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GCP To AWS

ClineOfficialPublished by Cline

  • Skill
  • Workflow
  • Cloud and DevOps
  • Apache-2.0
  • Low risk

From the author

Migrate workloads from Google Cloud Platform to AWS — including AI and agentic workloads regardless of cloud provider. Triggers on: migrate from GCP, GCP to AWS, move off Google Cloud, migrate Terraform to AWS, migrate Cloud SQL to RDS, migrate GKE to EKS, migrate Cloud Run to Fargate, Google Cloud migration, migrate from OpenAI to Bedrock, move off OpenAI, switch from ChatGPT API to AWS, migrate from Gemini to Bedrock, migrate LangChain to Bedrock, migrate LangGraph to AWS, migrate agentic workloads to AWS, move AI workloads to AWS, migrate my AI app to AWS. Runs a 6-phase process: discover GCP resources from Terraform files, app code, or billing exports, clarify migration requirements, design AWS architecture, estimate costs, generate migration artifacts, and collect optional feedback. Clarify must finish before Design, Estimate, or Generate. Includes AI provider migration guidance (for example, OpenAI to Amazon Bedrock) by selecting closest-fit Bedrock model families for required modality, latency/quality
— the description by Cline

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/cline/gcp-to-aws/download
  2. Unzip 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 (IaC google_bigquery_* and/or billing rows for BigQuery), you must surface the specialist advisory before Design (see references/phases/clarify/clarify.md). Design output uses Deferred — specialist engagement; keep directing the user to their AWS account team and/or a data analytics migration partner through Design, Estimate, and docs (see references/phases/design/design-infra.md BigQuery 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):

FileCondition
design-refs/ai-gemini-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "gemini" or "both"
design-refs/ai-openai-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "openai" or "both"
design-refs/ai-anthropic-to-bedrock.mdai-workload-profile.json exists AND summary.ai_source = "anthropic"
design-refs/ai.mdai-workload-profile.json exists AND summary.ai_source = "other"
design-refs/design-ref-harness.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "harness"
design-refs/design-ref-agentic-to-agentcore.mdagentic_profile.is_agentic == true AND ai_constraints.agentic.migration_approach == "strands"
shared/retarget-gotchas.mdagentic_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: .tf files (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 StateConditionNext Action
discoverphases.discover != "completed"Load references/phases/discover/discover.md
clarifyphases.discover == "completed" AND phases.clarify != "completed"Load references/phases/clarify/clarify.md
designphases.clarify == "completed" AND phases.design != "completed"Load references/phases/design/design.md
estimatephases.design == "completed" AND phases.estimate != "completed"Load references/phases/estimate/estimate.md
generatephases.estimate == "completed" AND phases.generate != "completed"Load references/phases/generate/generate.md
completephases.generate == "completed" AND phases.feedback == "pending"Set phases.feedback to "completed" (user had two chances), then migration complete
completephases.generate == "completed" AND phases.feedback == "completed"Migration planning complete

How to determine current state (deterministic):

  1. Read $MIGRATION_DIR/.phase-status.json

  2. If current_phase exists, use it (must match one of: discover, clarify, design, estimate, generate, complete)

  3. Otherwise use ordered phase evaluation: discover → clarify → design → estimate → generate

  4. Pick the first phase in that order where phases.<phase> != "completed"; if none, state is complete

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.

  1. Single $MIGRATION_DIR: Use one run directory for the entire migration. Do not mix artifacts across .migration/*/ sessions.

  2. Re-read from disk: Before each phase (and before each handoff gate), Read required artifacts from $MIGRATION_DIR/. Do not rely on chat memory.

  3. Advance only on HANDOFF_OK: A phase is complete only when its orchestrator emits HANDOFF_OK | phase=<name> | artifacts=.... Do not load the next phase without it.

  4. 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.

  5. Re-entry: Re-running an earlier phase after downstream phases completed requires explicit user confirmation; downstream phases must be reset to "pending". See handoff-gates.md re-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:

  1. Multiple sessions: If multiple directories exist under .migration/, list them with their phase status and ask: [A] Resume latest, [B] Start fresh, [C] Cancel.

