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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/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
MetricRate
Per vCPU-hour$0.04048
Per GB memory-hour$0.004445

Linux/x86, on-demand.

Lambda
MetricRate
Per request$0.0000002
Per GB-second (first 6B)$0.0000166667
Per GB-second (over 6B)$0.000015
Free tier1M requests + 400K GB-sec/month
EKS
MetricRate
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.micro0.01047.58
t3.small0.020815.17
t3.medium0.041630.34
t3.large0.083260.68
m5.large0.09670.08
m5.xlarge0.192140.16
c5.large0.08562.05
c5.xlarge0.17124.10

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.medium0.073
db.t4g.large0.146
db.r6g.large0.26
db.r6i.large0.29
db.r7g.xlarge0.553
db.r8g.large0.276
db.r8g.xlarge0.552
db.r8g.2xlarge1.104
db.r8g.4xlarge2.208
db.r8g.8xlarge4.416
Storage/IORate
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.medium0.073
db.t4g.large0.146

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.

MetricRate
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.micro0.032
db.t4g.small0.065
db.t4g.medium0.129
db.t4g.large0.258
db.t4g.xlarge0.517
db.t4g.2xlarge1.034
StorageRate
Per GB-month$0.23
RDS MySQL (On-Demand, Single-AZ)

For Multi-AZ, approximately double these rates.

Instance$/hour$/month
db.t3.small0.03424.82
db.t3.medium0.06849.64
db.t3.large0.13699.28
db.t4g.micro0.01611.68
db.t4g.small0.03223.36
db.t4g.medium0.06547.45
db.m5.large0.171124.83
StorageRate
Per GB-month$0.23
Backup per GB-month$0.023
DynamoDB (On-Demand)
MetricRate
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.micro0.01712.41
cache.t3.small0.03424.82
cache.t3.medium0.06849.64
cache.t4g.micro0.016—
cache.t4g.small0.032—
cache.t4g.medium0.065—
cache.r6g.large0.206150.38

Storage

S3
TierRate 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
RequestsRate
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
MetricRate
Per ALB-hour$0.0225
Per LCU-hour$0.008
Monthly fixed$16.43
Network Load Balancer
MetricRate
Per NLB-hour$0.0225
Per LCU-hour$0.006
Monthly fixed$16.43
NAT Gateway
MetricRate
Per hour$0.045
Per GB processed$0.045
Monthly fixed$32.85
VPC

VPC itself is free. Add-ons:

ComponentRate
VPN connection per hour$0.05
VPN monthly$36.50
Interface endpoint per hour$0.01
Interface endpoint monthly$7.30
Route 53
MetricRate
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)
MetricRate
Per GB transfer (first 10 TB)$0.085
Per 10K HTTPS requests$0.01
Free tier1 TB transfer/month

Supporting Services

Secrets Manager
MetricRate
Per secret per month$0.40
Per 10K API calls$0.05
CloudWatch
MetricRateNotes
Log ingestion per GB (Standard)$0.50
Log ingestion per GB (Infrequent Access)$0.2550% cheaper than Standard; no Live Tail, subscription filters, or metric filters
Log storage per GB-month$0.03Same for both Standard and Infrequent Access
Insights query per GB scanned$0.005Same for both log classes
Custom metric per month (≤10K)$0.30Flat 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
MetricRateNotes
Traces recorded per million$5.00First 100K free/month
Traces retrieved per million$0.50First 1M free/month
Traces scanned per million$0.50First 1M free/month
CloudWatch Container Insights (ECS/Fargate)
MetricRateNotes
Per-task performance log/GB$0.50Same as standard log ingestion
Cluster/service/task metrics$0.30Per custom metric — can accumulate quickly
SQS
MetricRate
Standard per million requests$0.40
FIFO per million requests$0.50
Free tier1M requests/month
SNS
MetricRate
Per million publishes$0.50
SQS delivery per million$0.00
HTTP delivery per million$0.60
Free tier1M publishes/month
EventBridge
MetricRate
Per million events$1.00

Analytics

Redshift Serverless
MetricRate
Per RPU-hour$0.375
Storage per GB-month$0.024

Minimum 8 RPU base capacity.

