Cost analysis
AI agent build cost.
Demo is cheap. Production isn't.
AI agents are easy to demo and hard to put into production. Here's what the production-ready version actually requires (and costs), versus the prompt-and-pray prototype that breaks the moment a real user touches it.
01
What does the build cost, one-time?
A production AI agent build is typically a 3-4 week engagement. €25.000 covers: provider selection (Claude / GPT / open-source), use-case scoping, custom tool definitions, API integrations, vector DB or RAG when needed, an eval suite for behavior regressions, prompt-injection defenses, output validation, and a production deploy with logging and traces.
Skip any of those and you have a prototype, not a production agent. Skipping evals in particular is the most common reason agents quietly degrade after launch.
02
What does it cost to run each month?
- · Provider tokens: depends on traffic and which model. A small internal agent can run under $50/month. A customer-facing agent at scale runs into the thousands.
- · Vector DB (if used): $20-200/month depending on storage and query volume.
- · Compute (Lambda or container): $10-100/month for small to medium scale.
- · Observability and eval reruns: $20-100/month for the LLM-traceable observability stack.
03
Where do most agents waste money?
Three places: oversized models for simple steps (use a small model for routing, a large model only for the hard step), no caching of repeated tool calls, and no rate-limit guardrails (one runaway loop can burn a four-figure bill in an hour). All three are configuration, not architecture, and all three should be in place before launch.
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