Why production LLM evaluation needs a stack
Production LLM evaluation is not a single-tool problem. Failures can appear before release as prompt regressions, during security testing as jailbreaks or prompt injection, and after launch as bad real-user outputs, cost spikes, or latency regressions. A reliable stack needs pre-deployment tests, adversarial testing, and production observability with feedback loops.
Layer 1 — CI regression tests with Promptfoo
Promptfoo turns prompt and model behavior into reviewable test suites. Teams can store configs in source control, compare providers, run assertions, and block risky changes in CI. Use a fast suite for pull requests, then run larger evals and red-team scans on a schedule.
Layer 2 — metric-heavy unit tests with DeepEval and OpenAI Evals
DeepEval fits Python teams that want Pytest-like LLM unit tests close to application code. OpenAI Evals is useful for teams standardized on OpenAI workflows that want a framework and registry for evaluating models or LLM-based systems. Together they complement Promptfoo with metric-oriented and provider-specific evaluation paths.
Layer 3 — production traces with Langfuse or Helicone
Langfuse is a strong choice when trace analytics, prompt management, datasets, human labeling, feedback, and custom evaluation pipelines matter. Helicone is a strong choice when gateway-style request logging, sessions, cost and latency tracking, prompt management, routing, and fallbacks matter. Both help move production failures into better eval datasets.
Recommended reference architecture
Start with small Promptfoo and DeepEval suites during local development. Pull requests run fast regression checks and enforce thresholds. Nightly jobs run larger evals and red-team scans. Production traffic flows through Langfuse or Helicone so failures can be inspected, tagged, and promoted into regression cases.
Budget planning
The open-source parts of this stack can start at zero software cost, but evaluation always has hidden costs: model calls, judge calls, red-team probes, storage, engineering time, and review time. Cloud observability typically moves the budget into low hundreds per month, while growth and enterprise teams may pay more depending on volume, retention, SSO, compliance, and support.
Final recommendation
The best default production LLM evaluation stack is Promptfoo for CI and red-team gates, DeepEval for Python metric tests, OpenAI Evals for OpenAI-centered workflows, and Langfuse or Helicone for traces and feedback. The important thing is building a loop that turns every failure into a future test.