Respan for AI Teams
Trace production behavior, run evals, and ship improvements — without building observability from scratch. Purpose-built for teams shipping LLM applications.
Proven at scale
Works with OpenAI, Anthropic, Gemini, LangChain, LlamaIndex, and 20+ frameworks.
What you get
Purpose-built LLM observability with the debugging power you expect from traditional APM — plus LLM-specific features you can't get anywhere else.
Trace every LLM call
Structured logs for every request: inputs, outputs, latency, cost, tokens, model, and status. Searchable in real-time.
Debug agent pipelines
Visual span waterfall for multi-step agents. See which tool was called, what data passed between steps, and where failures occurred.
Attribute costs to features
Break down LLM spend by user, feature, model, and environment. Know which parts of your app drive 80% of the bill.
Run automated evals
LLM-as-judge, custom Python evaluators, and rule-based checks. Run on production traffic or against testsets.
Compare model variants
Structured experiments: same testset, multiple models or prompts, scored side-by-side with statistical comparison.
Manage prompts via API
Version, deploy, and A/B test prompts without code changes. Pull the active version at runtime with sub-5ms overhead.
Detect regressions early
Quality alerts when eval scores drop. Cost alerts when spend spikes. Latency alerts when P95 degrades.
Export to data pipelines
Push logs, traces, and eval results to S3, BigQuery, or any destination via REST API or webhooks.
How AI teams use Respan
Daily workflow
- →Debug a user-reported issue by searching logs
- →Review trace waterfall for slow agent steps
- →Check eval score trends after a prompt change
- →Monitor cost dashboard for unexpected spikes
Quality & evals
- →Run hallucination and relevance checks on every output
- →Build eval datasets from real production traffic
- →Compare model variants with statistical A/B tests
- →Track quality scores over time per feature
Ship & govern
- →Deploy prompt updates without code releases
- →Set cost budgets and quality thresholds per project
- →Export data for fine-tuning or compliance audits
- →Onboard new team members with shared dashboards
How it works
Instrument once, observe everything, evaluate continuously, improve systematically.
Instrument
Wrap your LLM client with the Respan SDK. Two lines of code. Every call logged automatically.
→ Structured logs and traces
Observe
Search logs, trace agents, track costs and latency in real-time dashboards.
→ Full production visibility
Evaluate
Run automated evals on production outputs. Build testsets from real traffic. Compare variants.
→ Quality scores and experiment results
Improve
Update prompts from the dashboard. Deploy changes without code releases. Verify with evals.
→ Measured improvement
Govern
Set alerts, budgets, and quality thresholds. Export data for compliance. Audit every change.
→ Controlled AI operations
By the numbers
2 lines
to instrument
100%
of requests captured
<80ms
P99 ingestion latency
Real-time
search and tracing
Works with your stack
Model providers
- OpenAI
- Anthropic
- Google Gemini / Vertex AI
- Groq
- Mistral
- Together AI
- AWS Bedrock
- Azure OpenAI
- Any OpenAI-compatible endpoint
Frameworks
- OpenAI SDK (Python + Node)
- LangChain
- LlamaIndex
- Vercel AI SDK
- Mastra
- CrewAI
- AutoGen
- OpenAI Agents SDK
Languages
- Python
- TypeScript / JavaScript
- Go
- Ruby
- Any language via REST API