Langfuse
Open-source LLM observability that traces, evaluates, and manages prompts from prototype to production.
Langfuse is the most credible open-source LLM observability platform if you need data portability and self-hosting. The v4 real-time performance jump (up to 165x faster) makes it a serious contender against closed tools like LangSmith. Its unified tracing, prompts, evals, and experiments cover the full AI engineering loop. Skip it if you want a zero-config SaaS or are locked into a vendor-specific platform.
Verified 1d ago · liveness 87/100 · cite: rightaichoice.com/tools/langfuse
- Engineering teams building production LLM agents needing deep observability
- Enterprises requiring self-hosted, SOC 2/HIPAA-compliant AI telemetry
- Developers who want prompt management, evals, and experiments unified
- Teams scaling to billions of LLM observations per month
- Solo devs needing a zero-config LLM logger without setup
- Teams already locked into a vendor-specific AI platform (e.g., LangSmith)
- Projects requiring sub-second trace ingestion latency (even v4 is near real-time, not instant)
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Skip Langfuse if you want a zero-config SaaS with instant setup, or if you're already locked into LangSmith and don't need self-hosting or data portability.
Going past 100k units/month on Core or Pro adds $8 per 100k units, which can add up quickly with high-volume usage.
Langfuse's freemium model is generous for prototyping: Hobby includes 50k units/month free, while paid tiers start at $29/mo. Its pricing is comparable to competitor LangSmith but offers more transparency with volume discounts. For teams needing self-hosting, Langfuse is often cheaper than closed alternatives because you avoid per-seat fees and can scale on your own infrastructure.
In short
Langfuse — Open-source LLM observability that traces, evaluates, and manages prompts from prototype to production. Best for Engineering teams building production LLM agents needing deep observability, Enterprises requiring self-hosted, SOC 2/HIPAA-compliant AI telemetry, Developers who want prompt management, evals, and experiments unified. Free to start; paid plans from $29/mo.
What's new in Langfuse
Checked yesterdayAcross the latest 5 updates: 4 feature updates and 1 changelog entry.
Langfuse v4: real-time, up to 165× faster
Released Langfuse v4 with real-time processing and up to 165x performance improvements, enabling faster ingestion and querying.
Consistent evaluator sampling
Compare evaluators on the same sample of matching observations for more consistent evaluation results.
Pulse: find the outliers in your traces
A compact chart strip above the Observations table shows count, cost, and latency spikes over time; click or drag to narrow the table.
Secure remote experiment triggers
Authenticate remote experiment trigger requests with signed and custom headers for improved security.
Manage dashboards via API, CLI, and MCP
Create and manage dashboards and widgets programmatically via the public API and Langfuse CLI, or let AI agents build them via the Langfuse MCP server.
Viability Score
How well maintained and how widely used is Langfuse? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Hierarchical traces with filtering by user, session, cost, latency, or metadata
- Real-time ingestion (v4, up to 165x faster)
- LLM-as-a-judge evaluations
- Heuristic and boolean evaluations
- Prompt versioning with one-click deploy and rollback
- LLM Playground to test prompts on production inputs
- Experiments with side-by-side test case comparison
- Human annotation queues and golden dataset creation
- Cost and latency dashboards with alerts
- Pulse chart strip to spot trace outliers
- Graph view with aggregated and expanded modes
- Multi-modal data support (images, audio, video)
- OpenTelemetry-native instrumentation
- Python and TypeScript native SDKs
About Langfuse
Langfuse is an open-source AI engineering platform that unifies observability, prompt management, evaluation, and experimentation in one integrated tool. It's built for AI engineers and developers who need deep visibility into their LLM applications, from debugging a single trace to scaling with billions of observations per month. With hierarchical traces, you can capture every LLM call, tool invocation, and retrieval step, then filter by user, session, cost, latency, or custom metadata. The platform includes prompt versioning with one-click deploys and rollbacks, an LLM Playground for testing prompts on real production inputs, and a full evaluation suite—LLM-as-a-judge, heuristic scores, human annotation queues, and custom pipelines. Langfuse is OpenTelemetry-native, so it works with any language and framework, and it integrates with 100+ tools, including LangChain, Vercel AI SDK, LiteLLM, and major model providers. Released in August 2026, Langfuse v4 delivers real-time processing with up to 165x performance improvements, making it competitive with closed alternatives. The platform is MIT-licensed and self-hostable via Docker, Kubernetes, or Terraform, ensuring data portability and no vendor lock-in. You can start free on the Hobby plan (no credit card), then scale to Core ($29/mo), Pro ($199/mo), or Enterprise ($2499/mo). Langfuse is ideal for engineering teams building production agents, enterprises needing SOC 2 or HIPAA compliance, and teams that want a single platform for the entire AI engineering loop.