  2. Invalid JSON: If .phase-status.json fails to parse, STOP. Output: "State file corrupted (invalid JSON). Delete the file and restart the current phase."

  3. Unrecognized phase: If phases object contains a phase not in {discover, clarify, design, estimate, generate, feedback}, STOP. Output: "Unrecognized phase: [value]. Valid phases: discover, clarify, design, estimate, generate, feedback."

  4. Unrecognized status: If any phases.* value is not in {pending, in_progress, completed}, STOP. Output: "Unrecognized status: [value]. Valid values: pending, in_progress, completed."

  5. Invalid current_phase (if present): If current_phase is not in {discover, clarify, design, estimate, generate, complete}, STOP. Output: "Unrecognized current_phase: [value]. Valid values: discover, clarify, design, estimate, generate, complete."

  6. 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.json before resuming."

  7. 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:

  1. Read the current file before every update.

  2. Change only the phase keys being advanced and last_updated.

  3. Keep prior completed phases unchanged.

  4. Set current_phase to the next deterministic phase (or complete after generate).

  5. 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

PhaseInputsOutputsReference
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
ClarifyDiscovery artifacts (gcp-resource-inventory.json, gcp-resource-clusters.json, ai-workload-profile.json, billing-profile.json — whichever exist)preferences.json, .phase-status.json updatedreferences/phases/clarify/clarify.md
Designpreferences.json + discovery artifactsaws-design.json (infra), aws-design-ai.json (AI), aws-design-billing.json (billing-only)references/phases/design/design.md
Estimateaws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.jsonestimation-infra.json or estimation-ai.json or estimation-billing.json, .phase-status.json updatedreferences/phases/estimate/estimate.md
Generateestimation-infra.json or estimation-ai.json or estimation-billing.json, aws-design.json or aws-design-billing.json or aws-design-ai.json, preferences.jsongeneration-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 updatedreferences/phases/generate/generate.md
Feedback.phase-status.json (discover completed minimum), all existing migration artifactsfeedback.json, trace.json, .phase-status.json updatedreferences/phases/feedback/feedback.md

MCP Servers

awspricing (for cost estimation):

  • Provides get_pricing, get_pricing_service_codes, get_pricing_service_attributes tools

  • Only 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
ConditionAction
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 gateStop. Output: "Cannot enter Phase X: Phase Y-1 not completed. Start from Phase Y or resume Phase Y-1."
awspricing unavailable after 3 attemptsDisplay 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 clustersStop 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.micro for 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.md for tier definitions.

Workflow Execution

When invoked, the agent MUST follow this exact sequence:

  1. Load phase status: Read .phase-status.json from .migration/*/.

    • If missing: Initialize for Phase 1 (Discover)

    • If exists: Determine current phase using deterministic rules in State Machine

  2. Determine phase to execute:

    • If current_phase exists: 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).

  3. Read phase reference: Load the full reference file for the target phase.

  4. Execute ALL steps in order: Follow every numbered step in the reference file. Do not skip, optimize, or deviate.

  5. 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.

  6. Handoff gate: Emit HANDOFF_OK or GATE_FAIL per shared/handoff-gates.md. On GATE_FAIL, stop — do not update phase status or load the next phase.

  7. 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.

  8. 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.feedback is "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.feedback is "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.feedback to "completed". Continue to Generate.

    • After Generate: No feedback offer. If phases.feedback is still "pending", use the Phase Status Update Protocol to set it to "completed" (user had two chances and chose to defer/skip).

  9. 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

references/phases/estimate/estimate-ai.md

Estimate Phase: AI Workload Cost Analysis

Loaded by estimate.md when aws-design-ai.json exists.

Execute ALL steps in order. Do not skip or optimize.

Pricing Mode

The parent estimate.md selects the pricing mode before loading this file.

Price lookup order:

  1. shared/pricing-cache.md (primary) — Look up Bedrock model pricing and source provider pricing by table. Set pricing_source: "cached".

  2. MCP (secondary) — If a model is NOT in pricing-cache.md and MCP is available, query get_pricing("AmazonBedrock", ...) with model filter and the user's target region. Set pricing_source: "live".

  3. Cache after MCP failure — If MCP was attempted but failed, and the model IS in the cache, use the cached price. Set pricing_source: "cached_fallback".

  4. Unavailable — If a model is NOT in the cache AND MCP failed, set pricing_source: "unavailable" and warn the user.

For typical migrations (Claude, Llama, Nova, Mistral, DeepSeek, Gemma, OpenAI gpt-oss, Gemini source pricing), ALL prices are in pricing-cache.md. Zero MCP calls needed.

Model lifecycle: When building the model comparison table, check references/shared/ai-model-lifecycle.md and apply the 90-day exclusion rule:

  • Excluded (≤90 days to EOL): omit entirely from model_comparison, recommended_model, and backup_model.

  • Legacy (>90 days to EOL): include in model_comparison with (Legacy — EOL YYYY-MM-DD) annotation. Do not select as recommended_model unless no Active alternative exists.

  • Active: no restrictions.

Prerequisites

Read from $MIGRATION_DIR/:

  • ai-workload-profile.json — current_costs.monthly_ai_spend, current_costs.services_detected, models[], metadata.profile_source, summary.inferred_from_iac

  • preferences.json — ai_constraints.ai_token_volume.value, ai_constraints.ai_capabilities_required.value

  • aws-design-ai.json — metadata.ai_source, ai_architecture.honest_assessment, ai_architecture.tiered_strategy, ai_architecture.bedrock_models[] (with source_provider_price, bedrock_price, honest_assessment), ai_architecture.capability_mapping


Part 1: Establish Current GCP AI Costs

Determine current Vertex AI spending from the best available source:

  1. Billing data (preferred) — Use current_costs.monthly_ai_spend from ai-workload-profile.json

  2. Estimated from token volume — Use ai_constraints.ai_token_volume.value from preferences.json with Gemini pricing from pricing-cache.md (under "Source Provider Pricing"). Apply 60/40 input/output ratio if actual ratio unknown.

  3. Neither available — Note in output and present model comparison at multiple volume tiers so user can find their range.

IaC-only profile: If metadata.profile_source is iac_vertex or summary.inferred_from_iac is true and billing/token data is missing, state explicitly that current GCP AI spend is unverified and widen uncertainty bands (use the same multi-tier comparison approach as in case 3).


Part 2: Build Model Comparison Table

Calculate the monthly Bedrock cost for every viable model at the user's token volume.

Token volume mapping (from ai_token_volume in preferences.json):

ai_token_volumeInput tokens/monthOutput tokens/monthRatio
"low"6M4M60/40
"medium"60M40M60/40
"high"600M400M60/40
"very_high"6B4B60/40

If design or discover phase has more specific token estimates, use those instead.

Cost formula: Monthly = (input_tokens / 1M × input_rate) + (output_tokens / 1M × output_rate)

Long-context surcharge: If ai_critical_feature = "ultra_long_context" in preferences.json, Claude models charge 2x the standard input rate for tokens beyond 200K context. Apply the surcharge to the portion of input tokens that exceeds 200K per request. If per-request token counts are unknown, assume 50% of input tokens fall in the long-context tier as a conservative estimate.

Comparison table columns: Model, Bedrock Monthly, vs Source Provider ($ and %), vs Current GCP, Quality, Capabilities Match (checked against ai_capabilities_required).

Include source provider pricing from aws-design-ai.json → bedrock_models[].source_provider_price.

If Bedrock is more expensive for the recommended model, flag prominently.

If embeddings are needed, add a separate line (additive to primary model cost).


Using the model selected in the design phase, show:

  • Input tokens × rate, output tokens × rate, embeddings × rate (if applicable)

  • Total monthly cost

  • Comparison to current GCP spend (monthly and annual difference)

  • Backup model cost for comparison


Part 4: Human One-Time Migration Costs (Out of Scope)

Do not present human labor, contractors, professional services, or engineering effort as one-time migration costs or budget line items (no dollar figures, no "budget for people work" lists, no "one-time migration cost" categories for implementation).

Populate migration_cost_considerations.categories as an empty array []. Use migration_cost_considerations.note to state that human and professional-services one-time migration costs are intentionally excluded from this advisor.

