Observe every AI call.
Optimize every token.
OpenLLMetry-compatible AI observability. Track token usage, latency, cost, and agent behavior across every LLM provider with zero code changes.
Deep Dive
Core Capabilities & Architecture
Explore how LLM Insights instruments, processes, and correlates high-cardinality telemetry at scale.
Three lines of code. Complete visibility.
Add OpenLLMetry-compatible instrumentation to your Python or JavaScript AI code and get observability into every LLM call: no API key changes, no proxy.
- Works with OpenAI, Anthropic, Google, Groq, Cohere, and more
- Captures token counts, latency, and model per call
- Agent + workflow + tool spans tracked in full
- Vector DB retrieval spans for RAG pipelines
Find the token waste before your finance team does
See exactly which models, agents, and users are spending your AI budget. Attribute costs down to the conversation level.
- Cost attribution by model, user, agent, and workflow
- Budget alerts before overruns happen
- Model comparison: same task, which model is cheapest?
- Token efficiency trends over time
Debug AI failures with the same tools you use for APIs
Agent errors, slow responses, and failure-prone prompts all show up in the same trace explorer you use for microservices.
- Full conversation traces with prompt + completion content
- Error rate by model and operation type
- Latency percentiles per provider and model version
- Correlate LLM spans with the services that called them
Built for every team that cares about reliability
One platform, tailored to how your team actually works.
Debug every agent call
See the full trace of every agent decision, tool call, and LLM response.
Control AI costs
Budget alerts, cost attribution, and model efficiency tracking.
Model governance
Track which models are in use, their costs, and error rates across all teams.
LLM calls are just spans, and that's the point
aiAxonIQ ingests OpenLLMetry-format spans through the same OTLP pipeline as your application traces. Token counts, model names, latency, and cost live next to your service telemetry, so an expensive prompt correlates to the request, the user, and the service that triggered it.
Also in the platform
Guides
Start LLM monitoring in 5 minutes
Your existing OpenTelemetry instrumentation works unchanged, so there is nothing to rewrite and nothing to migrate.