Spanlens
Open-source LLM observability: log cost, latency, tokens, and agent traces for every OpenAI, Anthropic, Gemini, or Ollama call with a one-line baseURL swap. Self-hostable, MIT.
About Spanlens
Best for: LLM application developers, AI engineering teams, Data scientists, Organizations monitoring LLM costs
Key features
- Logs every LLM request with model, tokens, latency, and cost details
- Tracks spend, tokens, and cache savings per model and project
- Agent tracing visualizes multi-step workflows and dependencies
- Anomaly detection flags spikes in latency, cost, or error rate
- PII and prompt-injection detection with auto-masking
- Prompt versioning and A/B experiments with LLM-as-judge evaluation
- Self-hostable under MIT license with Docker support
- Exports data in JSON, CSV, Parquet, or streams to data warehouses
Use cases
- Monitor and analyze LLM usage and costs across multiple providers
- Detect and investigate anomalies in LLM performance or spend
- Trace and debug complex agent workflows involving multiple LLM calls
- Evaluate prompt changes and compare versions using automated and human-in-the-loop methods
- Enforce security by detecting PII and prompt-injection attempts in LLM traffic
Pros
- Self-hosting is free with no seat or usage limits
- Minimal integration effort with one-line baseURL swap
- Exports data in multiple formats and integrates with data warehouses
- No credit card required for free plan
Limitations
- Free plan limited to 50K requests/month and 14-day retention
- Paid plans have request quotas and overage fees
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