Kloudfuse traces every LLM call as a span in your distributed traces. Track token costs, model latency, and errors across OpenAI, Anthropic, Bedrock, and Vertex AI. Not a separate tool. A native extension of APM.
Every LLM call is a span in your existing distributed traces. Follow a request from frontend through the LangChain agent, vector DB, and model call. One trace.
Prompt and completion tokens, latency, and errors emitted as standard metrics. Alertable, dashboardable, queryable in PromQL.
OpenAI, Anthropic, Gemini/Vertex AI, AWS Bedrock, Azure OpenAI. Built on OTel GenAI semantic conventions. No vendor lock-in.
A platform deep dive: how model calls, frameworks, token costs, and the infrastructure underneath land in one correlated view.
Kloudfuse auto-instruments LLM calls from OpenAI, Anthropic, Google (Gemini/Vertex AI), AWS Bedrock, and Azure OpenAI and normalizes them into a single view.
Every chain step, tool call, retrieval, and LLM invocation appears as a nested span in the trace. When a RAG pipeline is slow, you see exactly where time is spent.
Kloudfuse captures prompt and completion token counts on every span and maps them to cost. Slice by service, model, provider, environment, or customer segment.
Isolated LLM monitoring tools cannot do this. Kloudfuse stores LLM spans in the same data lake as your infrastructure metrics, application traces, and logs — so you see whether the bottleneck is the model provider, the vector database, the API layer, or the underlying node.
Thirty minutes on your telemetry. The cause, before the call ends.