The first OpenTelemetry-native observability platform for production AI systems
Treat LLMs like the unreliable microservices they are. Get real-time traces, cost anomaly detection, and semantic regression testing—built for backend engineers, not data scientists.
Your OpenAI bill jumped 300% and you have no idea which endpoint, user, or feature is burning cash.
LLM responses degraded but your monitors didn't fire. Customers complained first.
One bad LLM response requires tracing across logs, databases, and vector stores with zero correlation.
No way to test prompt changes against real production traffic before deploying.
Vector DB returns low-quality chunks but you won't know until it's too late.
Datadog doesn't understand semantics. LangSmith was built for notebooks, not production.
Zero vendor lock-in. Built on OTEL standard. Send traces to Lumina, Datadog, and Grafana simultaneously. Instant adoption by 10,000+ companies already using OTEL.
Trace the entire pipeline: User → Router → Embedding → Vector DB → Reranking → LLM → Response. Root cause in 30 seconds instead of 4 hours.
The only tool that can query: "Show requests where cost > $0.50 AND latency > 2s AND semantic_similarity < 0.8". This query is impossible anywhere else.
One-click replay of real requests against new prompts or models. Semantic diffing shows exactly what changed. Quality gates prevent regressions.
Alert when your /chat endpoint got 40% more expensive AND quality degraded—in under 500ms. Hybrid detection: hash-based checks + LLM evaluation.
NATS JetStream, PostgreSQL/ClickHouse, sub-500ms alerting. Built by engineers who've scaled fintech systems, not data scientists building dashboards.
Join backend engineers from leading teams who are tired of debugging AI failures with grep. Early access launching Q1 2026.
Early adopters get lifetime 50% discount + priority support