Behind the Verdict
Langfuse stands out in the crowded LLM observability space by being open source and self-hostable, which is a huge win for teams with strict data residency or security requirements. The v4 release in August 2026 brought real-time processing and up to 165x faster performance, directly addressing the latency concerns that pushed some teams toward closed competitors. The unified platform approach—tracing, prompt management, evaluation, and experiments in one loop—means you don't have to stitch together multiple tools. The prompt versioning and one-click deployment are particularly strong, letting you manage prompts as a team sport. The evaluation suite is comprehensive: LLM-as-a-judge, heuristic scores, human annotation queues, and custom pipelines, all runnable on production data or during experiments. Monaco Editor integration for prompt editing and the Langfuse Assistant (in-app agent) that can investigate production data and take approved actions are thoughtful touches for developer productivity. On the downside, the free tier is limited to 50k units/month and 30 days of data access, which may not be enough for serious production use. The jump from Pro at $199/mo to Enterprise at $2499/mo is steep, though Enterprise adds SSO enforcement, RBAC, and a dedicated support engineer—critical for larger orgs. Self-hosting requires significant infrastructure management, so it's not for teams that want a fully managed experience. Also, while v4 is near real-time, it's not instant, so if you need sub-second trace ingestion latency, consider other options. Overall, Langfuse is a strong fit for engineering teams that value openness, data control, and a comprehensive toolkit, but it may be overkill for solo devs or those who prefer a hands-off SaaS.
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Real-world workflow fit
Concrete scenarios for the personas Langfuse actually fits — and what changes day-one when you adopt it.
You need to debug why your customer support agent gave a wrong answer. You open Langfuse, search for the trace by user or session, inspect the LLM calls, tool invocations, and see the exact input/output. You identify a prompt bug, fix it in the Prompt Management UI, and deploy to production with one click.
Outcome: Resolved the issue in minutes, improved response quality, and logged the fix for future reference.
You need to evaluate a new model provider (e.g., from OpenAI to Anthropic) on your own dataset. You create a dataset in Langfuse, run an experiment with both models, and compare side-by-side results on cost, latency, and quality. You use the insights to make a data-driven decision.
Outcome: Confidently switched providers after seeing quantitative results, saving costs without sacrificing quality.
You're responsible for cost monitoring across multiple teams. You set up dashboards and alerts in Langfuse to track cost per user and per feature, and configure Slack notifications for spikes. You use the Pulse chart to spot anomalies in real-time.
Outcome: Proactively managed costs, reduced overspend by 15%, and improved cross-team visibility.
Use Cases
- Debug a production agent's unexpected behavior by replaying the exact trace in the Langfuse UI.
- Compare two prompt versions on a dataset of 100 real conversations and pick the winner.
- Wire LLM-as-judge evals into CI to catch quality regressions before shipping a prompt change.
- Track per-user LLM cost in a multi-tenant SaaS and bill accurately.
- Run experiments to compare model providers side-by-side on your own test cases.
- Build golden datasets via human annotation queues to fine-tune or evaluate models.
- Set up monitors to alert your team when cost or latency spikes outside expected ranges.
Models Under the Hood
as of 2026-08-14
Limitations
- Langfuse's free Hobby plan includes 50k units/month and 30 days of data access, which may be limiting for production use.
- Paid tiers start at $29/mo (Core), but the jump to Pro at $199/mo and Enterprise at $2499/mo is steep.
- Self-hosting requires managing infrastructure (Docker, Kubernetes, Terraform).
- While v4 is real-time, it's not sub-second instant.
- The Teams add-on ($300/mo) is required for Slack/MS Teams support in Pro, which may be an unexpected cost.
- Also, some features like SSO enforcement and RBAC are locked to Enterprise.
as of 2026-08-13
Verification history
We have re-verified Langfuse 15 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Langfuse tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Hobby
$0/mo
Ideal for
Solo developers or small POC projects that need a free introduction to LLM observability with basic tracing and evaluation.
What this tier adds
Free entry point: includes 50k units/month, 30-day data access, 2 users, and all platform features with limits, no credit card required.
Core
$29/mo
Ideal for
Early-stage startups with production projects needing longer data access, unlimited team members, and in-app support.
What this tier adds
Adds 100k units/month included, 90-day data access, unlimited users, in-app support, and 4000 requests/min ingestion (vs 1000 on Hobby).
Pro
$199/mo
Ideal for
Scaling teams that need longer data history (3 years), high rate limits, and advanced evaluation features like unlimited annotation queues.
