How much does it cost to build an AI agent?

AI agent development cost is a project cost measured in engineering weeks, not a licence fee, and a first scoped piece typically runs 4 to 6 weeks of embedded engineering. Multiply those weeks by whatever day rate you are quoted and you have your build number. What moves the figure is almost never the model: it is how many systems the agent has to read, whether that data is already structured, whether the agent takes actions or only answers, and whether per-user permissions have to be enforced at retrieval time.

What does it cost to keep an AI agent running each month?

A monthly run bill has about seven lines: model tokens for generating answers, embeddings, vector storage, reranking, hosting, monitoring, and human review time. For an internal question-answering agent handling roughly 13,000 questions a month, the metered API and database lines come to under 600 dollars at August 2026 provider prices. Human review of flagged answers is usually the largest single line, and hosting and monitoring depend entirely on the cloud contracts you already have.

Why do AI agent quotes vary by ten times for the same brief?

Because the same sentence describes three different projects. A quote for an agent over your documents can assume clean text in one system or scanned PDFs across six, can include an evaluation set or none at all, can stop at handover or cover operating the thing in production, and can price a demo or a system that survives contact with real users. Ask every bidder the same four questions: which data sources are in scope, how will we prove it is right, who is on the hook after launch, and what is explicitly excluded. Most of the ten-times spread collapses once those answers are on paper.

What Does An AI Agent Actually Cost To Build And Run?

What an AI agent costs to build and run

An AI agent has two prices that behave differently. The build is a project cost measured in engineering weeks, four to six for a typical first scoped piece, and it is driven far more by the state of your data than by which model you pick. Running it is a recurring monthly bill with about seven line items, and for an internal question-answering agent the model tokens are usually not the biggest one.

Anyone who quotes you a build price before looking at your data sources is guessing. That is not a sales tactic, it is arithmetic: the same brief costs four weeks against a clean database and sixteen against thirteen years of scanned PDFs. What follows is the structure of both numbers, plus a worked monthly model with the provider prices we read on 4 August 2026, so you can substitute your own volumes and get a figure you can defend.

One thing worth saying early, because it shows up in the budget later. The part that makes an agent safe to put in front of staff is stopping it from inventing answers, and that work is a real line in the estimate. It is also the line most cheap quotes leave out.

What actually drives the build price

Eight things move the number. None of them is the model.

Driver	Cheap end	Expensive end
Number of data sources	One system, one export	Six systems, three legacy, one with no API
State of the data	Clean text or a real database	Scanned PDFs, merged cells, tables split across pages
Question types	One shape of question, one field	Twenty shapes, several needing arithmetic across sources
Actions	Answers only	Writes, sends, books, pays
Where it plugs in	One chat window	CRM, intranet, and a Slack bot
Security and tenancy	One team, one permission level	Per-user permissions enforced at retrieval time
Evaluation depth	A dozen spot checks	A graded set with "abstain" and wrong-value checks
Who operates it	Your team, after handover	The builder, on the hook, with an on-call path

Two rows do most of the damage. The state of the data is the one buyers consistently underestimate: getting numbers out of a scanned rent roll is not a model problem, it is weeks of extraction and verification work that no amount of prompt writing shortens. Per-user permissions are the other. An agent that answers from one shared pile of documents is a fraction of the work of one that must return different answers to different people and never leak across the boundary, because the permission check has to happen at retrieval, before the model ever sees the text.

Use the table as a scoring sheet on your next vendor call. Read the eight rows out loud and ask which end of each one the quote assumes. A bidder who cannot answer has not scoped the job.

The two costs people confuse

Build and run are separate budgets with separate owners, and mixing them is how projects get approved and then quietly starved. The build is one-off and finite. The run cost starts the day the first user asks a question and never stops, and it grows with adoption. Approving a build without a run budget produces a working system that gets switched off in month five.

What running it costs each month

Seven lines. Model tokens for generating answers. Embeddings, both the one-off pass over your corpus and the ongoing refresh as documents change. Vector storage. Reranking, which reorders candidate passages before the model sees them. Hosting. Monitoring. And human review time for the answers the system flags.

Here is a transparent model you can copy. The assumptions are deliberately stated so you can swap in your own.

An internal question-answering agent for 120 staff, each asking 5 questions on each of 22 working days, so 13,200 questions a month. A corpus of 250,000 documents averaging 1,200 tokens each, which is 300 million tokens, with 5 percent of it changing every month. Each answer sends 10,000 input tokens (system prompt plus twelve retrieved passages) and returns 700 output tokens. Each question reranks 50 candidate passages of 600 tokens.

