Choose an LLM observability tool
TinySet compares LLM observability tools on pricing and self-hosting. TinyResearch picks one for a small AI team.
TinySet
Dataset · 49 rowsLLM observability tools, with pricing model, free tier, self-host option and supported frameworks
| Tool | Pricing model | Free tier | Self-host option |
|---|---|---|---|
| HoneyHive | Usage-based per event, custom enterprise pricing | Yes | Yes |
| AgentOps | Usage-based per event | Yes | Yes |
| Langfuse | Open source, free self-host; usage-based cloud plans | Yes | Yes |
| Arize Phoenix | Open source, free | Yes | Yes |
| Helicone | Open source, usage-based | Yes | Yes |
| Langtrace | Open source, free | Yes | Yes |
| Lunary | Per-user monthly subscription | No | Yes |
| LiteLLM | Open source free to self-host; Enterprise priced to scale | Yes | Yes |
TinyResearch
ReportWhich observability tool should a 5-person AI team adopt, and why?
AI systems fail differently from traditional software. Models hallucinate, retrieval pipelines return stale or irrelevant context, and agent workflows loop or misuse tools — all without raising a 500 error or triggering a conventional alert [10]. Standard APM tools capture latency and error rates but miss quality degradation entirely. By one estimate, Gartner projects 60% of software engineering teams will use dedicated AI evaluation and observability platforms by 2028, up from 18% in 2025 [10].
