Open-source Fiddler AI alternatives

Langfuse is an open-source LLM engineering platform that helps teams develop, monitor, evaluate and debug AI applications together. You instrument your app, and Langfuse records traces of LLM calls and surrounding logic such as retrieval, embeddings and agent actions, so complex runs and user sessions can be inspected and debugged.

Prompt management lets you centrally version and iterate on prompts, with caching on server and client so that changes do not add latency to your app. Evaluation features cover LLM-as-a-judge, code evaluators, user feedback, manual labeling and custom pipelines through the API and SDKs. Datasets provide test sets and benchmarks, there is an LLM playground for trying prompts, and integrations exist for OpenAI, LangChain and LlamaIndex. It is built on the ClickHouse database, and the Langfuse team has been part of ClickHouse since January 2026.

You can use Langfuse Cloud or self-host it, which the maintainers say takes minutes. The repository license is listed as 'Other' because it combines open-source and enterprise components, so check the terms. It suits teams shipping production LLM features who need visibility into quality, latency and cost.

MLflow is an open-source AI engineering platform for agents, LLM applications and ML models. It helps teams debug, evaluate, monitor and optimize AI applications in production while keeping control of costs and access to models and data. It is written in Python, released under the Apache-2.0 license, and has been developed since 2018, with topics covering MLOps, model management and LLMOps.

For LLM and agent work it offers production observability with traces, plus evaluation, prompt management and prompt optimization, and an AI Gateway that governs costs and model access. It works with Python, TypeScript and JavaScript, Java and other languages, and integrates with OpenTelemetry and MCP. Getting started means starting an MLflow server, enabling logging in your code and running it, then exploring traces and metrics in the web UI on port 5000. A setup wizard can connect to an MLflow server or a Databricks workspace and add tracing with a coding agent.

Opik is an open-source LLM observability and evaluation platform built by Comet. It covers the application lifecycle starting with early traces during development and ending with production monitoring, for teams that build LLM apps and AI agents. The code is Python, licensed under Apache-2.0, and the README says the full platform is free to self-host. The project documentation lives on the Comet site.

Capabilities include deep tracing of LLM calls and agent activity including complete trace trees for agents with several steps and tool calls, and evaluation with datasets, experiments and LLM-as-a-judge metrics for tasks like hallucination detection, moderation and RAG assessment. An Agent Optimizer SDK improves prompts and agents, dashboards and online evaluation rules support production monitoring, guardrails help with safe AI practices, and a PyTest integration tests LLM pipelines on each commit. Integrations include frameworks such as LangChain, LlamaIndex and OpenAI clients.

Arize Phoenix is an open-source AI observability platform for experimentation, evaluation and troubleshooting. It helps teams building applications on large language models understand what their systems are doing at runtime, measure quality and debug problems before and after release.

Tracing relies on OpenTelemetry-based instrumentation to record an LLM application's runtime behavior. Evaluation uses LLMs to benchmark an application's performance with response and retrieval evals, and datasets can be versioned. The topics point to integrations with OpenAI, Anthropic, LangChain, LlamaIndex and smolagents, as well as work on agents and prompt engineering.

Phoenix is written in Python and published under a license listed in the repository. For managed production workflows, Arize also offers a separate product called Arize AX. The README is available in English and Simplified Chinese, and the project documentation is hosted on the Arize site.