The model bill is the part everyone sees. It is rarely the biggest part. A real AI agent carries about a dozen costs - safety, quality checks, human review, compliance, monitoring, integrations, keeping data and models current - and the ones people skip are exactly the ones that quietly sink the project. This guide walks through every cost the cost builder includes, what drives each one, and how they add up into a clear go / no-go answer.
Open the Builder →A demo runs on the model alone. A real production agent runs on everything around it. The research is blunt: a large share of AI agent projects get cancelled before they pay off, and when they stall the cause is almost never the model itself - it is the safety, quality-checking, and integration work that nobody budgeted for. Pricing only the model bill is how a project looks cheap on a slide and falls apart in production.
This tool takes the opposite approach. You enter simple numbers for each cost - how many requests, how often a human steps in, how big your data is - and each cost is worked out by its own dedicated calculator behind the scenes. Nothing is a rough guess. Sections you have not filled in stay on example numbers and are clearly marked, so the total is always honest about how much is really yours.
What it takes to stand the system up: integrations and connectors to your existing tools.
Prices the most underestimated build-phase line: connecting the agent to real business systems such as CRM, ERP, ticketing, and identity. Each connector is authentication, schema mapping, rate-limit and error handling, plus ongoing upkeep, not just plugging in an API. Shows one-time build, annual maintenance, amortized annual cost for TCO, and cost per integration.
What you pay to keep it running: model usage, the search that grounds it, storage and network.
See exactly what an LLM workload will cost across 70+ models. Pick a model, enter your tokens per request and daily volume, get per-request / daily / monthly / annual cost. Caching and Batch API savings calculated automatically.
Compare 9 embedding models on indexing + query-time cost for your RAG corpus.
What it costs to keep your data: model files, training data, the vector database, logs and backups. A simple sum of how much you store (in TB) times the monthly price per TB.
What it costs to move data out: sending results to users, copying between regions, and CDN traffic. Charged per TB of data sent out, which is easy to overlook until the bill arrives.
What it costs to keep it accurate over time: quality checks, fresh data, model upkeep and monitoring.
Prices continuous quality evaluation for an agent: a golden set of representative cases scored on every release by an LLM-as-judge. Shows the monthly eval cost, the cost per release run, and the agent-versus-judge split. This is the regression net that catches the silent works-Monday-fails-Wednesday drop when a provider updates the model.
Prices the production tracing and monitoring layer for an agent (Langfuse, LangSmith, Datadog, Braintrust, Logfire, or self-host). The unit is the span: each LLM call, tool call, retrieval, and rerank. It computes per-span, per-trace, and self-host billing side by side, because the billing model, not the vendor, is what blows up the bill on agentic workloads.
Prices the recurring data treadmill behind a RAG or agentic system: incremental re-embedding of changed documents each refresh, vector upsert and reindex, pipeline orchestration, the periodic full re-embed forced by an embedding-model or chunking change, and lightweight drift monitoring. This is the data-drift bill that pairs with eval's model-drift bill.
Prices the recurring cost of responding to model drift: forced migrations when a provider deprecates your model, prompt re-baselining and regression after each swap, periodic re-tuning to counter quality drift, and steady-state prompt-ops. This is the fix bill that pairs with eval-hallucination-cost, which is the detect bill, on the model side.
What it costs to keep it safe and dependable: guardrails, human review, compliance, vendor risk and downtime.
Prices the runtime safety layer in front of an agent: safety, prompt-injection, and PII screening passes run on a cheap classifier model for every checked call, plus an optional guardrail-platform fee. Shows the monthly guardrail bill, cost per 1,000 screened calls, and the percent it adds on top of your agent token spend.
Prices the human-in-the-loop review layer of an agentic workload: the monthly review labor, the cost per reviewed action, the reviewer headcount it implies, and whether the incidents that review catches pay for the labor. It is the single most-omitted line item in agentic TCO.
Prices the annual compliance and governance overhead of an agentic deployment across the five real cost centers: compliance staff, audit-log storage, conformity assessment and certification, external audit, and a governance platform. EU AI Act risk tier is the master switch that scales the rigor and triggers certification.
A cushion for relying on a single model vendor: the cost of an abstraction layer, a second supplier, or a fallback provider so one price change or shutdown cannot strand the system. Enter your best estimate of that monthly insurance.
The revenue you lose when the system is down. Hours unavailable in a month times the revenue you would normally earn each hour. It is not a bill you pay, but it is a real cost of running the service.
The smaller things that are easy to forget but add up.
The smaller things that never make the headline model bill: preparing and labeling data, the occasional rollback, and miscellaneous one-off costs. Each is small; together they add up.
Adding up the costs gives you a monthly total. That alone does not tell you whether to go ahead. So the tool reduces the expected payoff by the chance the project gets cancelled before it pays off - and roughly two in five AI agent projects do not make it. Every item you can honestly tick on the readiness checklist lowers that chance, because the things that keep a project alive are within your control.
You get one of three answers. GO: the payoff clearly beats the cost, even after allowing for risk - build it. RISKY: the payoff works and it pays back, but the margin is thin or the project is not ready - de-risk and finish the checklist before you commit budget to scale. NO-GO: even before risk, the payoff does not cover the full cost - the case is not there yet, usually a sign the task is too low-value or the hidden costs were never budgeted.
Run your numbers →Each cost is computed by its own validated calculator, so the ones tied to live pricing (model usage, embeddings, guardrails, quality checks, data-freshness) track the pricing catalog automatically, and yearly costs (compliance, integrations, model upkeep) are shown per month. The tool also divides the total by your volume to show cost per request, per 1,000 tokens, per user, and - the most useful number for most teams - cost per business result, such as per ticket resolved or per document processed. The risk-weighting and gate logic are shared with the express control tower, so the deep and express paths always agree on a verdict.
The framing draws on published industry research: Gartner's projection that a large share of agentic projects are abandoned by 2027; Bain's payback benchmarks showing measurable workflows recover their investment in months while diffuse ones take far longer; MIT Sloan's finding that stalled programs frequently had no evaluation in place and lost meaningful accuracy over time; and McKinsey and BCG/Forrester estimates of multi-month ramp periods and multi-year returns. The quality safety-net - quality checks catch problems while data-refresh and model-update budgets fix them - reflects a recurring lesson: catching problems without budgeting the fix is how accuracy quietly decays.
This guide and calculator are a budgeting and decision aid, not investment advice. Figures left on defaults are placeholders, not your data; refine the layers that matter before taking a number to a board.
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.
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 →