The box both of them check
If you're adding your first bit of visibility into an LLM app, the simplest version is a print() statement before the API call. That's enough while you're the only one testing it.
The natural next step is to swap that print statement for a real tracing SDK. You wrap your client, make a call, and a UI shows you a span with the prompt, the response, and how long it took. That feels solved — until your team grows past one prompt and one person testing it by hand.
That's the point where the real friction shows up: conditional prompt logic, a way to diff two prompt versions before you ship one, a way to catch a call your app got wrong before a user complains, and a currently-live guardrail against risky output. "It produces a trace" doesn't cover any of that. And the frustrating part is that this doesn't fail loudly — most open-source, self-hosted LLM observability tools look interchangeable from their landing pages, because they'd all check the same three boxes: open source, self-hosted, traces your calls.
Opik (built by Comet ML) and Langfuse are two of the most-used tools in that box. So we tested the actual boundary: we built the exact same prompt — a support-triage agent that changes tone for VIP customers and lists their open tickets — on both, independently, then set the two runs side by side.
TL;DR
- Both platforms hit the exact same wall on prompt templating — neither supports real conditional logic, only flat variable substitution.
- Both confirmed, independently, that their Playground alone does not produce a trace — only a wrapped SDK call does.
- They diverge sharply on prompt versioning: Opik ships a real diff view and environment labels, Langfuse's version history has neither.
- Their measured client-side overhead is different in kind, not just number — and the two numbers come from two separate test sessions, not one shared benchmark.
- Both have a feature that does the same job under a different name — auto-scoring live production traffic.
- They've drifted into genuinely different territory: guardrails and regression test suites (Opik) versus threshold alerting (Langfuse).
- And as of five months ago, they're no longer comparable as two independent startups — Langfuse was acquired by ClickHouse in January 2026.
At a glance
| Aspect | Opik | Langfuse |
|---|---|---|
| Prompt templating | Flat {{variable}} only |
Flat {{variable}} only |
| Playground produces a trace | No — confirmed hands-on | No — confirmed hands-on |
| Version diff | Real red/green diff view | No diff control found |
| Environment labels | "Deploy to" tags a version |
production/latest labels |
| Measured SDK overhead | +102ms (real, CI doesn't cross zero) | +15ms (not distinguishable from zero) |
| Auto-scores live traffic | Online Evaluation rules | LLM-as-a-judge evaluators |
| Standout feature | Guardrails (topic + PII) and test suites | Monitors — alerts to Slack/Webhooks/GitHub |
| GitHub stars (checked today) | 21,252 | 32,778 |
| Owned by | Comet ML (in-house product) | ClickHouse (acquired Jan 2026) |
Full breakdown below.
Where they hit the exact same wall: templating
Both platforms' prompt editors only do flat {{variable}} substitution — no {% if %}, no {% for %}. We needed a VIP-customer branch and a rendered list of open tickets, and on both platforms we had to flatten that logic into plain text ourselves before the prompt ever saw it, keeping only the simple fields (like {{company}}) as real template variables.
Neither product hides this — it's a real, shared design choice, not a bug in either one. If your prompts need branching logic, both push that logic back onto your own code (a pre-render step) rather than into the template itself.
Where they hit the exact same wall, again: the Playground doesn't trace
This one surprised us enough to check twice on each platform. Running a prompt from Opik's Playground left its Logs tab at "No traces yet." Running a prompt from Langfuse's Playground did the same thing — no trace appeared until we called the SDK-wrapped client instead. On both, the interactive "try it in the UI" surface and the "get a trace out of it" surface are two separate things, which is easy to miss if you only ever click around the Playground before wiring in the SDK.
Where they genuinely diverge: prompt versioning
Opik has a real "Diff" button — edit a prompt to create v2, and it shows the old system message in red strikethrough next to the new one in green. It also has a "Deploy to" menu that tags a specific version production, staging, or development with a colored badge.
Langfuse has environment labels too (production / latest), but we could not find any diff or compare control anywhere in its version history UI — each version sits in a flat list, and comparing two of them means opening both and reading side by side yourself.
Where the numbers genuinely differ (and why you shouldn't stack them)
We measured client-side SDK overhead for both platforms — but in two separate benchmark sessions, run on different days as part of two different posts, each with its own direct-to-provider baseline. That matters: network conditions differ session to session, so these two numbers aren't from one shared interleaved test and shouldn't be read as a single ranked table.
