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🚀 AI-Native Companies

Keep unit economics healthy as you grow

For companies whose product runs on AI. Cost per outcome for each workload, budgets and caps in the gateway, and evidence your customers will ask for.

Is this you?

  • AI is in your product, not only in your back office
  • Several production workloads and a monthly AI bill that keeps growing
  • Customers or investors ask about margins and controls
Priced on governed scope, not on consumption. You pay for the workloads AICost governs. No token markup, no share of your AI bill and no share of savings, so our price never grows because your usage does.

Plans

AICost Team

$299/mo
Small AI and platform teams
  • 3 active workloads
  • MCP and API
  • All decision engines
  • Workflow economics and TCO
  • Optimization recommendations
  • Basic governance envelopes and policy output
  • History and evidence
  • 5 users
Start Team

AICost Scale

$995/mo
AI-native companies and smaller enterprises
  • 10 active workloads
  • Team collaboration
  • MCP, API and CI
  • Advanced TCO and optimization
  • Cost policies for your gateway
  • Evidence history
  • Cloud and billing connections
  • Workload portfolio view
  • Email support
Start Scale

One Workload Review

$9,500 fixed
One important production workload, end to end
  • Complete workflow economics
  • Cost per business outcome
  • Step-level optimization
  • Forecast at today, 10 times and production scale
  • Governance envelope
  • Agent, tool and budget boundaries
  • Production-readiness findings
  • Recommended controls
  • Retest after your changes

$5,000 credited toward an enterprise plan if you upgrade within 60 days.

Book a Workload Review
An illustrative workload, priced by AICost engines

What AICost changes in one AI workflow

Each step below is priced by its own AICost engine at the volume shown. The levers and the governance envelope are what AICost adds. Numbers refresh with model prices.

Engineering + coding agents

4,400 completed task a month · 7 steps

Developers hand tasks to a coding agent that reads the repo over MCP, runs tests, retries, and opens a pull request a human reviews before merge.

Coding agent turns $9,298 $3,051/mo ↓ 13% context compaction↓ 6% prompt caching↓ 43% loop cap↓ 5% model routing Agent Loop
MCP tools: repository, CI, tickets $1,472/mo Tool Integrations
Test and eval runs $765/mo ✓ Test and eval gate Quality Testing
Retries $193/mo Retry Waste
Observability $2/mo ✓ Tracing and audit log Tracing Cost
Human PR review $66,000/mo ✓ Human review before merge Human Review
Deployment gate /mo Cost Firewall
Without AICost
$77,730/mo
$17.67 per completed task
  • Spend shows up on next month's invoice
  • No per-run or daily ceiling on agents
  • Controls written down, not enforced
  • No evidence trail for audits
With AICost
$71,483/mo
$16.25 per completed task 8% lower cost
Monthly budget$4,500
Daily cap$195
Per-run ceiling$4.41
Turn cap per run20
  • Test and eval gate
  • Tracing and audit log
  • Human review before merge
  • Cost firewall at the gateway
  • Repository and tool allowlist
Where the savings come from
🤖 Coding agent turns$9,298 → $3,051/mo Context compaction using the AICost Context Trim engine −$1,232 (13%)
Long histories are trimmed each turn, so every call carries less text. *; Prompt caching using the AICost Prompt Caching engine −$549 (6%)
Repeated instructions and documents are cached, so you pay less for the same prompt text. *; Loop cap using the AICost Agent Loop engine −$3,974 (43%)
A ceiling on turns and tokens per agent run, so a stuck agent cannot run up the bill.; Model routing using the AICost Model Router engine −$491 (5%)
Routine steps run on a cheaper model; critical steps stay on the primary model. *
What it means against the AICost price: Monthly savings on this one workload cover the AICost Scale fee 6.3 times.

Illustrative: one workload at the volume shown, priced from current public model prices. Your numbers come from your own workload. Open this workflow in the live demo →

Questions

How is Scale different from Team?

More workloads, team collaboration, cloud and billing connections, cost policies for your gateway and a history of evidence.

When should we buy a Workload Review?

When one workload matters enough that you want AICost to model it end to end and retest after your changes.

What counts as a workload?

One AI application or business process with its own outcome and governance boundary. Steps inside it, such as models, agents and tools, are not counted separately.

Do you charge a percentage of AI spend?

No. AICost is priced on the workloads it governs. There is no token markup and no share of savings.

Other paths

Builders and Small Teams

Know your AI costs before they scale.
View plans

Enterprises

Full visibility and control across all AI workloads.
View plans

System Integrators and Consultants

Deliver AI that is economical, governed and production-ready.
View plans

MSPs

Give your SMB clients basic AI cost and governance.
View plans

Talk to AICost

Bring one workload. We price it step by step and show what changes with AICost.

Book a free consultSee the live demo
📖 Data sources & methodology 172 text models · 9 embeddings · 44 vision · 62 audio · 8 vector DBs across 10 vendor pages · last verified 2026-10-18

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-10-18.
  • 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
2026-10-18
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 704 days captured
Anthropic Docs
2026-10-18
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 704 days captured
OpenAI
2026-10-18
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 705 days captured
Google AI
2026-10-18
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 680 days captured
Google Vertex
2026-10-18
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 680 days captured
DeepSeek
2026-10-18
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 619 days captured
xAI
2026-10-18
https://x.ai/api
Daily snapshot since Nov 2024 · 537 days captured
Mistral
2026-10-18
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 678 days captured
Cohere
2026-10-18
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 704 days captured
Voyage AI
2026-10-18
https://docs.voyageai.com/docs/pricing

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.
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 →