Technical integration complexity (for internal JSON and risk context only — not framed as money):

From ai-workload-profile.json, record non-monetary factors in migration_cost_considerations.complexity_factors[] as short strings, for example:

  • integration.pattern = "framework" → lower integration touch surface

  • integration.pattern = "direct_sdk" → moderate SDK and API pattern changes

  • integration.pattern = "rest_api" → higher endpoint, auth, and parsing changes

  • summary.total_models_detected > 3 → multi-model coordination

  • quota_risk = "high" (from aws-design-ai.json) → Bedrock quota increase required before migration; allow 1–5 business days (see shared/bedrock-quotas.md)

Do not repeat these as "costs" in the user-facing summary.


Part 5: ROI Analysis

Present the monthly and annual cost difference between current GCP AI spend and projected Bedrock cost:

  • If Bedrock is cheaper: present monthly and annual savings clearly

  • If Bedrock is more expensive: state clearly, justify with non-cost benefits or note "not justified if cost is the only priority"

Reference aws-design-ai.json → honest_assessment. If "recommend_stay", present prominently.

Non-cost benefits to present: model flexibility (30+ models), prompt caching (Claude, 90% savings), AWS ecosystem (Guardrails, Knowledge Bases, Agents), vendor diversification, multi-model strategy.

Note: Human/professional-services one-time migration costs are intentionally out of scope for this advisor and excluded from ROI calculations.


Part 6: Cost Optimization Opportunities

Present applicable optimizations with estimated savings:

OptimizationSavingsApplies When
Model downsizing / tiering60-87%High volume, premium model selected
Prompt caching (Claude)~30%Repeated system prompts
Batch API50%Non-real-time workloads (ai_latency = "flexible")
Provisioned throughputVariesToken volume > 100M/month, predictable traffic
Input token reduction10-30%Prompt optimization, shorter context
Multi-model tiered routing60-87%High/very-high volume, tiered_strategy in design

For each applicable optimization, calculate before/after monthly cost and show an optimized_projection (best-case monthly with all optimizations).

Post-migration optimization (do not surface during migration): Model distillation — training a smaller, faster student model from a larger teacher model — can reduce inference costs up to ~75% for high-volume, stable workloads. Requires production traffic, labeled examples, and a teacher/student eval loop. Mention in the estimate summary as: "Once you have 2–4 weeks of Bedrock production traffic, consider model distillation to further reduce costs. See docs.aws.amazon.com/bedrock/latest/userguide/model-distillation.html." Do not recommend distillation before the startup has migrated and validated their workload.


Part 7: Migration Recommendation (REQUIRED)

Produce a clear migrate/stay/optimize verdict for the AI workload migration. This is the AI-only equivalent of estimate-infra.md Part 7.

Decision logic:

ConditionVerdictrecommendation.path
Bedrock cheaper AND capabilities matchMigratemigrate_optimized
Bedrock more expensive BUT non-cost benefits justify (vendor diversification, Guardrails, multi-model) AND user priority ≠ costMigrate with caveatmigrate_optimized
Bedrock more expensive AND user priority = cost AND no compelling non-cost reasonStaystay
Design honest_assessment = recommend_stayStaystay
Mixed (some workloads cheaper, some not)Migrate selectivelymigrate_phased

Output fields (add to estimation-ai.json top-level):

"recommendation": {
  "path": "migrate_optimized | migrate_phased | stay",
  "path_label": "Migrate to Bedrock | Migrate selectively | Stay on current provider",
  "migrate_if": "Brief condition under which migration makes sense (1 sentence)",
  "stay_if": "Brief condition under which staying makes sense (1 sentence)",
  "confidence": "high | medium | low",
  "rationale": "2-3 sentence justification citing cost delta and non-cost factors"
}

Rules:

  • MUST emit recommendation — never omit. If data is insufficient, set confidence: "low" and state why in rationale.

  • If honest_assessment from aws-design-ai.json says recommend_stay, recommendation.path MUST be stay regardless of cost numbers.

  • For multi-workload runs: if some workloads favor migration and others don't, use migrate_phased and list which workloads to migrate vs. keep in rationale.


