7 Langfuse Alternatives Worth Considering in 2026

Langfuse is a reasonable default recommendation for LLM tracing and evaluation, and for good reason: it's open source, self-hostable under MIT, and covers tracing, evals, and prompt management in one place. It was also acquired by ClickHouse in January 2026, which is prompting some teams to at least survey what else exists before committing further. This is that survey: seven real, currently maintained tools, what each one actually does differently, and who genuinely fits each one, based on their own documentation and repositories rather than a generic feature list.

1. Braintrust

Braintrust is the closest hosted alternative when your work is mostly eval iteration rather than always-on tracing: writing evals, running them against datasets, and comparing prompt variants side by side. It's hosted only, with Starter free and Pro around $249 a month according to Braintrust's pricing page, and its own materials frame the pricing model around predictable scaling rather than seat count. We cover it head to head against Langfuse, including the self-hosting tradeoff in full, in our Braintrust vs Langfuse comparison.

2. Arize Phoenix

Phoenix is Arize's open source observability platform, and it's the strongest pick if full self-hosting with zero feature gates is the actual requirement. It's built on OpenTelemetry and OpenInference from the ground up, so it accepts traces over the standard OTLP protocol and ships first-class instrumentation for frameworks like LangChain and LlamaIndex and SDKs across OpenAI, Bedrock, and Vertex (Arize Phoenix on GitHub). It covers tracing, LLM-based evaluation through its phoenix.evals library, experiment tracking, prompt management, and dataset versioning, all runnable locally, in a notebook, or via Docker. It ships under the Elastic License 2.0 rather than MIT, which is worth checking against your own license policy before you standardize on it.

3. Helicone

Helicone sits closer to a gateway than a pure observability backend: it proxies your provider calls and logs prompts, completions, cost, and latency as a side effect of sitting in the request path. It's fully open source under Apache 2.0 and self-hostable, documented for Docker, Kubernetes, and manual deployment, and its own team notes that a modest instance is enough for most self-hosted setups (Helicone's self-hosting launch post; Helicone open source docs). Its hosted tiers start free at 10,000 requests a month, with Pro at $79 a month. Helicone fits teams that want observability to come for free as a byproduct of routing through a gateway, rather than instrumenting their application code directly. Worth flagging: Helicone's hosted product moved into maintenance mode after Mintlify acquired the company in March 2026, so weigh that against the fully open source, self-hostable option if long-term hosted support matters to your decision.

4. Weights & Biases Weave

Weave is Weights & Biases' entry into LLM-specific observability, extending a platform ML teams already know from traditional model training. It automatically tracks every decorated function call, capturing inputs, outputs, cost, latency, and evaluation scores, and builds a hierarchical execution tree so you can see a full agent or RAG pipeline run end to end (Weights & Biases Weave documentation). It fits teams that already have W&B in their ML workflow for experiment tracking and want LLM observability to live in the same place rather than a separate tool with a separate login.

5. PromptLayer

PromptLayer leads with prompt management rather than tracing: a central registry with immutable version history, diffs, and rollback, with observability and evaluation built around that registry rather than the other way around (PromptLayer's platform overview). Its pricing lists Free at $0, Pro at $49 a month plus per-transaction fees, and Team at $500 a month, per PromptLayer's own pricing. It's a good fit for teams where non-engineers, prompt writers, domain experts, need to iterate on and ship prompt changes without touching application code, with engineering keeping tracing and evaluation attached to those same versions.

6. Comet Opik

Opik is Comet's open source LLM evaluation and observability platform, Apache 2.0 licensed and free to self-host as a complete stack, backend included, not just a client SDK (Opik on GitHub). It covers tracing, scoring against datasets, and aggregate performance comparison across application versions, with direct integrations for LangChain, LlamaIndex, Ragas, promptfoo, and LiteLLM. Comet's own announcement of the project is explicit about its open source commitment:

"Comet has been contributing to machine learning open source for seven years and will continue to do so."

Gideon Mendels, Comet co-founder and CEO, Comet's Opik launch press release

Opik fits teams that want an Apache-licensed, fully self-hostable option with a track record behind the company shipping it, rather than a newer or single-purpose project.

