Langtrace
Trace LLM application calls with OpenTelemetry and route data to any observability backend
Langtrace is profiled here as a Prompt Management tool for engineering teams. Read about features, pricing, and how it compares to related options in the tools directory.
Description
Langtrace is an AGPL-3.0 LLM observability tool built by Scale3 Labs, a Palo Alto company founded in 2022 by Karthik Kalyanaraman and Ola Muse following their exit from Coinbase's Infrastructure and Observability team. Scale3 originally built blockchain node monitoring before pivoting to LLM observability in early 2024. The company raised $5.3M in seed funding led by Redpoint Ventures. Langtrace's architectural foundation is OpenTelemetry: the SDK emits OTEL-standard traces that route to Langtrace's own dashboard or export directly to Grafana, Datadog, Honeycomb, Splunk, or any other OTEL-compatible backend without requiring a Langtrace API key. DevExplore readers evaluating Langtrace for enterprise use should verify the AGPL-3.0 license implications with their legal team, as it is the most restrictive open-source license in this category.
Key Capabilities
OpenTelemetry-native trace architecture: Every LLM call, vector database query, and framework operation emits OTEL-standard spans that travel to any compatible observability backend, making Langtrace the only LLM tracing tool in the Testing category where vendor lock-in is structurally impossible
Two-component deployment: A lightweight SDK installs into the application codebase and a separate web dashboard handles visualization, with either component usable independently depending on whether teams already have an OTEL-compatible backend
30+ integrations: Covers LLM providers including OpenAI, Anthropic, and Azure OpenAI; frameworks including LangChain and LlamaIndex; and vector databases including Pinecone and ChromaDB, with TypeScript and Python SDKs
Cost and latency tracking per call: Per-request cost, token usage, and latency metrics capture the full operational profile of each LLM API call across production traffic
Self-hosted deployment: The full platform deploys on-premise, keeping LLM inputs, outputs, and trace data within a team's own infrastructure rather than routing through Langtrace's cloud
LLM evaluations alongside tracing: Quality evaluation runs alongside observability instrumentation within the same platform, providing scoring metrics in the same interface as latency and cost data
Alternative tools
- MLflow
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- Opik by Comet
Trace, evaluate, and monitor LLM applications across the full development lifecycle
- Orq.ai
European enterprise AI agent platform with EU AI Act compliance and agent runtime orchestration.
- Klu.ai
Collaborative prompt engineering platform with multi-LLM evaluation and fine-tuning.
- Humanloop
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- Langflow
Visual drag-and-drop AI workflow builder with built-in MCP server deployment — now part of IBM.