Output

Write estimation-ai.json to $MIGRATION_DIR/.

Schema — top-level fields:

FieldTypeDescription
phasestring"estimate"
timestampstringISO 8601
pricing_sourcestring"cached" or "live"
accuracy_confidencestring"±5-10%" or "±15-25%"
current_costsobjectsource, gcp_monthly_ai_spend, services[]
token_volumeobjectsource, monthly_input_tokens, monthly_output_tokens, ratio
model_comparisonarrayAll viable models: model, monthly_cost, vs_current, quality, capabilities_match, missing_capabilities[]
recommended_modelobjectmodel, monthly_cost, breakdown (input/output/embeddings), rationale
backup_modelobjectmodel, monthly_cost, rationale
embeddingsobjectmodel, monthly_cost, monthly_tokens, note (if applicable)
cost_comparisonobjectcurrent_gcp_monthly, projected_bedrock_monthly, monthly_difference, annual_difference, percent_change
migration_cost_considerationsobjectcategories[] (always []), complexity_factors[] (technical integration only), note (must state human/pro costs excluded)
roi_analysisobjectmonthly_cost_delta, annual_cost_delta, justification, non_cost_benefits[]
optimization_opportunitiesarrayopportunity, potential_savings_monthly, implementation_effort, description
optimized_projectionobjectmonthly_with_optimizations, vs_current, note
recommendationobjectpath, path_label, migrate_if, stay_if, confidence, rationale (see Part 7)

All cost values are numbers, not strings. Output must be valid JSON.

Validation Checklist

  • [ ] recommendation field is present with non-empty path, path_label, migrate_if, stay_if, and rationale

  • [ ] recommendation.path is one of: migrate_optimized, migrate_phased, stay

  • [ ] If Design honest_assessment = recommend_stay, then recommendation.path = stay

  • [ ] model_comparison includes ALL viable Bedrock models, not just recommended

  • [ ] Legacy models in model_comparison are annotated with EOL dates (per shared/ai-model-lifecycle.md)

  • [ ] recommended_model is an Active model (not Legacy) unless no Active alternative exists

  • [ ] Every model has capabilities_match checked against ai_capabilities_required

  • [ ] recommended_model.rationale references user's priority, preference, and volume

  • [ ] roi_analysis is honest — if migration increases cost, says so

  • [ ] optimization_opportunities only includes strategies relevant to user's workload

  • [ ] No compute, database, storage, or networking costs (those belong in estimate-infra.md)

  • [ ] migration_cost_considerations.categories is [] — no human one-time migration costs presented

Completion Handoff Gate (Fail Closed)

Before returning control to estimate.md, require:

  • estimation-ai.json exists and passes the Validation Checklist above.

If this gate fails: STOP and output: "estimate-ai did not produce a valid estimation-ai.json; do not complete Phase 4."

Present Summary

After writing estimation-ai.json, present under 25 lines:

  1. Pricing source and accuracy: State whether prices came from cache or live API, and the accuracy range (±15-25% for AI models from cache, ±5-10% from live API). Example: "AI model estimates based on cached pricing (2026-03-07), accuracy ±15-25%."

  2. Current GCP AI spend vs projected Bedrock cost (recommended model)

  3. Model comparison table: model name, monthly cost, vs source provider %, capabilities match

  4. Recommended model with cost breakdown

  5. If migration increases cost: flag honestly with non-cost justification

  6. Top 2-3 optimization opportunities with potential savings

  7. Optimized projection

Generate Phase Integration

The Generate phase uses estimation-ai.json:

  1. recommended_model — Which Bedrock model to provision and test

  2. migration_cost_considerations — complexity_factors[] only for integration risk context; never present human one-time migration costs to the user (categories stays [])

  3. optimization_opportunities — Which optimizations to implement and when

  4. cost_comparison — Cost monitoring targets and alerts in production

  5. model_comparison — Fallback options if recommended model doesn't meet quality bar

Scope Boundary

This phase covers financial analysis ONLY for AI workloads.

FORBIDDEN — Do NOT include compute, database, storage, networking cost calculations, infrastructure provisioning, code migration examples, or detailed migration timelines.

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

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