Traceloop turns evals and monitors into a continuous feedback loop - so every release gets better
OpenLLMetry is perceived to have a very positive reputation, particularly noted for its accessible AI capabilities and ease of use. Users appreciate its open-source nature, which allows for extensive customization and community-driven improvements. While there are limited explicit complaints in the social mentions, the lack of detailed reviews could suggest a nascent user base or limited adoption. Pricing sentiment is not discernible from the available information, indicating it may either be competitive or not a focal point of discussion amongst users.
Mentions (30d)
0
Reviews
0
Platforms
2
GitHub Stars
7,151
980 forks
OpenLLMetry is perceived to have a very positive reputation, particularly noted for its accessible AI capabilities and ease of use. Users appreciate its open-source nature, which allows for extensive customization and community-driven improvements. While there are limited explicit complaints in the social mentions, the lack of detailed reviews could suggest a nascent user base or limited adoption. Pricing sentiment is not discernible from the available information, indicating it may either be competitive or not a focal point of discussion amongst users.
Features
Use Cases
Industry
information technology & services
Employees
3
Funding Stage
Merger / Acquisition
Total Funding
$66.7M
197
GitHub followers
24
GitHub repos
7,151
GitHub stars
20
npm packages
Pricing found: $0 / mo
Made an awesome-list for everything LLM cost, would love contributions
So a few months back I got surprised by my Anthropic bill which somehow racked up like $400 ish on a staging key in a few weeks just running evals, no budget cap pretty dumb in hindsight I mean it’s not a big cost but I should have been careful nonetheless After that I started keeping a notes file of tools that actually helped reduce cost stuff like token counters, pricing pages that update properly, caching layers, prompt compression libs, observability tools (helicone, langfuse, langsmith, etc) it slowly grew to 80–90 entries so I cleaned it up and put it on github: https://github.com/ankitvirdi4/awesome-llm-cost what’s in there right now: pricing calculators + token counters observability / tracing (helicone, langfuse, langsmith, openllmetry, phoenix) caching (gptcache, semantic caching approaches) model routers (openrouter, notdiamond, portkey) prompt compression + context window stuff eval cost tracking self hosting / GPU cost calculators everything is linted (awesome-lint), short descriptions for each entry, and I checked links recently so nothing should be dead if there’s anything you’ve used that saved you money on inference, drop it here or send a PR especially looking for more prompt compression stuff, that section feels kinda weak rn not affiliated with anything listed btw just got tired of having 80 bookmarks submitted by /u/OldComposerbruh [link] [comments]
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Deep analysis of traceloop/openllmetry — architecture, costs, security, dependencies & more
Yes, OpenLLMetry offers a free tier. Pricing found: $0 / mo
Key features include: Start tracking in seconds, Run quality checks with zero setup, Define quality on your terms, Make quality part of the pipeline, Open standards at the core, Works with every stack, Compatible with the tools you actually use, Product.
OpenLLMetry is commonly used for: Real-time monitoring of LLM performance in production environments, Automated quality checks for machine learning models, Debugging and troubleshooting of black box models, Integration of observability tools into existing ML pipelines, Customizable quality metrics for diverse ML applications, Seamless collaboration between data scientists and DevOps teams.
OpenLLMetry integrates with: TensorFlow, PyTorch, Kubernetes, Apache Kafka, Prometheus, Grafana, Jupyter Notebooks, Slack, GitHub, AWS S3.
OpenLLMetry has a public GitHub repository with 7,151 stars.
Based on user reviews and social mentions, the most common pain points are: cost tracking, anthropic bill.
