Latitude

Key features

• OTEL-compatible trace ingestion OTEL-compatible trace ingestion: send agent telemetry over the documented OpenTelemetry exporter or the Latitude SDKs and each interaction arrives as traces of LLM calls, tool calls, and retrieval steps, with no proprietary format lock-in.[1][2]

• Session, user, tool, memory, and cost views Session, user, tool, memory, and cost views: follow multi-turn sessions, per-user activity, per-tool reliability, persistent memory records, and spend with cost-per-session views, tags, environments, and release markers.[1]

• Behavior clustering and conversation intelligence Behavior clustering and conversation intelligence: sessions group by meaning into topics with trend and outcome metrics such as escalations, resolutions, abandonments, and retries, surfacing what the agent keeps doing without a hand-written query.[1][2]

• Semantic and filtered search Semantic and filtered search: search every trace by meaning, exact text, or metadata filters to move from a broad question to a focused set of real examples.[1]

• Signals with lifecycle Signals with lifecycle: recurring failures group into prioritized signals with trends, failing traces, affected-user counts, and a lifecycle the team can act on, fed by annotations, flaggers, evaluations, and custom checks.[1][2]

• Monitors with Slack, email, and webhook alerts Monitors with Slack, email, and webhook alerts: watch a signal, a saved search, or raw traffic and open an incident with in-app, email, or Slack notification when something new appears or escalates.[1][2]

• Coding-agent dispatch to pull request Coding-agent dispatch to pull request: route a new or escalating signal to Claude Code or Cursor with full context, sample traces, and a deep link; the agent implements the smallest correct fix and opens a pull request, including an editor-side flow over MCP.[1][2]

• Datasets, experiments, and human annotation Datasets, experiments, and human annotation: build versioned golden datasets from validated production traces, compare traffic slices or releases side by side, and leave inline feedback that becomes structured, searchable signal.[1][2]

Who will love it

• Teams debugging agents in production Teams debugging agents in production: trace, session, and cost context plus behavior clustering isolates failing model, tool, and retrieval steps without retrofitting instrumentation.[1][2]

• Teams running continuous reliability loops Teams running continuous reliability loops: monitors plus signal-fed evals plus dispatch to pull request turns each incident into a tracked fix with regression scoring after release.[1][2]

• Organizations needing a compliance path Organizations needing a compliance path: Enterprise SAML SSO, role-based access, on-premises deployment, an uptime and support SLA, and SOC 2 plus ISO 27001 reports address regulated procurement.[1]

Who should avoid it

• Non-technical teams wanting turnkey AI Non-technical teams wanting turnkey AI: SDK or pipeline integration, API keys, and plan setup assume engineers who own code and infrastructure.[1][2]

• Teams assuming Langfuse-scale community resources Teams assuming Langfuse-scale community resources: Langfuse offers the larger community with official SDKs for LangChain, LlamaIndex, OpenAI, and Vercel AI, so narrower-ecosystem teams should weigh integration coverage first.[1][2]

• Budget-capped teams ignoring credit metering Budget-capped teams ignoring credit metering: Starter caps at 20,000 credits with 30-day retention and Pro adds $20 per extra 10,000 credits, so costs scale with trace volume.[1]

Alternatives and compatibility