Athena
MetricRate
Per TB scanned$5.00

Columnar formats (Parquet, ORC) and partitioning reduce scan volume.

SageMaker
Training Instance$/hour
ml.m5.large0.115
ml.m5.xlarge0.23
ml.g4dn.xlarge (GPU)0.736
Inference Instance$/hour$/month
ml.t3.medium0.0536.50
ml.m5.large0.11583.95

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.

ModelModel IDProviderInput $/1MOutput $/1MContextTierStatus
Claude Sonnet 4.6anthropic.claude-sonnet-4-6Anthropic3.0015.00200Kflagshipactive
Claude Sonnet 4.6 — Long Contextanthropic.claude-sonnet-4-6Anthropic3.0015.00200K+flagshipactive
Claude Opus 4.6anthropic.claude-opus-4-6-v1Anthropic5.0025.00200Kpremiumactive
Claude Opus 4.6 — Long Contextanthropic.claude-opus-4-6-v1Anthropic5.0025.00200K+premiumactive
Claude Opus 4.5—Anthropic5.0025.00200Kpremiumactive
Claude Sonnet 4.5—Anthropic3.0015.00200Kflagshipactive
Claude Sonnet 4.5 — Long Context—Anthropic6.0022.50200K+flagshipactive
Claude Haiku 4.5anthropic.claude-haiku-4-5-20251001-v1:0Anthropic1.005.00200Kfastactive
Llama 4 Maverickmeta.llama4-maverick-17b-instruct-v1:0Meta0.240.971Mmidactive
Llama 4 Scoutmeta.llama4-scout-17b-instruct-v1:0Meta0.170.6610Mefficientactive
Llama 3.3 70Bmeta.llama3-3-70b-instruct-v1:0Meta0.720.72128Kmidactive
Llama 3.2 90Bmeta.llama3-2-90b-instruct-v1:0Meta0.720.72128Kmidexcluded (EOL Jul 7, 2026)
Nova 2 Liteamazon.nova-2-lite-v1:0Amazon0.332.751Mmidactive
Nova 2 Pro (Preview)amazon.nova-2-pro-v1:0Amazon1.37511.001Mflagshipactive
Nova Proamazon.nova-pro-v1:0Amazon0.803.20300Kmidactive
Nova Pro (latency optimized)—Amazon1.004.00300Kmidactive
Nova Liteamazon.nova-lite-v1:0Amazon0.060.24300Kfastactive
Nova Microamazon.nova-micro-v1:0Amazon0.0350.14128Kbudgetactive
Nova Premieramazon.nova-premier-v1:0Amazon2.5012.501Mreasoninglegacy (EOL Sep 14, 2026)
Mistral Large 3mistral.mistral-large-3-675b-instructMistral0.501.50256Kflagshipactive
DeepSeek-R1deepseek.r1-v1:0DeepSeek1.355.40128Kreasoningactive
DeepSeek-V3.1—DeepSeek0.581.68—midactive (Sydney only)
gpt-oss-20bopenai.gpt-oss-20b-1:0OpenAI0.070.30128Kbudgetactive
gpt-oss-120bopenai.gpt-oss-120b-1:0OpenAI0.150.60128Kefficientactive
Gemma 3 4B ITgoogle.gemma-3-4b-itGoogle0.040.08128Kbudgetactive
Gemma 3 12B ITgoogle.gemma-3-12b-itGoogle0.090.29128Kbudgetactive
Gemma 3 27B ITgoogle.gemma-3-27b-itGoogle0.230.38128Kefficientactive
Jamba 1.5 Large—AI21 Labs2.008.00—midactive
Jamba 1.5 Mini—AI21 Labs0.200.40—efficientactive
Jurassic-2 Mid—AI21 Labs12.5012.50—legacylegacy
Jurassic-2 Ultra—AI21 Labs18.8018.80—legacylegacy
Jamba-Instruct—AI21 Labs0.500.70—midactive
Anthropic Claude — batch & prompt cache (Standard, US East N. Virginia)

Per 1M tokens unless noted. See Bedrock pricing for full regional and tier tables.

