AI agent development cost by type
The biggest cost driver is engineering time, so the table shows typical effort first and then the cost at our rates. Model and hosting fees come on top and are covered in the next section.
| Agent type | What it includes | Typical effort | Cost |
|---|---|---|---|
| Pilot / proof of concept | One workflow on your real data, read-only tools, an evaluation set and a go/no-go report | 4–8 engineer-weeks | $5,000–$16,000 |
| RAG knowledge assistant | Document ingestion, search with citations, per-user access control, re-indexing | 6–14 engineer-weeks | $6,000–$28,000 |
| Production agent (single workflow) | 2–3 integrations, write actions with approval, evaluations, monitoring, staged rollout | 10–20 engineer-weeks | $10,000–$40,000 |
| Multi-agent system | An orchestrator plus specialised agents across several systems, with shared state and checkpoints | 20–40 engineer-weeks | $20,000–$80,000 |
| Run and improve (monthly) | Monitoring, re-evaluation on new models, prompt and tool updates | 20–60 hours per month | $500–$3,000 per month |
What drives the price up or down
- Number of integrations. Each system the agent reads from or writes to (CRM, ERP, help desk, email) adds design, testing and permission work.
- Write actions. An agent that only reads and drafts is far cheaper than one that sends, pays or updates records, which needs approval steps and audit logs.
- Data readiness. Clean APIs are quick. Scanned PDFs, spreadsheets and inconsistent records need extraction and cleaning first.
- Accuracy bar. Moving from 'usually right' to 'right enough to run unattended' takes a larger evaluation set and more iteration.
- Security and compliance. Deploying in your cloud account, redacting personal data or meeting a regulated industry's rules adds work.
- Interface. An agent that runs in the background is simpler than one with its own chat interface or admin dashboard.
Running costs after launch
Model usage is usually the largest running cost. Estimate it as: tasks per month × tokens per task × price per token. Prices differ by model and change often, so check your provider's current price list.
Hosting, a vector database and monitoring tools usually add a smaller, fairly fixed monthly amount. Ask any vendor to show the cost per task in their monitoring, not just the total bill.
How to keep AI agent costs under control
- Start with one workflow that has a clear owner and a measurable outcome, and prove it in a pilot.
- Use smaller, cheaper models for simple steps such as classification and routing, and larger models only where they change the result.
- Cache repeated context and batch work that does not need an instant answer.
- Build the evaluation set early, so every change is checked against real cases instead of re-tested by hand.
- Set token and spending limits per run, and review cost per task every month.
