LLM Eval Tooling Stack: What to Build vs Buy in 2026

Most teams fail at evals because the tooling is ad hoc. This guide lays out a practical eval stack, which pieces to build, which to buy, and how to keep it lean.

TL;DR

• •Start with logging and a clean dataset before any fancy dashboard.
• •Use rule-based validators before LLM judges.
• •Keep prompts, model versions, and eval results tied together.
• •Build the minimum stack first, then expand.

The eval stack in one picture

You can think of eval tooling as six layers:

1. Instrumentation and tracing
2. Dataset management
3. Evaluation runner
4. Judges and validators
5. Reporting and gating
6. Feedback loop and iteration

Minimum viable eval stack

For a small team, you can start with:

• •Structured request and response logs
• •A curated dataset of 100 to 200 examples
• •A simple script that runs evals against models
• •A spreadsheet or lightweight dashboard for results
• •A clear binary pass or fail rubric

Production eval stack

As you scale, you will want:

• •Versioned datasets with labels and tags
• •A repeatable eval runner with metadata
• •A pool of rule-based validators
• •LLM judges for subjective quality
• •A gate that blocks releases below thresholds
• •Alerts when quality drops on key scenarios

Build vs buy guidance

Build when:

• •You have unique workflows or strict security constraints.
• •You need a simple system and want full control.
• •You have the engineering capacity to maintain it.

Buy when:

• •You want fast setup and existing integrations.
• •You need collaboration and UI for labeling.
• •You want to compare models across teams.

What to log on every request

Logging is the foundation. Capture:

• •Input prompt and context
• •Output text and tool calls
• •Model version and parameters
• •Latency and token usage
• •User intent or route name
• •Trace or request IDs

Designing your dataset

Good evals come from realistic data.

Checklist:

1. Pull examples from production traces.
2. Include edge cases and failure cases.
3. Tag examples by scenario and intent.
4. Keep a stable golden set for regressions.
5. Add new examples after every incident.

Judge types and when to use them

Use the cheapest and most reliable judge first.

• •Rule-based checks: schema validation, regex, or strict parsers.
• •Deterministic checks: exact match or constraints.
• •LLM judges: subjective quality, tone, reasoning.

The binary judge pattern

Binary judgments keep you honest. For every eval, decide:

• •Pass or fail
• •Why it failed
• •Which error category it belongs to

Reporting that actually helps

Report the metrics that change behavior:

Common pitfalls

• •Tracking only aggregate scores and ignoring categories.
• •Mixing datasets across unrelated features.
• •Changing prompts without recording versions.
• •Letting judges drift without recalibration.

A realistic rollout plan

1. Add logging and trace IDs.
2. Create a 100 example dataset.
3. Write binary validators for output format.
4. Add an LLM judge for subjective quality.
5. Run weekly evals and track deltas.
6. Add gating rules before new releases.

Final recommendation

Evals are a workflow, not a tool. Start small and build the minimum stack that lets you run repeatable comparisons. Expand only after you trust your data and your judges.

Last updated: February 2026