What this tier adds
Adds 3-year data access, data retention management, unlimited annotation queues, high rate limits, SOC2/ISO27001 reports, and prioritized support.
Enterprise
$2499/mo
Ideal for
Large organizations requiring enterprise-grade security and compliance with SSO enforcement, RBAC, and dedicated support.
What this tier adds
Adds audit logs, SSO enforcement, fine-grained RBAC, SCIM API, custom rate limits, uptime SLA, and a dedicated support engineer.
Where the pricing makes sense
The company stage and team size where Langfuse's pricing actually pencils out — and where peers do it cheaper.
Langfuse's freemium model is generous for prototyping: Hobby includes 50k units/month free, while paid tiers start at $29/mo. Its pricing is comparable to competitor LangSmith but offers more transparency with volume discounts. For teams needing self-hosting, Langfuse is often cheaper than closed alternatives because you avoid per-seat fees and can scale on your own infrastructure.
Setup time & first value
How long it actually takes to get something useful out of Langfuse — broken out by persona, not the marketing-page minute.
For a quick start, you can be tracing within 10 minutes using the Step-by-step guide with OpenAI, LangChain, or SDKs. Fully integrating with your existing stack and setting up evals may take a few hours. Self-hosting via Docker Compose can take about an hour, while Kubernetes and Terraform setups may take longer depending on your infrastructure.
Switching to or from Langfuse
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From LangSmith: Export your traces via LangSmith's API and import them using Langfuse's REST API or SDKs; you'll need to recreate datasets and eval configs manually.
- →From OpenAI's built-in trace tooling: Use Langfuse's OpenAI integration to capture traces directly, then build out your own dashboards.
- ↗To LangSmith: Export traces from Langfuse via its export APIs, then use LangSmith's import tools (if available) to migrate datasets and prompts manually.
Integrations
Resources & Guides
- Documentationlangfuse.com
Overview
Langfuse is an open-source LLM engineering platform (GitHub) that helps teams collaboratively debug, analyze, and iterate on their LLM applications. All platform features are natively integrated to accelerate the development workflow.
- Guidelangfuse.com
Guides
End-to-end examples and resources to get started with Langfuse for LLM Tracing, Monitoring, Prompt Management, and more.
- Resourcelangfuse.com
Langfuse Academy
Understand why LLM engineering is different and how to navigate the full AI engineering lifecycle.
- Resourcelangfuse.com
Langfuse
Traces, evals, prompt management and metrics to debug and improve your LLM application.
- Resourcelangfuse.com
Langfuse
Traces, evals, prompt management and metrics to debug and improve your LLM application.
- Resourcelangfuse.com
Support
Overview of available support options for Langfuse.
- Resourcelangfuse.com
Overview
Helpful link from langfuse.com
Tutorials & Learning
Tools that pair well with Langfuse
Common stack mates teams adopt alongside Langfuse, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Langfuse vs Langgraph
Choose Langfuse if your priority is observability, debugging, and prompt management for production LLM apps, with a need for multi-modal evals and alerts. Choose LangGraph if you're building complex, stateful multi-agent systems that require fine-grained workflow control, human oversight, and deep integration with LangSmith for evaluation. They can complement each other—use LangGraph for orchestration and Langfuse for observability.
Langchain vs Langfuse
If you need deep agent debugging with autonomous failure clustering and fix suggestions, LangSmith is the edge. If you want open-source flexibility, self-hosting, and unified prompt management plus observability, Langfuse is the pragmatic choice. Choose based on whether you need proactive root-cause analysis (LangChain) or full control and compliance via self-hosting (Langfuse).
Langfuse vs Litellm
If you need to centrally manage and route requests across 100+ LLMs with cost tracking and fallbacks, LiteLLM is your gateway. If you need deep observability, prompt management, and evaluations for production LLM apps, Langfuse is the observability layer. They integrate together, so a powerful stack uses both.
Langfuse vs Mlflow
If you need a single open-source platform that covers both traditional ML (experiment tracking, model registry) and LLM agents (tracing, prompt versioning, AI Gateway), choose MLflow. If your primary focus is production LLM observability with rich prompt management, evaluation workflows, and a mature SaaS option, Langfuse is more specialized and easier to adopt for LLM-only teams.
Langfuse vs Promptfoo
Choose Promptfoo if your priority is AI security — automated red teaming, guardrails, and CI/CD scanning against 50+ attack types, backed by recent OpenClaw injection analysis and ModelAudit launch. Choose Langfuse if you need production LLM observability, prompt management, and evaluations with deep framework integration (100+), now with multi-modal datasets and monitors/alerts. Both are open-source, but Promptfoo leans security-first while Langfuse is engineering-first.
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Open-source LLM agent observability with tracing, evals, and experiments
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