Spent months researching LLM eval & observability platforms for a 250-person rollout — sharing what we found

We've been looking for an AI evaluation tool to onboard at our company (250 people), so we've done a fair bit of research. Seen a lot of these lists on Reddit and noticed they sometimes say pretty contradictory things, or don't quite match what we found when we actually dug into each tool — so figured I'd share what we've got in case it's useful to anyone else going through the same thing. Not claiming this is exhaustive or definitely right, just what we've pieced together so far. From what we can tell there are roughly 4 categories of tools right now: full-stack observability + eval platforms, tools that are more observability-focused, open-source eval frameworks, and traditional ML platforms that have added AI support. Just our take, but category 1 seemed to fit us best since they felt built for this from the ground up rather than bolted on later — though that might just reflect our particular use case. 1. Full-Stack LLM Evaluation & Observability Platforms Braintrust: Seems like a good fit for product teams focused on experimentation, playground workflows, and iterating on prompts or agent components. Supports a lot of observability workflows too, but from what we saw, experimentation can require rebuilding parts of your agent inside the platform, and it looked lighter on governance features for multi-team enterprise use. Confident AI: Looked like a solid fit for enterprise teams trying to standardize evals and observability across product teams. Their eval metrics are research-backed, and it supports red-teaming and governance, which mattered for us. Might be harder to justify if you're a smaller team without an org-wide eval standard yet, and observability pricing seemed a bit less predictable. This is the one we've been leaning toward, though we're still finalizing. LangSmith: Probably the right call if you're already deep in the LangChain/LangGraph ecosystem and want to standardize pipelines within that stack. Purpose-built for evals and observability there, and widely used because of that ecosystem's size. Worth being aware of ecosystem lock-in if you're not fully committed to LangChain. Maxim AI: Interesting if you want AI-powered agent simulations, multimodal traces, and an optional LLM gateway (Bifrost). It's newer than the others here though, so it felt a bit less mature for standardizing eval/observability across a whole org — take that with a grain of salt since it's evolving fast. 2. Observability-Focused Tools Arize: Seemed solid for observability-first AI/LLM workflows, with an open-source OTEL package. Started out in traditional ML monitoring and expanded into AI — felt flexible and enterprise-ready, though we'd guess more tailored workflows still need custom instrumentation. Datadog: Good if you want traditional observability with really granular instrumentation, but honestly can feel noisy and like a lot to navigate. Didn't feel as purpose-built for AI workflows/evals as some of the more AI-native tools. HoneyHive: Seemed to work well for teams mainly focused on production monitoring. A bit weaker on evaluation from what we saw, but has a genuinely nice UI. Laminar: Newer and less popular, but leans more agent-centric than Langfuse. Uses AI to analyze traces (a bit like Arize) and focuses on agentic trace/process visibility. Langfuse: Popular open-source pick if you want to get LLM observability running quickly. Felt heavier on observability than evaluation, and maybe less mature than Arize on deeper observability workflows, but good if you value fast setup and open-source flexibility. 3. Open-Source Evaluation Frameworks DeepEval: Open-source framework for unit testing LLM apps, built by Confident AI. Looked strong for RAG metrics, agent metrics, conversational metrics, custom LLM-as-judge metrics, and CI/CD via its Pytest integration. OpenAI Evals: Open-source framework for evaluating LLMs and agent behavior — supports multi-turn conversations, tool calls, custom graders. Seemed like a reasonable baseline for model-level and task-level eval, and maybe a better fit than Promptfoo if you're dealing with more complex agent trajectories. Promptfoo: Lightweight, code-first, good for prompt/model regression testing and has a strong red-teaming module. Handy for comparing prompts/providers/outputs in CI. Worth noting it's now owned by OpenAI, though still open source (MIT). RAGAS: Focused specifically on RAG evaluation — faithfulness, answer relevancy, context precision/recall, retrieval quality. Probably has the deepest RAG-specific metrics of the bunch, though it reads more like a metrics library than a full eval system on its own. No longer maintained. 4. Traditional ML Platforms that Switched MLflow: Widely used open-source MLOps platform — experiment tracking, model registry, deployment, model eval. Has added LLM tracing and an AI gateway, so probably makes sense if your team's already standardized on it. Weights & Biases Weave: LLM observability/eval layered on top of W&B's existing tracking platform. Good option if your team's already in the W&B ecosystem, though the LLM-specific metric coverage seemed younger than some of the dedicated eval platforms. Comet (Opik): Opik is Comet's open-source LLM eval/observability tool — tracing, datasets, experiments, LLM-as-judge, debugging. Probably a good fit if you're already using Comet for ML experiment tracking. Galileo: Enterprise-focused, built around low-latency guardrail models (Luna) for real-time production monitoring, especially in regulated environments. Worth flagging that Cisco recently acquired them and they're being folded into Splunk's observability suite, so it's a bit of an open question how the roadmap shakes out from here. What's "best" honestly seems to depend a lot on where you're starting from — LangChain-native vs. framework-agnostic, OSS vs. wanting real enterprise support, prompt-iteration vs. production monitoring. Just sharing in case it saves someone else some time — genuinely curious what's worked for others, since I'm sure we're missing things.