With that caveat stated plainly:
| Opik SDK | Langfuse SDK | |
|---|---|---|
| Measured overhead | +102ms | +15ms |
| 95% bootstrap CI | [+14, +191]ms | [-90, +150]ms |
| Statistically real? | Yes — CI does not cross zero | No — CI crosses zero, indistinguishable from noise |
Comparison with other platform can be found at AcruxCore
Opik's track_openai() wrapper adds overhead that clears the noise floor in its own test session. Langfuse's instrumentation, in its own separate test session, didn't clear that floor at all — its "cost" was statistically zero. Both are real findings about client-side instrumentation, not about which product is "faster" than the other, since neither routes a call through a gateway the way a proxy-style tool would.
Same idea, different name: scoring production traffic automatically
Both platforms have a feature that does the same underlying job — score live traffic as it lands, with no manual review step — under completely different names and UI.
Langfuse's LLM-as-a-judge Evaluators walk you through writing an eval template: a scoring prompt, a model to run it, and the trace variables it reads.
Opik's Online Evaluation page, under Production, lets you create a rule that scores every matching trace as it arrives.
If a platform-neutral feature list only checked for the name "online evaluation" or "LLM-as-a-judge," it would miss that both products actually offer this.
Where they've genuinely diverged: guardrails vs. alerting
Opik ships a guardrails panel — a topic-restriction check with a sensitivity slider and a comma-separated restricted-topics list, plus a PII (personally identifiable information) check that flags categories like credit card numbers, phone numbers, and email addresses, each with its own threshold. It also has a dedicated Test suites object for pre-deployment regression testing, distinct from its Experiments feature — import cases from a CSV/JSON file or the SDK, each with an expected output and a scoring method.
Langfuse doesn't ship anything like that. What it has instead is Monitors — set a threshold on cost, quality, or latency once, and get a notification routed to Slack, a webhook, or a GitHub Action when a metric moves outside it.
Neither one has the other's feature. If you need to block a call before it reaches a user for containing PII, Langfuse has nothing for that today. If you need a Slack ping when your P95 latency creeps up, Opik has nothing for that today.
Who's behind them, and how big is each
This is where the story changed since we first tested these products. Numbers below are live as of this week, pulled straight from each project's GitHub API.
| Opik | Langfuse | |
|---|---|---|
| GitHub stars | 21,252 | 32,778 |
| Forks | 1,690 | 3,524 |
| Contributors | ~136 | ~190 |
| Open issues | 178 | 764 |
| Repo created | May 2023 | May 2023 |
Both repos were created within a week of each other in May 2023 and are still receiving commits daily — this isn't a "one's abandoned" story. Langfuse has the larger community footprint on every raw count, including a notably higher open-issue count, which can mean more active usage surfacing more edge cases, or a smaller team keeping up with triage — we didn't dig into issue-close rates to tell which.
The bigger difference is who owns each project. Opik is a product inside Comet ML, a New York-based ML platform company founded in 2017, with $6.8M in disclosed seed funding (2018, 2020) and nothing disclosed since. Langfuse was an independent, Y Combinator-backed startup — until ClickHouse acquired it on January 16, 2026, the same day ClickHouse announced a $400M Series D at a $15B valuation. Both companies have publicly committed to keeping Langfuse open source and self-hostable going forward.
For a reader picking a platform to build on, that's a real signal worth weighing alongside the feature list: one tool is a side product of a smaller, steady company; the other just became an integration point inside a much larger, newly-flush infrastructure company. Neither is inherently the safer bet — but they're different bets.
We also compared each project's documentation footprint via its own sitemap, with a caveat: Langfuse's sitemap covers its entire domain (blog, changelog, academy, and docs together — 822 URLs, of which only 112 are strictly /docs), while Opik's docs live under Comet's site and its sitemap is scoped to just that docs subtree (709 URLs). So "709 vs 822" isn't an apples-to-apples count of documentation depth — it's closer to "Opik's docs alone" versus "Langfuse's docs plus its blog, changelog, and handbook combined."
What we're not calling a winner
We picked seven points, not the full feature list either platform ships, and skipped a symmetric "pro/con" for every row — some things really are one-sided (neither platform gained a fake counterweight just to look fair). Templating and Playground-tracing are genuine ties. Versioning favors Opik. The overhead numbers aren't comparable to each other, only to their own baselines. Guardrails/test-suites and Monitors/alerting are two teams building in different directions, not one being behind the other.
If you're choosing between them today, the feature list would point you toward whichever gap matters more to your team — guardrails and regression testing, or threshold alerting. The ownership question is the one that didn't exist a year ago, and it might now matter just as much as either feature list.
Sources: comet-ml/opik and langfuse/langfuse on GitHub (checked 2026-08-10); ClickHouse's acquisition announcement; Langfuse's own "joining ClickHouse" post. Product screenshots and hands-on findings are from our own independent testing of both platforms.
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