Line	Arithmetic	Monthly
Generation, input	13,200 x 10,000 = 132 MTok at $3.00	$396.00
Generation, output	13,200 x 700 = 9.24 MTok at $15.00	$138.60
Reranking	13,200 x 30,000 = 396 MTok at $0.02	$7.92
Embedding refresh	15 MTok at $0.02	$0.30
Query embeddings	0.26 MTok at $0.02	$0.01
Vector database	Pinecone Standard minimum	$50.00
Metered total		$592.83

Embedding the entire 300 million token corpus once, to stand the thing up, costs 6 dollars. That number surprises people more than any other on this page.

The prices behind those numbers

Every unit price above was read on a live provider page on 4 August 2026.

Claude Sonnet 5: 3 dollars per million input tokens and 15 dollars per million output tokens from 1 September 2026, with introductory pricing of 2 and 10 dollars in effect until 31 August 2026. Claude Haiku 4.5 is 1 and 5 dollars. Source: Anthropic's pricing documentation 1. The model above uses the standard rate, not the introductory one, because the introductory rate expires in under a month.
OpenAI text-embedding-3-small: 0.02 dollars per million input tokens. text-embedding-3-large is 0.13. Source: OpenAI's API pricing page 2.
Voyage rerank-2.5-lite: 0.02 dollars per million tokens, with rerank-2.5 at 0.05. Source: Voyage AI pricing 3.
Pinecone Standard: 50 dollars per month minimum usage, 0.33 dollars per GB per month for storage, and 16 to 18 dollars per million read units depending on cloud and region. Source: Pinecone pricing 4.

Where the money actually goes

Now add the two lines with no published unit price. Hosting and monitoring come out of the cloud and observability contracts you already hold, and we are not going to invent a figure for a stack we have not seen. And human review: if 5 percent of those 13,200 answers get flagged for a person to check, that is 660 reviews, and at four minutes each it is 44 hours a month. Multiply by your own loaded hourly cost. At any professional rate, that single line is larger than every metered line above it combined.

That is the point most cost articles miss. Inference is rarely the biggest number. The biggest numbers are the people who check the output and the engineers who keep the retrieval honest as the corpus drifts. If a quote shows you a token estimate and nothing else, it has priced the cheapest part of the system.

Where the hidden money is

Four things move a run bill in ways nobody budgets for.

Tokenizers change. Anthropic's pricing documentation notes that Claude 4.7 and later models use a newer tokenizer producing roughly 30 percent more tokens for the same text. A per-token price that looks flat can still raise your bill when you upgrade models.

Pinning inference to a region costs more. On Amazon Bedrock and Google Cloud, regional and multi-region endpoints carry a 10 percent premium over global endpoints, per the same page. If your compliance team requires regional routing, that is a real line, not a checkbox.

Server-side tools are billed separately. Web search on the Claude API is 10 dollars per 1,000 searches on top of tokens. Code execution is free for the first 1,550 hours a month per organization, then 0.05 dollars per hour per container. An agent that searches on every question has a second meter running.

Caching cuts the bill only while the prefix holds. A cache read costs a tenth of the base input price, a five-minute cache write costs 1.25 times it. Change one byte near the front of your prompt and every request after it pays full price again. Teams see the saving in week one and lose it in week six when someone adds a timestamp to the system prompt.

What money cannot buy

Grounding and citations do not make mistakes impossible. They make them visible and traceable, instead of silent. No budget converts an AI agent into a system that is right by construction, which is why the evaluation and human review lines are permanent, not a launch expense you retire in month three. Any quote that shows those costs falling to zero after go-live is describing a system nobody is checking.

There is also a real question of whether to build at all. For a common workflow with an off-the-shelf tool that already does it, the honest answer is often to buy, and we cover that trade in build versus buy for AI agents.

How Ilayer prices this

The build is fixed scope, with the number agreed before we start rather than metered by the hour, so the estimation risk sits with us instead of you. After launch there is a flat monthly rate to operate and improve it, and no open-ended retainer. A first fixed-scope piece runs 4 to 6 weeks from kickoff to something working on your real data, and the code lives in your repositories under your license.

We do not publish a price list, for the reason at the top of this page: the same brief is four weeks or sixteen depending on what your PDFs look like. What we do publish is how a fixed-scope engagement runs, step by step, so you can see exactly what you are agreeing to before the number exists. If a workflow needs a guarantee AI cannot give, we say so before you spend a dollar.

What an AI agent costs to build, and what it costs to keep running