LLM monitoring is no longer optional. Once a feature is in production, you need to know what's hitting your model, what it's saying back, what it's costing, and when quality drifts. The tools below are the five we'd actually pick from across the dozen-plus options on the market — ranked by what they're best at, not by who paid for placement.
Methodology, criteria, and a note on conflicts of interest are on the about page.
Eval-driven dev platform combining traces, datasets, scorers, and a playground in one product.
The best LLM monitoring tool, period — and the one we'd point almost any team toward as the default. Braintrust is the rare product where the whole is greater than the sum: monitoring, evaluation, prompt iteration, and CI gating all live in one product, and the connections between them (your CI scorer is the same as your prod scorer is the same as your playground scorer) save more time than any single feature.
Loop, the AI assistant for generating datasets and scorers from production logs in plain English, is the most interesting recent addition in the entire category — it makes eval-driven development legitimately accessible to PMs and domain experts, not just engineers. The customer list (Notion, Stripe, Vercel, Airtable, Instacart, Zapier) reflects this: these are teams that did the comparison and picked Braintrust on the merits.
The honest case against it is closed source, and pricing once you're past the free tier. For most teams, neither is disqualifying.
Open-source LLM observability with evals, prompt management, and best-in-class tracing.
The pick if you need self-hosting, want to read the source, or are operating somewhere a closed-source vendor won't fly. Tracing is genuinely best-in-class — drilling into a multi-step agent run feels closer to a real APM than what most eval-first competitors offer.
The cloud version at $29/month is reasonable. The eval UX trails Braintrust on polish, but the gap has closed meaningfully over the past year, and the open-source guarantee is worth real money to teams in compliance-sensitive industries.
Proxy-based LLM observability — drop in by changing the base URL, no SDK changes needed.
The fastest path to "I can see what my LLM is doing." Change one base URL, get logs, costs, and basic tracing immediately. No SDK to integrate, no instrumentation. For the "we just need to start measuring" phase of an LLM project, nothing else is faster.
The catch: the proxy sees what's on the wire — request and response. It can't see your reasoning steps, tool calls, or agent structure unless you also instrument your code. Most teams outgrow it for primary observability but keep it around for cost monitoring across providers.
Agent reliability platform with cheap, fast evaluators that can run on every request in production.
The pick once your volume gets serious. Their Luna-2 evaluators are dramatically cheaper than LLM-as-judge scoring, which lets you actually evaluate every request instead of a 10% sample. For high-volume customer-facing agents, that's the difference between catching a quality regression in real time and finding out from a support ticket.
Younger ecosystem than Braintrust or Langfuse, but the production-eval cost story is unmatched.
APM giant with bolted-on LLM observability for OpenAI and Anthropic calls.
The pick if and only if you're already a Datadog shop. The LLM features are competent but unmistakably bolted onto an APM product, and you pay APM-shaped pricing for them. Eval depth is shallow compared to the specialists.
If your CTO insists on one observability vendor across infrastructure and LLM apps, this is the path of least resistance with your security team. If you're not already running Datadog, don't start now just for LLM monitoring — the specialists win on every other axis.
How we tested
Same dataset (a 200-case ticket triage set), same task (classify into 7 categories), same models (GPT-4o, Claude Opus 4.7, Llama 3.3-70B). Time to first useful dashboard, time to first regression caught, and "would we ship this on a Friday" — three categories, weighted toward developer experience and cost predictability at scale.
What didn't make this list
We tested several others — Maxim AI (specialized eval scorer library), Vellum (visual workflow builder), Fiddler (enterprise governance) — but they're either narrower in scope or aimed at different buyers than "engineering team that wants to monitor an LLM feature in production." Reviews of those tools live on their company pages.
How to choose
- Default answer: Braintrust. If you don't have a strong reason to pick something else, this is the one.
- Hard requirement to self-host? Langfuse.
- Volume so high that LLM-as-judge online eval is cost-prohibitive? Galileo, layered with Braintrust or Langfuse for dev-time iteration.
- Already a Datadog shop and don't want a new vendor? Datadog, with eyes open about the gap on eval depth.
- Just starting? Helicone gets you to "I can see something" in five minutes; expect to graduate to Braintrust once monitoring isn't enough on its own.
The deeper question isn't which tool — it's whether you actually have an eval loop. Without one, the dashboard is just an expensive way to find out you have a problem after your users do. With one, monitoring becomes a means to a continuously improving product. That's the gap between picking a tool and picking a workflow.