ModelBatch inBatch out5m cache write1h cache writeCache read
Claude Sonnet 4.6 (+ LC)1.507.503.756.000.30
Claude Opus 4.6 (+ LC)2.5012.506.2510.000.50
Claude Opus 4.52.5012.506.2510.000.50
Claude Haiku 4.50.502.501.252.000.10
Claude Sonnet 4.51.507.503.756.000.30
Claude Sonnet 4.5 — LC3.0011.257.5012.000.60
AI21 Labs

On-demand, US East (N. Virginia) per Amazon Bedrock pricing. Prices per 1M input / output tokens.

ModelInput $/1MOutput $/1M
Jamba 1.5 Large2.008.00
Jamba 1.5 Mini0.200.40
Jurassic-2 Mid12.5012.50
Jurassic-2 Ultra18.8018.80
Jamba-Instruct0.500.70

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)
ModelInput $/1MOutput $/1MBatch inBatch out
Pixtral Large (25.02)2.006.00N/AN/A
On-demand — US East (N. Virginia), US East (Ohio), US West (Oregon)

Prices per 1M input / output tokens.

ModelInput $/1MOutput $/1M
Devstral 2 123B0.402.00
Magistral Small 1.20.501.50
Voxtral Mini 1.00.040.04
Voxtral Small 1.00.100.30
Ministral 3B 3.00.100.10
Ministral 8B 3.00.150.15
Ministral 14B 3.00.200.20
Mistral Large 30.501.50

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).

ModelInput $/1MOutput $/1M
DeepSeek-R11.355.40
US East (N. Virginia), US East (Ohio), US West (Oregon)
ModelInput $/1MOutput $/1M
DeepSeek v3.20.621.85
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).

ModelRegionInput $/1MOutput $/1M
gpt-oss-20bUS East (Ohio)0.070.30
gpt-oss-120bUS East (Ohio)0.150.60
gpt-oss-20bAsia Pacific (Sydney)0.07210.3090
gpt-oss-120bAsia Pacific (Sydney)0.15450.6180

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
ModelOn-demand inOn-demand outBatch inBatch out
Llama 4 Maverick 17B0.240.970.120.485
Llama 4 Scout 17B0.170.660.0850.33
Llama 3.3 — US East (Ohio), on-demand and batch
ModelOn-demand inOn-demand outBatch inBatch out
Llama 3.3 Instruct (70B)0.720.720.360.36
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.

ModelOn-demand inOn-demand outBatch inBatch out
Llama 3.2 Instruct (1B)0.100.10N/AN/A
Llama 3.2 Instruct (3B)0.150.15N/AN/A
Llama 3.2 Instruct (11B)0.160.16N/AN/A
Llama 3.2 Instruct (90B)0.720.72N/AN/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):

ModelTrain 1M tokensStore / monthInfer / 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):

ModelNo commit1-mo commit6-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.

ModelOn-demand inOn-demand outBatch inBatch out
Llama 3.1 Instruct (8B)0.220.220.110.11
Llama 3.1 Instruct (70B)0.720.720.360.36
Llama 3.1 Instruct (405B)2.402.401.201.20
Llama 3.1 Instruct (70B) (latency optimized inf.)0.900.90N/AN/A
Llama 3.1 Instruct (405B) (latency optimized inf.)3.003.00N/AN/A
Llama 3.1 — customization & provisioned throughput (US West Oregon)
ModelTrain 1M tokensStore / monthInfer / 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
ModelNo commit1-mo commit6-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
ModelInput $/1MOutput $/1M
Llama 3 Instruct (8B)0.300.60
Llama 3 Instruct (70B)2.653.50
Llama 2 — US East (N. Virginia) and US West (Oregon), on-demand
ModelInput $/1MOutput $/1M
Llama 2 Chat (13B)0.751.00
Llama 2 Chat (70B)1.952.56
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.

ModelInput $/1MOutput $/1M
Amazon Nova 2 Lite0.332.75
Amazon Nova Micro0.0350.14
Amazon Nova Lite0.060.24
Amazon Nova Pro0.803.20
Amazon Nova Pro (latency optimized inf.)1.004.00
Amazon Nova Premier2.5012.50
US East (Ohio) — Standard tier, Global cross-region inference (text + image + video)
ModelInput $/1MOutput $/1M
Amazon Nova 2 Lite0.302.50
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.

