Al

All you need is Prometheus and Jaeger for LLM Observability

Hacker News

All you need is Prometheus and Jaeger for LLM Observability

Hey folks! I'm thrilled to let you all know about something my friend and I have been working on at [OpenLIT]( https://github.com/openlit/openlit )! For those of you working with LLMs, we’ve got some pretty cool updates. We’ve successfully integrated Prometheus and Jaeger—yes, the go-to observability tools to fully support your LLM application observability. This means you can stick with the tools you already know and trust! Here's how it works: Imagine spending less time on setup and more on what truly matters: enhancing features and functionality. OpenLIT simplifies the process of adding observability to your LLM applications through OpenTelemetry (OTel). Just a single line of code and you can start monitoring key attributes like costs, token usage, user interactions, and performance metrics. And it doesn't stop at Prometheus and Jaeger! OpenLIT is also compatible with other backends like Grafana Tempo, or any OTel-compatible system, giving you the flexibility to store and visualize data just the way you like it. Head over to our guide to get started. Oh, and we've set you up with a Grafana dashboard that's pretty much plug-and-play. You're going to love the visibility it offers. We're super keen to see how OpenLIT integrates into what you’re doing! Give it a go and share your thoughts. Your feedback is super valuable and will help us make OpenLIT even better. Swing by and star our project on GitHub here -> https://github.com/openlit/openlit Can’t wait to see OpenLIT in action in your LLM applications! Cheers! Patcher

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, visual, single · Missing: mac, agents, macos
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, compatible · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released
63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: visualize, way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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