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AI Observability and Cost Governance

See what every agent, model, and tool did on every run, and what it cost, before AI operations scale.

Discuss your use caseView the template

The problem

Once agents reach production the questions start: why did this run cost so much, which tool failed, and what did the model actually see? Without answers, teams stall.

How it runs today

Teams bolt on a separate observability vendor, wire exporters by hand, and still cannot connect a cost spike to the workflow change that caused it.

With Heym

Every run records a trace with payloads, timing across model and tool calls, and token cost per model. OpenTelemetry export ships the same spans to your own stack.

The workflow, end to end

The Governed Web Research Agent template is an importable starting point: an agent fetches pages through a governed MCP proxy, and every run produces a full trace of model calls, tool calls, and MCP requests to inspect.

Workflows runTraces and spans recordedCost and latency rollupsInspection and tuning
Governed Web Research Agent (MCP)
View template
Open this template, inspect every node, and import it

See it in Heym

A short walkthrough recorded in the product.

Opening a trace to follow an AI workflow run step by step, with the model and tool calls behind each result.

Where control lives

Costs compute from your own model price table, error rates surface by model and time range, and any suspicious run opens into its full payloads for audit.

AI operations you can explain: every output links back to its inputs, tool calls, latency, and cost.

Built with these Heym capabilities

Execution traces
Cost and token rollups
Per-model charts
Timing breakdowns
Tool call inspection
LLM cost table
OpenTelemetry export
Time range analytics

Common applications

Cost per workflow trackingModel comparison by cost and latencyDebugging failed agent runsAudit trails for AI outputsOpenTelemetry export to existing APMError rate monitoring

Deployment and integration

  • Self-host with Docker or Kubernetes, keeping data on your infrastructure.
  • Connect your own model providers: OpenAI, Ollama, vLLM, and more.
  • Integrate over HTTP, webhooks, Slack, email, and MCP tools.
  • Expose finished workflows as APIs, portals, or MCP servers.

Have a process that fits this pattern?

Show us the workflow, data sources, tools, and human decisions involved. We will help map it to Heym.

Discuss your use caseExplore workflow templates