I’m looking for tools that work like LangSmith but can be installed locally on my own servers. I need something to track how well my language models are performing and monitor the prompts I’m using. Privacy is important for my project, so I can’t use cloud-based solutions. Has anyone found good open source or self-hosted options for LLM observability and prompt tracking? I want to measure response quality, latency, and token usage across different models. Any recommendations for local deployment would be really helpful.
Skip hunting for multiple monitoring tools - there’s a better way.
I run LLM workflows where the automation platform handles monitoring automatically. Route your LLM calls through a proper automation tool and you get built-in logging, performance tracking, and prompt management. No separate monitoring infrastructure needed.
Use an automation platform that handles API calls, data processing, and logging in one spot. Set up workflows that automatically capture response times, token counts, and quality metrics while keeping everything on your infrastructure.
I’ve watched teams waste time piecing together different monitoring tools when they could automate the whole process. You’ll get better visibility into LLM performance and can easily tweak monitoring logic when needs change.
Throw in custom quality checks, A/B test different prompts, and get automatic alerts when performance tanks. All while keeping full control over your data.
Latenode nails this kind of automation and gives you the local deployment control you want: https://latenode.com
honestly, opentelemetry + jaeger works great for basic monitoring. takes more setup than langfuse, but you get distributed tracing - super helpful when running multiple models. had some config issues at first, but once it’s running, the observability is rock solid for self-hosted setups.
Built something custom for this exact problem last year when we needed privacy-compliant LLM monitoring at work.
Went with Phoenix by Arize - solid self-hosted option that handles token tracking, latency monitoring, and prompt evaluation. Setup was way easier than piecing together separate tools.
The automatic prompt drift detection was clutch. It catches response degradation before you’d even notice. Plus it works with most LLM frameworks without major code changes.
Heads up though - resource usage gets heavy with lots of requests. Had to tune sampling rates so our monitoring server wouldn’t choke.
Also checked out LangWatch - newer but promising. Lighter than Phoenix with nice A/B testing features for different prompts. Still pretty rough though.
For quick wins, Phoenix gets you up fastest. For long-term flexibility, building on InfluxDB gives you way more control over your metrics.
MLflow’s worth a shot if you’re already using their stuff. Deployed it with their model registry 6 months back and the tracking handles most of what you need. Logs custom metrics for response quality, grabs latency data automatically, and tracks token usage across model runs. UI isn’t as slick as dedicated LLM tools but works fine for self-hosted setups. Pro tip - get your storage backend right from day one. Started with local file storage and got crushed when logs piled up. Switched to PostgreSQL and everything ran smooth. The experiment comparison stuff’s actually solid for A/B testing prompts, though you’ll write custom logging code to capture prompt metrics how you want.
Separate monitoring tools create way more work than necessary. You’re building an entire observability stack when you could just automate everything.
Skip Phoenix and MLflow. Build workflows that handle LLM calls AND capture metrics automatically. Route everything through automated workflows that log response times, track tokens, and measure quality scores - no dedicated monitoring infrastructure needed.
This beats standalone tools because monitoring gets baked into your actual LLM processes. Add conditional logic to flag bad responses, auto-retry failed calls, and switch models based on performance thresholds.
You can easily add custom quality checks, rotate API keys, and handle rate limiting in the same workflows. No separate systems to maintain or integrate.
Best part? Flexibility. When monitoring needs change, just update the workflow instead of reconfiguring multiple tools or writing custom scripts.
Latenode handles this LLM workflow automation perfectly and runs on your own infrastructure: https://latenode.com
The Problem:
You need a self-hosted solution for monitoring the performance of your language models and tracking prompts, prioritizing privacy and avoiding cloud-based options. You want to measure response quality, latency, and token usage across different models.
Step-by-Step Guide:
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Install Langfuse: Langfuse is a self-hosted observability tool designed for tracking key metrics related to LLM performance and prompt management. Begin by installing it. The exact installation process will depend on your system, but generally involves using Docker. Consult the Langfuse documentation for detailed instructions tailored to your environment. Expect the process to involve pulling the Docker image and setting up the necessary database (the documentation will guide you through this).
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Configure Langfuse: Once installed, you’ll need to configure Langfuse. This usually involves setting up environment variables (consult the Langfuse documentation for specifics). Key settings will likely include database connection details and any API keys or authentication tokens required for integrating with your LLMs. The documentation will outline how to connect it to your specific Language Models.
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Integrate with your LLMs: Langfuse needs to be integrated into your existing LLM workflow. This typically involves adding instrumentation to your code to send relevant data to the Langfuse server. The exact method of integration will depend on your LLM framework and libraries (e.g., LangChain). Check the Langfuse documentation for examples and guidance on integrating it with your specific setup.
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Monitor and Analyze: After successful integration, you can start using the Langfuse dashboard to monitor your LLM performance. You should see metrics on response quality, latency, token usage, and prompt versions. The dashboard is intended to provide a clear overview of model performance trends.
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Prompt Management: A key feature of Langfuse is its prompt management capabilities. This allows you to version your prompts, compare the performance of different versions, and set up basic quality scoring.
Common Pitfalls & What to Check Next:
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Database Setup: The database setup can be tricky; ensure you have properly configured the database connection according to the Langfuse documentation. Common issues include incorrect credentials or network connectivity problems.
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Instrumentation Errors: If your LLMs aren’t properly instrumented, Langfuse won’t collect the necessary data. Carefully review the integration steps and check the Langfuse logs for any errors.
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Advanced Configurations: Langfuse’s documentation might lack detail on advanced configurations. If you encounter issues, explore the project’s GitHub repository or consider posting questions in their community forums. You may need to write custom scripts for some metrics beyond the standard ones.
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Resource Usage: Monitor your server’s resource usage, especially if you’re processing a large volume of requests. Langfuse, like any monitoring tool, can consume resources. Adjust settings accordingly.
Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!
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