ModelText inImage inVideo inAudio inText outImage out
Amazon Nova 2 Omni (Preview)0.300.300.301.102.8044.00
Amazon Nova 2 Pro (Preview)1.3751.3751.3751.37511.00N/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
TierInput $/1MOutput $/1M
Standard0.302.50
Priority0.5254.375
Flex0.151.25
Batch0.151.25
Geo cross-region inference and in-region
TierInput $/1MOutput $/1M
Standard0.332.75
Priority0.57754.8125
Flex0.1651.375
Batch0.15951.342
US East (Ohio) — Batch tier (text + image + video), selected models
ModelInput $/1MOutput $/1M
Amazon Nova 2 Lite0.15951.342
Amazon Nova Micro0.01750.07
Amazon Nova Lite0.030.12
Amazon Nova Pro0.401.60
Amazon Nova Premier1.256.25
On-demand inference (listed rates, cache read column)
ModelInput $/1MCache read $/1MOutput $/1M
Amazon Nova 2.0 LiteN/AN/AN/A
Amazon Nova Micro0.035N/A0.14
Amazon Nova Lite0.06N/A0.24
Amazon Nova Pro0.80N/A3.20
Built-in tools — Web grounding (US East Ohio)
ModelPrice
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.

ModelModalityInput $/1MOutput $/1MStatus
Amazon Nova SonicSpeech3.4013.60legacy (EOL Sep 14, 2026)
Amazon Nova SonicText0.060.24legacy (EOL Sep 14, 2026)
Amazon Nova 2 SonicSpeech3.0012.00active
Amazon Nova 2 SonicText0.332.75active
Multimodal embeddings — US East (N. Virginia)
OfferingText $/1MStd 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.

ModelInput $/1MOutput $/1MContextTier
Gemini 3.5 Flash1.509.001Mflagship
Gemini 3.1 Pro2.0012.001Mflagship
Gemini 3.1 Flash-Lite0.251.501Mbudget
Gemini 2.5 Pro1.2510.001Mflagship
Gemini 2.5 Flash0.302.501Mfast
Gemini 2.0 Flash0.100.401Mfast
Gemini 2.0 Flash Lite0.0750.301Mbudget

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.

ModelInput $/1MOutput $/1MContextTier
GPT-5.55.0030.001Mflagship
GPT-5.5 Pro30.00180.001Mpremium
GPT-5.42.5015.001.05Mflagship
GPT-5.4 Mini0.754.50—fast
GPT-5.4 Nano0.201.25—budget
GPT-5.4 Pro30.00180.001.05Mpremium
GPT-5.21.7514.00200Kflagship
GPT-5.11.2510.00200Kflagship
GPT-5 Mini0.252.00200Kfast
GPT-5 Nano0.050.40128Kbudget
GPT-4.12.008.001Mflagship
GPT-4.1 Mini0.401.601Mfast
GPT-4.1 Nano0.100.401Mbudget
GPT-4o2.5010.00128Kflagship
o32.008.00200Kreasoning
o4-mini1.104.40200Kreasoning

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
MetricRate
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
MetricRateNotes
First 30 days$0.00Free trial per account
CloudTrail event analysis$4.00 per 1M eventsFirst 500M/mo; scales down thereafter
VPC Flow Log / DNS analysis$1.00 per GBFirst 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)
MetricRateNotes
Continuous configuration item$0.003 per itemRecords every change
Daily configuration item$0.012 per daily itemOnce-per-day snapshot; cheaper for slow-changing accounts, less signal
Small-startup typical (continuous)~$2–10/mo50–300 CIs/mo continuous
SourceAWS Pricing API, us-east-1, 2026-05-04
AWS Security Hub (pricing effective 2026-03-01)
MetricRateNotes
First 30 days$0.00Free trial per account
Security checks$0.001 per checkFirst 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/moAfter trial; Fargate-only startups pay nothing for the EC2 dimension
SourceAWS Pricing API, us-east-1, 2026-05-04
AWS Budgets
MetricRateNotes
First 2 budgets per account$0.00Free tier
Additional budgets$0.02 per budget per dayTier 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 or Cline.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download gcp-to-aws.zip