From 26 authors in 25 discussions. Small sample. Run a pilot on one production app to measure which failures it surfaces and how long tracing them takes.
88 sources from practitioners, by month and sentiment.
Team accounts lean positive, and 26 team and leadership sources give an opinion. All 4 discussing open source and self-hosting praise it. Upgrades draw the most concern, in 5 of 6 sources discussing them. Eight use OpenTelemetry to keep the backend replaceable. Platform choice now splits evenly, 5 to 5, since Apify moved its evals to Langfuse.
Positive, mixed and negative sources
1214
The score counts each source once, as the split does. No source is negative. See How the numbers work.
Counted for Teams and technical leaders, from 31 sources. Change group
What practitioners praise
Every capability practitioners describe positively, ranked by how many sources say so. Open one to read who said it.
1Trace-Level DebuggingAll 5 sources on it praise itEngineers locate failures in model and tool calls, including agent steps. Traces show inputs and outputs, latency, and cost.
The Medic team publishes the agent's OpenTelemetry traces to Langfuse and reads each run as a waterfall of steps to find what caused a weak diagnosis. It then grades prompt changes with offline evals in its own replay harness.
"To improve the system, we invested in observability and testability. We used OpenTelemetry to publish traces to Langfuse, and by viewing the agent's execution as a waterfall diagram of steps, we could better understand the cause of lower quality responses."
Excerpt, Drasko Profirovic
Published Jul 20, 2026Pinterest's Medic agent, which diagnoses failing Spark jobs through MCP tools over job logs and metrics
Cresta self-hosts one Langfuse instance per Kubernetes cluster with one organization per customer, and propagates trace context across Go and Python services over gRPC. It deletes traces after four weeks and uses the trace tree to find the step where an agent chain went wrong.
"We self-host Langfuse rather than using a managed SaaS offering."
Excerpt, James Idzik
Published Mar 26, 2026self-described: Cresta, AI agents for customer conversations, self-hosted Langfuse per Kubernetes cluster
Khan Academy's AI platform team built a Go component interface so any Khanmigo feature that conforms gets tracing in Langfuse. Engineers now open a failing case's stack trace in Langfuse and debug from there.
"…we recently built this component interface in Go that when you as a developer conform to that component interface, it automatically think-- means that things like tracing and Langfuse are available as long as you conform."
Excerpt, Shawn Jansepar
Published Feb 5, 2025Khanmigo AI tutor, where an AI platform team owns the developer experience for product teams
Chose PromptLayer for prompt management because they needed a git-like prompt API with release labels and rollback, while finding Langfuse much better at tracing.
Published Jan 18, 2026self-described: Hacker News thread: ClickHouse acquires Langfuse
Every concern practitioners raise, most raised first.
1Upgrades & Migrations5 of 6 sources on it raise it as a concernVersion upgrades introduce breaking changes. These include the v2 to v3 ClickHouse migration and SDK rewrites, plus the v4 data model and Cloud cutover.
Moves Traigent's Langfuse connector to the Observations API v2 because Langfuse Cloud removes the trace and v1 observation read endpoints on November 16, 2026.
Published Oct 7, 2026self-described: Traigent SDK's Langfuse connector
Medic's CHT agent moves its new Langfuse tracing from the v3 npm package to the OpenTelemetry-based JS SDK v5 because Langfuse Cloud removes legacy batch ingestion on November 16, 2026.
"…migrates the Langfuse tracing added there from the legacy langfuse v3 npm package to the OpenTelemetry-based JS SDK v5…"
Excerpt, alexosugo
Published Sep 23, 2026self-described: Medic's Community Health Toolkit agent
Brainforge moved its agent platform to Langfuse v4 in August 2026, three months before the Cloud cutover. The v4 move caused the costliest of the 18 integration problems it logged, and the team found v4 measurably faster to query.
"Coexisting OTEL providers is the real fight. We run HyperDX and Langfuse in one process, and the two pull against each other on the global tracer provider and conflicting OpenTelemetry version lines."
Excerpt, Uttam Kumaran
Published Sep 18, 2026Brainforge agent platform with Mastra agents, chat routes, voice traces, and Cloudflare workers on Langfuse Cloud
In the v4 tracing table, root span rows of their LangGraph agent show US$0.00, so trace cost is visible only inside each trace, unlike the earlier traces table.
