Galileo AI Alternative? Langfuse vs. Galileo AI
This article compares Langfuse and Galileo AI, two platforms that help developers evaluate and monitor applications built on LLMs.
While both platforms aim to improve LLM reliability, they differ in philosophy. Galileo AI is a proprietary, all-in-one platform focused on real-time intervention and automated metrics. Langfuse is an open-source, engineering-centric platform focused on flexibility, data control, and deep observability.
At a Glance
| 🪢 Langfuse | Galileo AI | |
|---|---|---|
| Open Source | Yes (MIT licensed, fully open codebase) | No (closed-source commercial platform) |
| Self-Hosting | Yes (free to self-host; extensive documentation and user base) | Yes (Enterprise on-prem available, otherwise SaaS) |
| Integrations | 80+ SDKs/integrations for OpenAI, LangChain, etc., via OpenTelemetry | SDK & auto-instrumentation for popular providers like OpenAI, Anthropic |
| Openness | Not opinionated; designed to be flexible enough to cover all workflows and evaluation methods via SDKs/APIs | Opinionated approach with templated guardrail evaluators using proprietary models |
| Scalability | Built to scale (ClickHouse-based architecture handles >5B events/month) | No public information available |
Galileo AI
Galileo AI positions itself as an enterprise-grade evaluation and governance solution. Its core value proposition revolves around reliability and safety.
- Real-Time Protection: Galileo’s standout feature is its built-in “Protect” system, which uses proprietary Luna-2 models to intercept and block hallucinations or toxic outputs in real-time before they reach the user.
- Automated Evaluation: It provides an opinionated workflow with pre-packaged metrics, allowing data science teams to get scores out of the box without engineering custom judges.
Langfuse
Langfuse is the open-source standard for LLM observability. It is designed for engineering teams that want to treat LLM traces like any other application log - providing full visibility without vendor black boxes.
- Observability: Langfuse captures the full lifecycle of an LLM interaction - prompts, model responses, tool calls, and latency.
- Engineering-First Workflow: It supports complex debugging (tracing chain-of-thought), prompt management, and diverse evaluation methods (human feedback, SDK methods, or any LLM-as-a-Judge).
- Scalability: Built on a high-performance architecture (ClickHouse), Langfuse handles millions of events with minimal latency, ensuring it scales with your production traffic.
A detailed summary why users chose Langfuse can be found here.
Distribution
| pypi downloads | npm downloads | docker pulls | |
|---|---|---|---|
| 🪢 Langfuse |  |  |  |
| Galileo AI |  |  | N/A |
Feature Comparison
The following table breaks down how Langfuse and Galileo AI approach the three critical pillars of the LLM engineering lifecycle: Tracing, Evaluations, and Annotations.
| 🪢 Langfuse | Galileo AI | |
|---|---|---|
| Tracing | ||
| Cost Tracking | Fine-grained tracing of all token types, including reasoning and cached tokens. Ability to set custom costs for private LLMs or negotiated rates | Limited configurability of tokens and costs. |
| Full Text Search | Full-text search across inputs, outputs, and metadata. | No support for full-text search. |
| Distributed Tracing | Support and documentation for distributed tracing (via OpenTelemetry and native SDKs). | Distributed tracing is in beta but lacks full OpenTelemetry support. |
| Tool Calls | Dedicated UI views and agent graphs for analyzing tool usage and behavior. | No specific views for tool invocations or agent step visualization. |
| Replay Traces | Ability to open generations in the Langfuse Playground to iterate on the prompt and debug issues. | Manual copying of messages into the playground. |
| Trace Exports | Ability to export the whole trace, including all observations as JSON to process or debug with coding agents. | No direct export of trace details, but Galileo exposes logs via their MCP server. |
| Custom Dashboards | Ability to build custom dashboard widgets to drill down on various metrics. | No dashboarding support in the UI. |
| Evaluations | ||
| Datasets | Both tools support datasets and dataset versioning. Langfuse offers a notebook to generate synthetic datasets but no UI feature. | Both tools support datasets and dataset versioning. Galileo AI allows to use AI to create test datasets. |
| Experiments | Experiments can be triggered via the dataset view. Both tools support an in-UI flow to experiment on prompts using datasets. Both tools also support experiments via the SDKs. | Ability to trigger experiments in the Playground. This allows users to see prompts, dataset items, and experiment results at a glance. |
| Corrected Output | Langfuse users can correct the model output right in the trace detail view for future evaluation and fine-tuning. | Correct model output is not possible. |
| Comments | Langfuse has a native comments feature where users can comment on traces, observations, prompts and experiment results. The feature also allows commenting only on specific parts of i/o and @mention other users. | No comment feature. (Only annotations available) |
| LLM-as-a-Judge | Standard support for scoring live tracing data and experiment runs using any model the user configures and an evaluation prompt. | Support for categorical LLM-as-a-Judge scores. Allows having multiple judge models for one evaluation job. |
| Code-based evaluations in UI | Not supported yet, but on the roadmap | Supported via custom metrics in the UI. |
| Guardrails | Not built-in (integrate with external tools; Langfuse is not in the critical path by design) | Built-in real-time guardrails/protection for outputs using their proprietary Luna-2 model. |
| Annotations | ||
| Annotation Queues | Integrated annotation queues for domain experts to review filtered traces (e.g., “Check all negative feedback traces”). | No dedicated view where domain experts can annotate a subset of traces. |
| Annotations in Trace view | Langfuse supports annotating traces and observations via the Trace detail view | This is available via the annotations feature and supports, other than Langfuse, also text annotations, thumbs up/down and stars. (Langfuse has a native comment feature for text annotations) |
| Annotating Experiments | Ability to add human annotation scores in Dataset Experiment compare view | User has to navigate to the Trace of the experiment to add annotation scores. |
Summary
Choose Galileo AI if: You are a data science team that needs a “turnkey” solution for safety. If you require real-time guardrails to block bad responses and prefer a managed, opinionated set of evaluation metrics out of the box, Galileo’s specific focus on governance is a strong fit.
Choose Langfuse if: You are an engineering team building production applications. If you value open-source software, require flexible and unopinionated workflows, and need a platform that scales effortlessly with your traffic while allowing you to define your own evaluation logic, Langfuse is the robust, developer-centric choice.
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