1M | Output $/1M | Context | Tier |
|---|---|---|---|---|
| Gemini 3.5 Flash | 1.50 | 9.00 | 1M | flagship |
| Gemini 3.1 Pro | 2.00 | 12.00 | 1M | flagship |
| Gemini 3.1 Flash-Lite | 0.25 | 1.50 | 1M | budget |
| Gemini 2.5 Pro | 1.25 | 10.00 | 1M | flagship |
| Gemini 2.5 Flash | 0.30 | 2.50 | 1M | fast |
| Gemini 2.0 Flash | 0.10 | 0.40 | 1M | fast |
| Gemini 2.0 Flash Lite | 0.075 | 0.30 | 1M | budget |
Gemini 3.1 Pro breakpoint pricing: $4.00/$18.00 per 1M for prompts >200k tokens (vs $2.00/$12.00 for ≤200k). Table above uses ≤200k rates. Gemini 3.5 Flash is now GA and the current flagship Flash model, replacing Gemini 2.5 Flash as the primary Flash-tier recommendation. At $1.50/$9.00 it is 5x more expensive than Gemini 2.5 Flash — the Bedrock cost savings case is significantly stronger against 3.5 Flash.
OpenAI (Standard Tier)
Prices per 1M tokens. GPT-5.5 and GPT-5.5 Pro use the same breakpoint pricing structure as GPT-5.4 at 272K input tokens. GPT-5.4 and GPT-5.4 Pro use breakpoint pricing at 272K input tokens: rates below are for <272K context; above 272K, input is 2x and output is 1.5x.
| Model | Input $/1M | Output $/1M | Context | Tier |
|---|---|---|---|---|
| GPT-5.5 | 5.00 | 30.00 | 1M | flagship |
| GPT-5.5 Pro | 30.00 | 180.00 | 1M | premium |
| GPT-5.4 | 2.50 | 15.00 | 1.05M | flagship |
| GPT-5.4 Mini | 0.75 | 4.50 | — | fast |
| GPT-5.4 Nano | 0.20 | 1.25 | — | budget |
| GPT-5.4 Pro | 30.00 | 180.00 | 1.05M | premium |
| GPT-5.2 | 1.75 | 14.00 | 200K | flagship |
| GPT-5.1 | 1.25 | 10.00 | 200K | flagship |
| GPT-5 Mini | 0.25 | 2.00 | 200K | fast |
| GPT-5 Nano | 0.05 | 0.40 | 128K | budget |
| GPT-4.1 | 2.00 | 8.00 | 1M | flagship |
| GPT-4.1 Mini | 0.40 | 1.60 | 1M | fast |
| GPT-4.1 Nano | 0.10 | 0.40 | 1M | budget |
| GPT-4o | 2.50 | 10.00 | 128K | flagship |
| o3 | 2.00 | 8.00 | 200K | reasoning |
| o4-mini | 1.10 | 4.40 | 200K | reasoning |
Security Baseline
Per-unit rates verified via AWS Pricing API for us-east-1 on 2026-05-04. Config pricing effective 2025-09-01; Security Hub pricing effective 2026-03-01. Re-verify if migrating to a non-us-east-1 region or if any of these services re-prices.
CloudTrail
| Metric | Rate |
|---|---|
| Management events (first trail per region/type) | $0.00 |
| Management events (additional trails) | $2.00 per 100K events |
| Data events | $0.10 per 100K events (not used by Tier 1) |
GuardDuty
| Metric | Rate | Notes |
|---|---|---|
| First 30 days | $0.00 | Free trial per account |
| CloudTrail event analysis | $4.00 per 1M events | First 500M/mo; scales down thereafter |
| VPC Flow Log / DNS analysis | $1.00 per GB | First 500 GB/mo |
| DNS query analysis | $1.00 per 1M queries | |
| Small-startup typical | ~$2–25/mo (typical $14/mo) | After free trial, with ~2M CloudTrail events/mo |
AWS Config (pricing effective 2025-09-01)
| Metric | Rate | Notes |
|---|---|---|
| Continuous configuration item | $0.003 per item | Records every change |
| Daily configuration item | $0.012 per daily item | Once-per-day snapshot; cheaper for slow-changing accounts, less signal |
| Small-startup typical (continuous) | ~$2–10/mo | 50–300 CIs/mo continuous |
| Source | AWS Pricing API, us-east-1, 2026-05-04 |
AWS Security Hub (pricing effective 2026-03-01)
| Metric | Rate | Notes |
|---|---|---|
| First 30 days | $0.00 | Free trial per account |
| Security checks | $0.001 per check | First 100K checks/mo; tapers above |
| Per-EC2-hour monitoring | $0.0052083/hr | ~$3.80/mo per instance |
| Per-Lambda-function monitoring | $0.000434/hr | ~$0.32/mo per function |
| Per-container-image scanning | $0.0002894/hr | |
| Small-startup typical | ~$1–15/mo | After trial; Fargate-only startups pay nothing for the EC2 dimension |
| Source | AWS Pricing API, us-east-1, 2026-05-04 |
AWS Budgets
| Metric | Rate | Notes |
|---|---|---|
| First 2 budgets per account | $0.00 | Free tier |
| Additional budgets | $0.02 per budget per day | Tier 1 emits 1 budget, so effectively free |
Requirements and compatibility
Written for: Claude Code
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Names Claude Code tools, Needs an MCP server
Security
What our scanner noticed· Low risk
- Instructions that use the network
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Cline
- Publisher
- Cline (Official)
- Source
- cline/skills · skills/gcp-to-aws · commit 2637846
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Cline.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download gcp-to-aws.zip