Choosing Your Eval Stack

You have reached the last chapter. Over this course you learned what evals are, how to design metrics, how to use LLMs as judges, how to build a pipeline, how to run regression tests, how to evaluate agents, and how to observe systems in production. Now comes the question every team actually has to answer: which tools do you use to do all of that? There is no single correct stack, but there is a correct way to decide, and that is what this chapter gives you.

First, separate what you build from what you buy

The most expensive mistake teams make is deciding this backwards. They spend a quarter building a trace-ingestion service and a comparison dashboard - undifferentiated plumbing that a dozen tools already do well - and spend almost no time on the thing only they can create: a dataset of real cases and a clear definition of what “good” means for their product.

Draw the line here. Build your evaluation datasets, your golden examples, your judge rubrics, and your domain-specific pass or fail criteria. Those encode knowledge about your users that no vendor has. Buy (or adopt open source for) the infrastructure: trace storage, span visualization, experiment comparison, dataset management, and SDK integrations. Our build versus buy for LLM observability breakdown makes the same argument with the cost math attached. Reimplementing the plumbing almost never pays off, because open-source options have already absorbed years of that work.

The decision axes that actually matter

When you compare tools, most feature lists blur together. Filter on the axes that change the decision:

Open source versus hosted. Do you have data-residency rules, air-gapped environments, or a strong aversion to per-trace pricing? Then self-hostable open source is a hard requirement, not a nicety. If you would rather pay to never think about ops, hosted wins. This axis eliminates more options than any other, so resolve it first.

Eval-first versus observability-first. Some tools are built primarily to run experiments over datasets before you ship. Others are built primarily to trace and monitor what is already live. The best now do both, but tools still have a center of gravity. Match it to your most urgent pain: are you trying to stop shipping regressions, or trying to understand a system that is already burning money in production?

Framework fit. If your whole app is LangChain or LlamaIndex, a tool with deep native integration saves real time. If you are framework-agnostic or roll your own, prioritize OpenTelemetry support so you are not locked in.

Team shape. A two-person startup optimizing for setup speed and a free tier wants something different from an enterprise that needs SSO, audit logs, and role-based access. Neither is “better” - they are answers to different questions.

Unified data loop. Can a production trace become an eval sample in one click? Can an offline experiment pull from production data? If pre-release and production live in two disconnected tools, bad real-world cases never become regression tests, and you keep relearning the same failures.

Mapping the tools to the axes

Here is how the tools this course referenced fall out, grounded in what each is actually built for.

Langfuse is the strong default for teams that want open source and a unified loop - self-hostable, framework-agnostic tracing plus datasets, experiments, and online evaluators in one place. If you are unsure where to start, start here and you will rarely regret it.

Braintrust is hosted and eval-first, built around a polished experiment-and-compare workflow. If your primary job is iterating on prompts and comparing versions with a clean UI, and you do not need self-hosting, it is excellent.

DeepEval is an open-source, code-first framework that feels like Pytest for LLMs. If your team lives in test files and CI and wants evals as assertions in the repo, it fits the mental model perfectly. Our best LLM eval frameworks roundup goes deeper on the code-first options.

Promptfoo is open source and config-driven, strongest for prompt testing and red-teaming from a YAML file. It is the low-friction way to compare prompts and models side by side without writing much code.

For the fuller landscape, best LLM eval tools for production and best LLM observability tools compare these and more against real workloads.

A stack that grows with you

You do not need the final stack on day one. A sane progression:

Stage one, prototyping. Reach for a lightweight framework - DeepEval or Promptfoo - to sanity-check prompts against a handful of cases. No infrastructure, no account, just a test file.

Stage two, shipping. Add a tracing and dataset platform like Langfuse or Braintrust. Now every change runs against a real dataset in CI, and you can see traces when something looks off.

Stage three, production at scale. Turn on online evaluation over sampled live traffic, wire user feedback to traces, set cost and quality alerts, and close the loop by promoting bad production traces into your regression datasets. At this point your evals are self-reinforcing: the system’s own failures make it harder to fail the same way twice.

That loop is the whole point. Metrics from chapter two, judges from chapter three, the pipeline from chapter four, regression tests from chapter five, agent trajectories from chapter seven, and production traces from chapter eight all feed one growing dataset that defines quality for your product.

Where this course leaves you

You started not knowing what an eval was. You can now define quality in measurable terms, choose the right metric for a task, use an LLM as a judge without trusting it blindly, build a pipeline that runs in CI, catch regressions before users do, evaluate multi-step agents by their trajectories, observe live systems through traces, and choose tooling on the axes that matter instead of the ones vendors advertise.

The single habit that separates teams who ship reliable LLM products from teams who ship demos is this: they treat evaluation as a permanent part of the system, not a one-time gate. Every bug becomes a test case. Every production surprise becomes a dataset row. Keep that loop turning and your system gets more trustworthy with every failure it survives.

Key takeaways

• Build your datasets and quality definitions; buy or adopt open source for trace storage, dashboards, and experiment comparison.
• Decide on the axes that matter - open source versus hosted, eval-first versus observability-first, framework fit, team shape, and a unified data loop.
• Match tools to those axes: Langfuse as an open-source default, Braintrust for hosted experiments, DeepEval for code-first tests, Promptfoo for config-driven prompt testing.
• Grow the stack in stages and close the loop by turning production failures into permanent regression tests.

That is the course. Go instrument something, write your first ten eval cases, and let the loop start turning.