Published Aug 13, 2026self-described: LangGraph agent in production
NonBioS builds most tooling in house and uses only Langfuse for observability, which was breaking down for them at their volume; they hoped v3 would hold up.
Published Jun 19, 2025self-described: NonBioS, AI engineering agent
GILD's fact checks, which don't count as sources
Checked Oct 12, 2026.Cloud becomes v4-only on November 16, 2026. Legacy ingestion and read APIs stop working, as do Python SDK v2 and older, JS/TS SDK v3 and older, and trace-level LLM judges. Self-hosters have no forced deadline. v3 receives security patches through January 2027. Langfuse v4 is live
Questions the sources split on, with the count on each side.
Evidence remains split
When practitioners weigh Langfuse against another platform, which do they keep?
55
Chose or kept Langfuse, 5 sources
Some chose Langfuse over LangSmith, Phoenix, Helicone, MLflow, or a homegrown tool, citing open source, self-hosting, integrations, and price.
Chose another tool, 5 sources
Others chose Phoenix, LangSmith, PromptLayer, Sentry, a Grafana stack, or no tracer, for less infrastructure to run, a prior familiarity, or stronger prompt features.
Evidence remains split
Do practitioners run Langfuse themselves or use Langfuse Cloud?
72
Self-host it, 7 sources
Most run Langfuse in their own infrastructure, from a laptop to Kubernetes, to keep data in house or avoid usage fees.
Use Langfuse Cloud, 2 sources
Others use Langfuse Cloud and handle unit billing and the v4 cutover instead, such as filtering spans to stay within the free tier or moving SDKs before November 16, 2026.
The evidence
Whose experience this is
The three groups, then every practitioner by the context they work in, and how emphasis changes with scale.
Three groups drawn from the same sources. Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. Signals open on Teams and technical leaders.
Teams and technical leaders
31 sources. Product teams and technical leaders describe Langfuse in production. They report on traces and prompt management, plus evals and self-hosting costs.
Individual builders
57 sources. Developers tracing their own apps and agents describe setup and self-hosting, including SDK reliability.
All practitioners
88 sources. This group combines product teams and startups with solo developers. Their hosting choices and traffic volumes differ.
88 sources from practitioners, by the context they work in. Filled rows count for this group.
Individual builderDevelopers building on the platform on their own57 sources
Emphasizes Setup, self-hosting load, and SDK bugs
Small product team2 to 10 engineers, or an unnamed startup team14 sources, counted here
Emphasizes Platform choice and running ClickHouse
Product or platform team11 to 50 engineers, or a named company's team10 sources, counted here
Emphasizes Self-hosted tracing, evaluation loops, and SDK migrations
Technical leadershipLeaders setting model and platform choices across teams6 sources, counted here
Emphasizes Self-hosting for data control and swap cost
Organization-wideEnterprise or company-wide AI platform1 sources, counted here
Emphasizes Amplifon runs Langfuse at the platform level so its development teams trace and evaluate agents from day one
What changes with scale. Individual builders emphasize setup speed and the open-source core, alongside ClickHouse operations; their 2026 GitHub issues report Redis timeouts, stuck v4 backfills and retention jobs that block inserts on self-hosted stacks. Cresta, Pinterest and SuperDial describe self-hosted or OpenTelemetry tracing in production. Named teams also report moving to v4 SDKs or plain OpenTelemetry, and Brainforge moved its agent platform to v4 three months before the Cloud cutover. Teams split on the platform: bbv, Apify and CREATE SOMETHING moved to Langfuse, while Bayes Impact moved to Phoenix.
Where the sources come from
Where this group's sources were published.
Counted for Teams and technical leaders, from 31 sources. Change group
46 sources for this group, 15 of them documentation
Developer Communities19, 41% of 46 sources
Product Documentation15, 33% of 46 sources
Engineering Blogs & Reviews6, 13% of 46 sources
Podcasts, Talks & Videos6, 13% of 46 sources
Older accounts remain when applicable. Accounts tied to changed prices or plans, or license terms, carry Needs a recheck.
Each account links to a public page and names its author and publication date.
A finding needs at least 5 sources from 3 different authors for each group. A disagreement also needs at least 2 sources on each side.