7. Traceloop (OpenLLMetry)

OpenLLMetry is a different shape of alternative entirely: it's not a standalone platform but a set of OpenTelemetry extensions, built and maintained by Traceloop under Apache 2.0, that capture LLM-specific signals like model name, prompt and completion tokens, and latency (OpenLLMetry on GitHub). Because it's built on standard OpenTelemetry, it plugs into observability backends you may already run, Datadog, Honeycomb, or others, rather than requiring a dedicated LLM observability product at all. This is the right pick if your team's answer to "where should LLM traces live" is "wherever our other traces already live," not a new dashboard.

How self-hosted, open source, and focus actually compare

Sourced from each project's own GitHub repository or documentation, linked above, September 2026. "Self-hosted" reflects whether the full core feature set can run on your own infrastructure at no license cost, not whether a hosted convenience option also exists.

What actually pushes a team to look for an llm observability tools list

The searches that lead here tend to cluster around a few real triggers rather than idle curiosity: hitting Langfuse's enterprise gate for a feature like SCIM or audit logs that a compliance team suddenly requires, outgrowing self-hosted ClickHouse operations at real production volume, or wanting evaluation to be the primary workflow rather than a feature added on top of tracing. If your trigger is specifically "we need evals to be the main event," Braintrust, Opik, and Phoenix's eval libraries are the more direct comparisons. If it's "we need this to run entirely inside our own infrastructure with no exceptions," Phoenix, Helicone, and Opik are the three genuinely full-featured self-hosted options on this list.

Why open source llm evaluation matters for some teams and not others

Open source availability matters most when data residency, vendor risk, or long-term cost at high volume are real constraints, not preferences. A regulated team that cannot send prompts and completions to a third party's servers has a materially different evaluation than a five-person startup optimizing for time to first working integration. For the latter, a hosted product's managed infrastructure is very often worth paying for; the ongoing engineering cost of operating Postgres, ClickHouse, and Redis at scale isn't free even when the software license is. Weigh open source against your actual constraints, not against a general preference for open source as a category.

Where llm11 fits

None of these seven tools do what llm11 does, and llm11 doesn't do what any of them do; they're adjacent categories, not competitors. llm11 is a router that picks the cheapest model in a pool capable of handling each request, then verifies the response inline, schema and groundedness checks always, heavier checks on requests that earn them, before escalating once to a stronger model if a check fails. That verification happens at request time, on every call. Every tool on this list is built for a different timescale: tracing what happened across production over days or weeks, or running offline evaluations against a curated dataset before you ship a change. Teams frequently run both: llm11 for inline, per-request verification, and one of these platforms for the longer-horizon tracing and evaluation work that inline checks were never meant to replace. If you're evaluating this category more broadly, our list of LLM evaluation tools covers the wider field beyond just Langfuse alternatives, and our LLM observability tools roundup narrows specifically to the tracing side of the category. For the head-to-head against the platform this whole list is an alternative to, see our Braintrust vs Langfuse comparison, and the wider comparison hub lines up gateways, routers, and eval platforms side by side.

Next step

Pick the two candidates from this list that match your actual constraint, self-hosting requirement, eval-first workflow, or existing OpenTelemetry investment, and run a real week of your own traces through both before deciding. A live trial against your own workload will tell you more in an afternoon than any comparison table, including this one.

Frequently asked questions

Is Langfuse actually bad, or is this just about alternatives existing?

Langfuse remains a strong, genuinely full-featured open source option, and nothing here suggests otherwise. The value of a list like this is knowing what else is available so a choice is deliberate rather than default, especially after an ownership change like the ClickHouse acquisition.

Which of these alternatives is the most similar to Langfuse itself?

Arize Phoenix and Comet Opik are the closest structural matches: both are open source, self-hostable, and cover tracing plus evaluation in one platform the way Langfuse does. The meaningful differences come down to license terms (Elastic License 2.0 versus Apache 2.0 versus MIT) and which specific integrations and ecosystem each one leans into.

Do any of these tools replace the need for inline answer verification?

No. Every tool on this list operates on traces after a request completes, or on offline datasets before a change ships; none of them intercept a live response and decide whether to escalate to a stronger model in real time. That's a distinct problem from tracing or evaluation, and it's what a verification layer like llm11's is built to do.

Can I self-host more than one of these at once?

Yes, and some teams do, for example running OpenLLMetry as the instrumentation layer feeding into a self-hosted Phoenix or Opik backend. It adds operational surface area, so it's worth doing only when each tool is covering a genuinely distinct need rather than duplicating another one already in your stack.