When organizations budget for AI, they usually focus on one number: what the model provider charges. That number matters, but it’s rarely the whole story, and it’s often not the biggest part. In our experience building and running AI agents, the real cost comes from a handful of design choices that are easy to get right early and expensive to fix later.
The costs people expect
- Model usage. Most AI models are priced by how much text goes in and comes out. Bigger, more capable models cost more per request.
- Hosting. The service that runs your agent, stores its data, and connects it to your systems.
- Setup. The one-time work of building the agent, preparing its data, and connecting it to your tools.
The costs people miss
- Oversized models for simple jobs. Using the most powerful model for every step, including simple ones, can multiply costs without improving results.
- Sending too much context. Stuffing whole documents into every request is expensive. Retrieving only the relevant passages is usually cheaper and more accurate.
- Infrastructure built for a future that hasn’t arrived. Specialized databases and complex architectures add ongoing cost long before the data volume needs them.
- Verification time. If people have to double-check every answer because the agent doesn’t cite its sources, the hours spent verifying can exceed every other cost combined.
- The cost of wrong answers. An error that becomes a requirement or a client commitment costs far more than any model bill.
How we keep our own agents lean
When BRIO built the Litify knowledge expert our team uses every day, cost was a design requirement from the start, alongside accuracy and governance.
Right-size every step
Each step uses the least expensive model that does the job well. A capable mid-tier model handles preparing content and answering questions, rather than the largest model available. The result is a small cost per question without sacrificing quality.
Retrieve, don’t stuff
Instead of sending entire documents to the model, the agent retrieves only the most relevant passages for each question. That keeps each request small, fast, and focused, and it makes answers easier to cite.
Start lean and keep choices reversible
The service runs on a modest cloud tier, and search works without a separate database layer until the content is large enough to need one. Because that choice is reversible, the system can grow without being rebuilt.
Make answers easy to verify
Every answer cites its source, so a person can confirm it in seconds instead of redoing the research. That’s where the biggest savings live: questions that once took minutes of manual searching now take seconds.
Build once, share everywhere
The knowledge expert is a single tool that any of our AI agents can consult. Instead of each agent maintaining its own copy of the same knowledge, one well-governed source serves all of them.
Questions to ask before you build
- What does one question or task cost at the volume you expect?
- Which steps really need the most capable model, and which don’t?
- How much time will people spend checking the agent’s work?
- What happens to cost if usage grows tenfold?
- Which infrastructure choices can be reversed later if they need to be?
AI doesn’t have to be expensive to be useful. Designed carefully, it can be one of the cheapest skilled helpers your team has. Talk to our AI consulting team about building agents that fit your budget, or read about how we stood up an AI assistant across a company.
Frequently asked questions
What drives the cost of running an AI agent?
The main drivers are model usage, the size of each request, hosting and infrastructure, and the human time spent verifying answers. Design choices such as model size and how much context is sent have the biggest effect.
How can we reduce the cost of an AI agent?
Use the least expensive model that does each step well, retrieve only relevant passages instead of whole documents, start with lean infrastructure, and make answers easy to verify with citations.
Is the most powerful AI model always the best choice?
No. Many steps can be handled well by smaller, less expensive models. Matching each step to the right model keeps costs down without hurting quality.
