For builders, AI-native companies, enterprises, SIs and MSPs

Lower AI cost. Tighter control. One price per governed workload.

Pick your path below. No token markup and no share of your savings.

See every stepWhat each AI step costs: model, retrieval, tools, review.
Stay in policyBudgets, data rules and evidence where AI traffic flows.
Repeatable numbersDeterministic engines, same answer every run.
Your stackLLM gateways, vendor APIs, AWS, Azure, Google Cloud.

What AICost does to a real workload

Three example workloads, each priced step by step by AICost engines:

  • Customer support + RAG: $75,172/mo $51,665/mo (31% lower overall) AI spend: $28,073 → $12,486 (56% lower) See details

    Customer support + RAG

    44,000 resolved case a month · 9 steps

    Voice and chat front door, RAG over product docs, an agent that reads the CRM and issues refunds, human review on escalation.

    Voice / chat front door $29,280/mo Voice Agent
    RAG retrieval $1,409/mo Knowledge Search (RAG)
    Vector database $50/mo Vector Store
    Model inference and agent turns $26,304 $10,717/mo ↓ 7% prompt caching↓ 43% loop cap↓ 9% model routing Agent Loop
    CRM and ticketing tool calls $1,917/mo Tool Integrations
    Retries $264/mo Retry Waste
    Guardrail screening $96/mo ✓ PII and prompt-attack screening Safety Screening
    Observability $12/mo ✓ Tracing and audit log Tracing Cost
    Human review $15,840 $7,920/mo ↓ 50% better grounding Human Review
    Without AICost
    $75,172/mo
    $1.71 per resolved case
    • 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
    $51,665/mo
    $1.17 per resolved case 31% lower cost
    Monthly budget$13,800
    Daily cap$598
    Per-run ceiling$1.27
    Turn cap per run12
    • PII and prompt-attack screening
    • Tracing and audit log
    • Human review on escalation
    • Cost firewall at the gateway
    • Data-class model allowlist
    Where the savings come from
    🤖 Model inference and agent turns$26,304 → $10,717/mo Prompt caching using the AICost Prompt Caching engine −$1,837 (7%)
    Repeated instructions and documents are cached, so you pay less for the same prompt text. *; Loop cap using the AICost Agent Loop engine −$11,410 (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 −$2,341 (9%)
    Routine steps run on a cheaper model; critical steps stay on the primary model. *
    🧑‍💼 Human review$15,840 → $7,920/mo Better grounding using the AICost Human Review engine −$7,920 (50%)
    Answers come only from your approved sources, so fewer cases go to a person. *
    What it means against the AICost price: Monthly savings on this one workload cover the Continuous Enterprise fee 5.2 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 →

  • Engineering + coding agents: $77,730/mo $71,483/mo (8% lower overall) AI spend: $10,256 → $4,009 (61% lower) See details

    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 Continuous Enterprise fee 1.4 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 →

  • Generative multimedia: $14,293/mo $6,374/mo (55% lower overall) See details

    Generative multimedia

    2,000 approved asset a month · 7 steps

    A brief becomes copy, images, voice-over and video in five languages, screened for brand and IP, reviewed by a person above the risk threshold.

    Brief to copy, 5 drafts per asset $210 $90/mo ↓ 57% model routing AI Cost
    Image generation, 2.5 revisions per kept image $211/mo Image Cost
    Voice-over $1,227/mo Audio Cost
    Video clips, 400 x 10s $400/mo Video Cost
    Localization, 5 languages $240/mo AI Cost
    Brand and IP screening $5/mo ✓ Brand and IP screening Safety Screening
    Brand and legal review, every asset $12,000 $4,200/mo ↓ 65% policy-based review sampling Human Review
    Without AICost
    $14,293/mo
    $7.15 per approved asset
    • 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
    $6,374/mo
    $3.19 per approved asset 55% lower cost
    Monthly budget$400
    Daily cap$17
    Per-run ceiling$2.00
    Turn cap per run6
    • Brand and IP screening
    • Human brand and legal review
    • Cost firewall at the gateway
    • Approved generative models only
    Where the savings come from
    🤖 Brief to copy, 5 drafts per asset$210 → $90/mo Model routing using the AICost Model Router engine −$120 (57%)
    Routine steps run on a cheaper model; critical steps stay on the primary model. *
    🧑‍💼 Brand and legal review, every asset$12,000 → $4,200/mo Policy-based review sampling using the AICost Human Review engine −$7,800 (65%)
    People review a sample set by policy, not every item, where the rules allow it. *
    What it means against the AICost price: Monthly savings on this one workload cover the Continuous Enterprise fee 1.8 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 →

AI spend is the model, retrieval and agent steps, the part the cost levers act on. The rest is people time, integrations and compliance, shown in the details.

Select an offer below to cut cost and get better governance today. See all 54 workflows →

Pick your path

Each page shows your plan, price and outcome.

Builders and Small Teams

Know your AI costs before they scale.
  • Use the engines and MCP
  • Analyze your workflows
  • Get optimization ideas
From $0 / $299/mo
View plans

AI-Native Companies

Keep unit economics healthy as you grow.
  • Deeper workflow analysis
  • Budgets and policies
  • Team collaboration
From $299 / $995/mo
View plans

Enterprises

Full visibility and control across all AI workloads.
  • Enterprise-wide inventory
  • Cost, governance and risk
  • Continuous assurance
From $19,500 / $4,500/mo
View plans

System Integrators and Consultants

Deliver AI that is economical, governed and production-ready.
  • Use in client projects
  • Co-branded delivery
  • Partner discount
30% partner discount
View plans

MSPs

Give your SMB clients basic AI cost and governance.
  • Multi-client management
  • Simple policies and reports
  • Easy to resell
From $1,500/mo
View plans
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.

Not sure which path fits?

Answer three quick questions and we point you to the right page.

What we mean by a workload

A workload is one AI application or business process that delivers a defined outcome, measured end to end: its models, retrieval, agents, tools, data, cloud and the people who review it.

StepOne part of the work: a retrieval, a model call, a tool call, a human review.
WorkloadThe whole process: for example a customer support assistant.
Workload portfolioEvery AI workload across the company.
Customer support assistantCoding agentInvoice processingContract reviewVoice sales agentMarketing content production

A new workload starts when the business outcome or the governance boundary changes. Switching the same support agent from one model to another, or from one cloud region to another, is still one workload. A support agent and a separate claims agent are two.

Every workload has a purpose, an owner, an outcome, an architecture, its economics and a governance envelope.

Questions

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.

Are the workflow numbers real?

They are calculated by the same AICost engines used in the live demo and the MCP server, from current public model prices, for an example volume. Your own numbers come from your workload.

Can we start small?

Yes. The calculators and MCP key are free. One Workload Review is a fixed-price way to go deep on one workload before any subscription.

Talk to AICost

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

Book a free consultSee the live demoCurrent pricing page
📖 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 →