How much does it cost to build an AI agent in 2026?

"How much does an AI agent cost?" is the first question most companies ask us, and the honest answer is a range. Here are the ranges we actually quote, what's inside them, and what pushes a project up or down.

The short answer

What you're building	Typical build cost	Typical timeline
A single-workflow agent (e.g. support triage, lead research)	$5k to $15k	3 to 6 weeks
A multi-system agent with several integrations and an eval suite	$15k to $50k	6 to 12 weeks
An AI product or platform (multi-tenant, billing, admin)	$50k and up	3 months and up

Those numbers are for a first version in production, with real users on it.

What you're actually paying for

The model itself is a small part of the bill. A typical agent project splits roughly like this:

• Discovery and scoping (10%). Sitting with the team, collecting real examples, agreeing what "working" means in numbers.
• Integrations (30 to 40%). Getting reliable, permission-aware access to your CRM, inbox, database or documents. This is where most of the effort goes, and where most cheap quotes fall apart.
• The agent and its tools (20 to 30%). Prompts, tool definitions, routing, memory and the human review step.
• Evals and guardrails (15%). A test set of real cases, checks for hallucination and unsafe actions, and logging.
• Deployment and handover (10%). Monitoring, alerts, docs and training your team.

Five things that move the number

1. How many systems it touches. One inbox and one CRM is cheap. Six internal systems with no APIs is not.
2. How wrong it's allowed to be. Drafting internal notes tolerates mistakes. Refunding customers doesn't, and needs approval flows and stricter evals.
3. Data privacy rules. If data can't leave your cloud, we may need self-hosted or region-locked models, which adds setup and running cost.
4. Volume. Ten tasks a day and ten thousand a day are different engineering problems.
5. Who runs it afterwards. A clean handover to your engineers costs a little more up front. A retainer with us spreads that cost over time.

Running costs

Once live, an agent costs money every time it works. For most business workflows we see a few cents to a few tens of cents per task in model fees, plus hosting. We estimate this per thousand tasks during discovery, and design to a budget using smaller models, caching and routing where quality allows.

Here's what that looks like for three agents of the kind we build, at typical volumes:

Agent	Tasks a month	Model cost per task	Model fees a month
Support triage: read, tag, route, draft a reply	3,000	about 5 cents	about $150
Lead research: enrich a lead, score it, write the first email	1,000	about 20 cents	about $200
Document intake: read a PDF, extract fields, update the system	5,000	about 3 cents	about $150

Hosting, logging and monitoring for agents like these usually add tens of dollars a month, more if a model has to run in your own cloud. If someone keeps the agent healthy for you, with model upgrades, eval runs and small fixes, a retainer with us starts from $1.5k a month. The numbers that move monthly cost the most are the model you choose, how much text each task reads, and how often the agent has to retry.

Build or buy?

Off-the-shelf agents are a good deal for generic jobs: meeting notes, basic website chat, inbox sorting. You pay a subscription and you're live this afternoon.

A custom build earns its cost when the agent has to:

• work inside your systems, like an internal database, a legacy CRM or a tool with no public API
• follow your rules, such as refund limits, approval chains or regional compliance
• be measured against your real cases, with logs you can show an auditor
• stay yours, with the code in your repository and the option to switch models later

A common middle path: start with a platform for the generic part and build only the piece that's specific to your business. We'll tell you in the first call if a subscription would do the job.

Red flags in an AI agent quote

• No discovery step. If someone prices your agent before seeing your data, the integration work isn't in the number.
• No eval set. Without a test set of real cases, nobody can tell you whether version two is better than version one.
• "Fully autonomous from day one." Agents earn autonomy. A good plan starts with a person approving risky actions.
• No running-cost estimate. You should know the cost per thousand tasks before you sign.
• Code you won't own. Ask where the repository lives and whose cloud account it runs in.

How to keep the cost down

• Start with one workflow, not a platform.
• Collect twenty real examples before anyone writes a prompt.
• Keep a human in the loop at first. It's cheaper than building for perfection on day one.
• Choose a team that will stay through production. Most of the cost of a failed AI project is the months spent on a demo that never shipped.

For a real example, Altrun, our own multi-channel agent, keeps every capability as a separate tool (classify, summarise, research, draft, schedule), so each one is logged, tested and priced on its own. You can see how we scope and price this work on our AI agent development page, and every engagement model is on pricing.

If you want a number for your own workflow, tell us about it. We'll give you a written estimate after a short discovery, before any build starts.