What Does a Running AI Agent Cost in 2026? From PLN 25,000

An AI agent has two separate costs: implementation from PLN 25,000 net and variable operating bills for models, tools, infrastructure, monitoring, and human review. Typical build projects are worth PLN 25,000–150,000 net. Maintenance after launch is priced individually because monitoring, service levels, and change scope differ between processes.

Five parts of a running agent's cost

1. Models and APIs. Every call to a model or paid tool has a usage price.
2. Orchestration and tools. Workflows, connectors, and third-party services may have separate plans or execution charges.
3. Infrastructure. Hosting, storage, queues, secrets, and integrations carry their own bill.
4. Logs and monitoring. A production agent needs enough telemetry to reconstruct actions and catch failures.
5. People. Review, maintenance, decisions, and incident response remain part of the operating cost.

The model can be the smallest line once the full production process is counted. Keep the five bills separate so a low token estimate does not hide the cost of operating the system.

How to count token cost

The formula is simple:

cost/month = number of queries × (input tokens + output tokens) × price per token

The devil is in "input tokens." If every call resends the full conversation history, a large system prompt, and ten context documents, a single query can run to tens of thousands of input tokens before the model even answers.

Fill the calculation with your process data

For each real case, record the average input tokens, output tokens, model calls, tool calls, cache charges, and review time. Then multiply the cost per case by the measured monthly case volume. Model choice matters, but the comparison belongs in that calculation rather than in a generic monthly band.

Who pays for inference

For us, inference is a separate, usage-based cost, independent of the build and maintenance fee. We usually run it on your own API accounts or in your cloud, so you see the real bill with no markup from us. The exact billing model is set in the proposal. The logic is simple: a cost that scales with volume should be transparent, not hidden in a flat fee.

Three levers that keep the bill in check

1. Model routing per task. Classification or extraction don't need the most expensive model. We reserve the strong model for where reasoning accuracy matters.
2. Context discipline. We don't resend the full history on every call. Fewer input tokens means a smaller bill and less quality drift.
3. Cache and reuse. We cache repeated queries and stable context instead of paying for them again.

We track the effect as Cost Per Query, one of the metrics we report in maintenance. It turns the conversation from "it'll be fine" into "it costs X per query, and here's how we bring it down." What post-launch maintenance actually covers and costs, we break down in a separate guide.

The decision threshold

The most important number isn't technical, it's commercial: the agent's cost per case versus the cost of the human it replaces or relieves. If the agent costs more per case than the person it was meant to relieve, the audit verdict is "don't build." We'd rather say that before code than after the budget is spent on a pilot that never made sense.

FAQ

What does a running AI agent cost per month?

There's no single honest figure, because the bill depends on case volume, context length, the model, the number of steps, and human review. Instead of one number, count the cost per case and multiply it by your real process volume.

What makes up the monthly cost?

Five separate bills: inference (tokens), orchestration and tools, hosting with data and integrations, logs and monitoring, and people for review, maintenance, and decisions. Inference is often the smallest of these once a process runs at scale.

How do you count model cost per query?

Take the average input and output tokens per case, multiply by the provider's per-million-token prices, add cache and tool costs, then count it per case rather than per message. One customer case is often several model calls.

Does prompt caching lower the cost?

Only when the agent has a repeatable prompt prefix: a stable instruction, the same tool description, or a shared document. Caching doesn't reduce the cost of long outputs and doesn't help with constantly changing context.

How this works with us

We estimate operating cost in the audit on your real volume and data. The first working result is a pilot on real data after about 6–8 weeks against one written target. Full agent implementations close within 6–16 weeks depending on integrations and scope, with a typical project value of PLN 25,000–150,000 net. Payment is 50 percent at signing and 50 percent at delivery.

Have a process you want a real cost-per-query on? Book a 30-minute call.