Keeps Langfuse's own blog, customer stories, comparison pages, and posts by Langfuse staff out of the counts.
Counts only accounts published on or after January 1, 2025.
Checks the self-hosting, upgrade, pricing, and retention findings against Langfuse's current documentation.
Oct 25, 2026: adds 5 company engineering blog, personal blog, tutorial, and conference-talk sources. Open Source & Self-Hosting now reaches 5 team sources, so Signals open on leaders and teams.
Oct 26, 2026: adds 4 recorded AI Engineer talks by named engineering leaders and teams at Khan Academy, SuperDial, Amplifon, and Pinterest. Each speaker's role is as the AI Engineer page states it. Brainforge's own site states founder Uttam Kumaran's role.
Oct 28, 2026: adds 19 records. Seventeen come from GILD's R3 research, which searched for problems and switches first, so they take no side: 6 GitHub issues on langfuse/langfuse, including Redis socket timeouts every 31.5 seconds after heavy OTLP ingestion and stuck v4 backfills, 6 X posts, 3 Reddit threads and 2 Hacker News comments. Apify and CREATE SOMETHING, which moved their evals to Langfuse from Arize Phoenix and Braintrust, join the platform-choice split, now 12 to 11 across all practitioners. Adds each author's words to 27 records and marks 28 self-described roles.
Oct 30, 2026: a second-model check removed an X post and a Reddit comment that praise self-hosting with no result, Brainforge's card, which sits on a consultancy's sales page while its pre-SURFACE record stays, and Trade Republic's card and record, whose talk the check could not read. It corrected Bayes Impact's move, a merged plan that keeps Langfuse read-only for 30 days, bbv's card, which now says only what its merged pull request says, the issue date behind bbv's license reason in the Arize comparison, Shawn Jansepar's self-described role and Ramp's results, which its talk gives for the Research agent alone. Ramp's card names its speakers as the talk does, Ramp engineers Ryan and David. 88 public accounts remain and the counted figures do not move.
Method
A running review of what teams building AI features on Langfuse report, from individual builders to company-wide platforms. Each finding links to its sources, and each conflict shows both sides.
Counts cover only the sources GILD reviewed.
Who counts
Teams and technical leaders
Small product team, Product or platform team, Technical leadership, Organization-wide
Individual builders
Individual builder
All practitioners
Everyone in the teams and leaders and individual builders groups
Choose a group to select whose experience counts. Within that group, each author counts once in the practitioner sentiment, with their latest view. GILD files each source in a group from how the account describes its author. A role or employer marked self-described is what the source states about themselves. Documentation checks facts for every group and never counts toward sentiment.
How the numbers work
Overall signal. Positive votes minus negative ones, per 100 votes from the sources that take a side. Each author counts once, with their latest view, whoever they are and wherever they posted. The older weighted reading, where accounts GILD matched to a named person count three times when GILD lists the author among technical leaders and twice otherwise, is shown beside it as a second view. Where an author wrote more than one source, the line beside the split gives the votes, one per author, so the score follows from it. When one author's sources from the same day disagree, that author counts as mixed, and a half point rounds away from zero. Sources with no opinion are listed and not counted. Strong positive is +50 or higher, positive +20 to +49, mixed −19 to +19, negative −20 to −49, strong negative −50 or lower.
Weighted reading, second view. Counted the older way, where an account GILD matched to a named person counts two or three times, teams and leaders read +59 strong positive (method sentiment/S1). GILD printed that number as "GILD score" until October 29, 2026; most of those matches rest on a name or a self-described role.
Method change, Oct 30, 2026. GILD changed how practitioner sentiment counts, to one vote per author. Weighted, it read +59 Strong positive; with one vote per author it reads +46 Positive. No evidence changed.
Unscored. A group gets a score only with 20 sources from 15 authors. Below that, one source moves the score about 5 points, so the read says "Unscored until 20 sources from 15 authors" and shows its counts.
Independence. A source whose author works for the vendor, for one of its partners or programs, or for a competitor, and a page the vendor published or ran, is listed with that tie and counts toward nothing. One author counts on at most two records per product. The line under the score says how many authors and how many separate discussions the counted sources come from: comments in one thread are one discussion.
Praise and concerns. How many of the sources that discuss a theme praise it or raise it.
