How Much Does AI Agent Evaluation Cost? Build vs. Buy
When everything is changing, it makes sense to want maximum flexibility over the tools that your business is running on. Now with coding agents, the trade-offs often come only after running in production. One thing is clear: the evaluation of your AI system is something that is core to your workloads, whereas the infrastructure to support this effort can often be acquired.
Which variables matter most?
Every piece of software has points of flexibility and constraints which ensure good design. For example, in our use case with Nous Research, we fixed the workload objective (deep research), the sandbox environment and the agent, but adjusted the runbook and evals as a way of improving model performance. Conversely, when trying to pick the right model for the job in a document, it might make sense to fix everything but the agent and the model choice.
Agent evaluation infrastructure should make it easy to change the following parts of an AI workload:
- Workload definition (the runbook / workload definition)
- Agent evaluations (e.g. code checks, checklists and judges)
- Runtime configuration (model choice, agent harness, MCPs and skills)
- Integrations (third-party services, RAG retrieval systems)
- Sandbox environment (including software libraries)
At the same time, there are a TON of choices to make and it can be daunting just to get started. Jetty provides you with a great set of sensible defaults so you can focus on the part that nobody else can decide for you; the workload definition and the definition of quality.
A framework for making the call
Here are some questions to get you started when thinking about the buy vs build question for agent evaluation infrastructure.
| Question | Leans build | Leans buy |
|---|---|---|
| How many runs per week, realistically? | A handful, run manually is fine | Dozens to thousands, manual monitoring doesn’t scale |
| Who else needs to trust the results? | Just you, on your machine | A team that needs a shared, reproducible record |
| How often does the model or agent change? | Rarely, one fixed setup | Often, and portability matters |
| What’s the evaluation need? | A pass/fail check or two | Multiple judges, rubrics, and code checks per run |
| Where’s the real bottleneck? | Infrastructure you don’t have yet | Engineering time better spent on the research question |
With coding agents today, building each component is doable for a small team, however most of the complexity in any software system lies at the intersection of each component working together in concert, at scale. Our experience onboarding existing teams is that getting this right can shift a team from their main quest for three to six months. Open source solutions are great, but they still require you to learn and maintain the stack yourself.
The most important question is what’s the cost of building a system that includes sandboxing, cloud storage, AI gateways, third-party integrations, observability and the ability to track and change your evaluation criteria.
The cost comparison
Jetty's free tier (Scoop) covers twelve runs a month at no cost, enough to test the comparison for real before committing to anything. Squadron Platform, the paid tier, is a flat monthly fee for unlimited runs across a team of up to ten, with judges, rubrics, and your own AI gateway included. The engineering cost of building yourself can be significant: maintaining parallel execution, retry logic, config storage, an evaluation framework, and ensuring that new models and agents keep working over time. For a team running real volume, that math tends to resolve quickly in one direction.
Try it against your own workload
The fastest way to answer this for your own team is to skip the guessing and run the comparison the way Morgane did with Hermes Agent: point a real agent skill at both approaches and see which one gets you to a trustworthy answer faster. Sign up for Jetty and bring your own agent.