A platform dedicated to providing unbiased reviews of newly launched applications, analyzing everything from their features to their full potential.
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© 2025 Scoutforge. All rights reserved.
A platform dedicated to providing unbiased reviews of newly launched applications, analyzing everything from their features to their full potential.
info@scoutforge.net© 2025 Scoutforge. All rights reserved.
A platform dedicated to providing unbiased reviews of newly launched applications, analyzing everything from their features to their full potential.
info@scoutforge.net© 2025 Scoutforge. All rights reserved.
I've been testing Spanlens for the last few days, and I'm genuinely impressed. It's an open-source (MIT) LLM observability platform that logs every request to OpenAI, Anthropic, and Gemini with just one line of code. Setup is ridiculously easy – you simply swap your provider SDK for Spanlens's drop-in, and boom, you get request logging, cost tracking, agent tracing, anomaly detection, PII scanning, model recommendations, evals, and experiments. The self-hosted option means your data never leaves your VPC, which is huge for compliance. I love the waterfall traces for debugging multi-step agents – it shows critical path, cost attribution, and latency outliers instantly. The cost dashboard is a lifesaver for understanding why your AI bill spiked. And the eval/experiment workflow lets you replay real traffic against different models and prompt versions, so you can actually measure quality improvements before shipping. With a free tier for 50K requests/month and Pro at $29/month, it's in...
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Open-source LLM observability with traces, evals, and prompt management. Self-hostable, but SDK more complex.
LLM observability with caching and rate limiting. Focused on cost control but less emphasis on evals and traces.
LangChain's observability platform, tightly integrated with LangChain ecosystem. Not self-hostable and seams with other frameworks.
Open-source AI observability with focus on LLM evals and drift detection. More complex setup and less cost transparency.
Spanlens is a fresh, MIT-licensed LLM observability tool that excels at dead-simple integration and practical features like agent tracing, cost recommendations, and experiment replay. It directly addresses pain points in the provided alternatives—easier setup than Langfuse, more evals than Helicone, and self-hosting unlike LangSmith. The free tier and flat pricing are attractive, but its youth means real-world validation is still pending.
| Features |  Spanlens |  Langfuse |  Helicone |  LangSmith | AP Arize Phoenix |
|---|---|---|---|---|---|
| Integration complexity | One-line SDK swap or CLI init | More complex SDK setup | Proxy-based, moderate complexity | LangChain-native, framework lock-in | More complex setup for evals |
| Self-hosting | Docker one-liner, free forever | Supported, more involved | Supported | Not self-hostable | Open-source, self-hostable |
| Evals & experiments | LLM-as-judge + dataset replay | Strong evals & prompt management | Limited evals focus | LangChain-centric evals | Strong LLM evals & drift detection |
A platform dedicated to providing unbiased reviews of newly launched applications, analyzing everything from their features to their full potential.
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