Consensus. A statement joins when 20% of the group's sources support it and fewer contradict it. Contradicting sources are counted separately. Below that line a statement shows as building. At 40% or more it reads Strong agreement; below, Recurring point.
Disagreements. The count on each side. GILD names no winner and gives no share of all users.
Minimum evidence. A finding needs 5 sources from 3 authors; a disagreement also needs 2 on each side. Smaller counts stay in the table below, marked under the minimum.
Labels on a source
A source's date and whether it still applies are separate: a recent post can describe an old version, and an old one can still hold.
Documented
Stated in official documentation.
Reported
A practitioner's account of their own use.
Measured
Backed by the source's own measurements, such as a comparative test or tracked team metrics.
GILD analysis
GILD's check of a claim against official documentation.
Current
Describes the product as it is now.
Still applicable
Older, and the behavior or lesson still holds.
Needs a recheck
May depend on a plan, version or setting that changed since it was written. It keeps counting until GILD rechecks it, since the rest of the account usually holds, and its card says so.
Out of date
No longer accurate. Kept in the record; counts toward nothing current.
Flagged for a recheck, of the sources that take a side: teams and leaders 2 of 26, individual builders 1 of 43, all practitioners 3 of 69.
Limits
The figures count reviewed public sources. Bug reports and migration work tilt GitHub toward problems, so an issue counts as mixed unless its author rejects or abandons Langfuse. Ramp engineers' recorded talk leans positive and appeared on parent company ClickHouse's YouTube channel. On Oct 25, 2026, GILD added 4 company, engineering, personal, and tutorial blog posts and 1 recorded InfoQ talk. Hacker News returned 20 qualifying comments from 2025 onward. Arctic Shift returned 9 qualifying posts across r/LLMDevs and r/LangChain, plus r/LocalLLaMA. Searches of r/devops and Reddit comments timed out. The 15 accounts added on Oct 28, 2026 from GILD's R3 research came from searches aimed at problems and switches, so they carry no sentiment or position and count toward no figure; their words and measurements stay on the record.
Sources cover Langfuse v2, v3, and v4 but do not consistently name versions. Records retain the versions stated. In December 2024, v3 moved self-hosted storage to ClickHouse. All product features became open source in June 2025. ClickHouse acquired Langfuse in January 2026. Cloud becomes v4-only on November 16, 2026. Accounts tied to earlier prices or plans, or license terms, carry Needs a recheck.
Every count, and the sources behind it
18 findings for 3 groups
Each row opens its sources for the group you are reading.
GILD's checks of claims against official documentation, kept apart from the source counts.
Open Source & Self-Hosting. Langfuse's self-hosted pricing lists a free MIT edition with all core features and unlimited usage. An Enterprise key adds project RBAC and retention policies, plus audit logs and server-side masking. Self-hosted pricing, checked Oct 12, 2026
Datasets & Evaluations. Langfuse documents online production evals and offline dataset experiments. Teams use LLM judges and code or decision-model evaluators, plus annotation queues. Its GitHub Action fails pull requests when scores cross thresholds. Evaluation overview, checked Oct 12, 2026
Prompt Management. Langfuse documents prompts fetched at runtime and cached client-side by the SDK, so a prompt update ships without a code deploy and adds no request latency. Prompt management, checked Oct 12, 2026
Self-Hosting Operations. Self-hosted Langfuse needs web and worker containers with PostgreSQL, ClickHouse, Redis or Valkey, and S3-compatible storage. Docker Compose runs on one VM without high availability or scaling. It provides no backups. Production uses Kubernetes or Terraform modules. Self-host Langfuse, checked Oct 12, 2026
Upgrades & Migrations. Cloud becomes v4-only on November 16, 2026. Legacy ingestion and read APIs stop working, as do Python SDK v2 and older, JS/TS SDK v3 and older, and trace-level LLM judges. Self-hosters have no forced deadline. v3 receives security patches through January 2027. Langfuse v4 is live, checked Oct 12, 2026
Unit Pricing & Cost. Langfuse bills a unit for each trace and observation, plus each score. Outputs from its evaluators and annotation queues, including experiments, count too. Core includes 100,000 monthly units, then charges US$8 per 100,000. Tiered rates decline to US$6 above 50 million. Billable units, checked Oct 12, 2026