Developer docs

AICost MCP

122+ AI-cost decision engines behind one MCP server. Ask in plain language from Claude, ChatGPT, Cursor, or any MCP client — or call REST from CI/CD. Every engine returns the same decision envelope: the number, the assumptions, the confidence, the evidence, and (for policy engines) an executable gateway policy.

● Live  Status page · /v1/status (JSON, public) · /v1/health

Connect in two minutes

MCP clients (Claude, ChatGPT, Cursor, and friends) — add a remote server:

URL:    https://mcp.aicost.ai/mcp
Auth:   Bearer aicost_sk_...   (request a key via the form on this site)

REST (CI/CD, scripts):

curl -X POST https://mcp.aicost.ai/v1/run \
  -H "Authorization: Bearer aicost_sk_..." \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: build-1234" \
  -d '{"engine_id":"aicost.agentic-workflow-cost",
       "inputs":{"preset":"support-agent-production"},
       "context":{"workload_id":"support-bot","pricing_as_of":"2026-07-01"}}'
Contract you can verify: published input schemas are generated from the running validators and drift-blocked in CI — see schemas_verified_at in /v1/health. snake_case and camelCase field names are both accepted.

The five jobs (every engine is tagged)

JobYou askFlagship engines
Protect“Stop the bleeding before it happens”cost-firewall, agentic-envelope, overage-forecaster
Identify“Where is the money actually going”business-bill-diagnose, margin-calculator, pricing-watch
Optimize“Same quality, less money”multi-model-router, prompt-cache-roi, batch-vs-realtime, cheapest-model
Plan“What will this cost before we build it”cost-calculator, agentic-workflow-cost, cross-cloud-placement, tco-roi-builder
Verify“Prove the savings were real”ledger_ingest_actualsledger_reconcileledger_attribution

Don’t know which engine? Call recommend_engine with your question in plain language, or filter list_engines { job: "protect" }.

Guided workflows (MCP prompts)

runaway-triage pre-build-cost-review optimize-portfolio cloud-placement-review prove-the-savings

Each walks your assistant through a proven multi-engine chain — cap a runaway, gate a new feature in CI, stack routing + caching + batching into one gateway policy, compare Bedrock vs Azure vs Vertex all-in, or produce the QBR savings-proof report.

Resources your assistant can read for free

aicost://catalog/enginesFull truthful catalog with schemas, versions, lifecycle
aicost://methodology/{slug}Sources, inferred fields, methodology per engine
aicost://pricing/as-ofPricing SSOT freshness + how to pin
aicost://docs/envelopeThe 16-field decision envelope contract
aicost://docs/errorsMachine-readable error taxonomy
aicost://docs/changelogWhat changed in every server version

Presets, missing fields, and elicitation

Presets: flagship engines ship named enterprise scenarios — e.g. {"preset":"support-agent-production"}, coding-copilot-pilot, h100-cluster, deep-research, viral-consumer-app. One field instead of eight; anything you pass explicitly overrides the preset, and application is disclosed with a [preset_applied] warning.

Missing fields: if required inputs are absent you get status: "needs_more_data" with result.missing_fields[] (name, type, description). Clients that support form elicitation are simply asked mid-call and the engine re-runs with your answers.

Reproducibility for CI

context.pricing_as_of: "YYYY-MM-DD" pins token rates to that day’s snapshot (add "pricing_pin":"require" for strict mode). Idempotency-Key replays the same recorded run on retries. POST /v1/run-batch gates up to 25 workloads in one call with per-item envelopes.

Error codes

CodeHTTPMeaning
auth_invalid401Missing or unrecognized credential. Send Authorization: Bearer aicost_sk_...
auth_expired401Credential expired — request a fresh key.
tenant_forbidden403Your key is not authorized for the requested tenant.
admin_forbidden403Admin credential invalid (X-Admin-Key).
engine_not_found404Unknown engine_id — check list_engines.
engine_retired410Engine retired; the response includes superseded_by.
input_invalid400Inputs failed schema validation (wrong types or unknown fields). Missing-required-only cases return a needs_more_data envelope instead.
rate_limited429Per-tenant rate limit exceeded — honor the Retry-After header.
idempotency_conflict409Same Idempotency-Key reused with a different request body.
pin_unavailable200Requested pricing snapshot not found; live pricing used (warning on the envelope).
internal500Our fault — the audit trail has it; tell us the timestamp.

Why engineers accept the numbers

Every envelope carries assumptions, evidence, and a confidence score; inferred values are flagged, never silent. Model quality behind routing is measured (Artificial Analysis indices via OpenRouter, refreshed weekly, per-value provenance) — latency is honestly tier-seeded until a defensible feed exists. Every claimed saving can later be verified against your invoices via the Ledger reconcile loop.

Server changelog: aicost://docs/changelog · Uptime: status page · Keys: request via the AICost site · Built by CloudIntelligence.ai LLC — vendor-neutral by policy.

📖 Data sources & methodology 163 text models · 9 embeddings · 37 vision · 57 audio · 8 vector DBs across 10 vendor pages · last verified 2026-08-03

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-08-03.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-08-03
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 637 days captured
Anthropic Docs
2026-08-03
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 637 days captured
OpenAI
2026-08-03
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 638 days captured
Google AI
2026-08-03
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 613 days captured
Google Vertex
2026-08-03
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 613 days captured
DeepSeek
2026-08-03
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 552 days captured
xAI
2026-08-03
https://x.ai/api
Daily snapshot since Nov 2024 · 470 days captured
Mistral
2026-08-03
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 611 days captured
Cohere
2026-08-03
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 637 days captured

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →