Description
telemetry.dev provides comprehensive LLM observability for AI applications and agents, leveraging the OpenTelemetry standard. The platform allows developers to trace and monitor every aspect of their AI operations, from individual model calls to complex agent runs. A key feature is the ability to group instrumented model calls, tool steps, and retrievals into a single trace, offering a holistic view of a request or agent's execution.
Read More
Users can inspect critical metrics such as token usage, cost, latency, and errors, which are essential for debugging failures, optimizing performance, and managing operational spend. The platform calculates costs at ingest based on reported token usage and its model-pricing catalog, with explicit span costs taking precedence when available. It supports first-party SDKs for TypeScript and Python, along with integrations for popular providers and frameworks like OpenAI, Anthropic, Vercel AI SDK, LangChain, and CrewAI. Other languages can connect by sending standard OTLP over HTTP.
telemetry.dev emphasizes privacy and control, allowing users to disable prompt and response capture per environment before storage. Built-in and project-specific redaction patterns are also applied to help manage sensitive data. The platform offers a free plan that includes 30,000 ingestion units per month, 14-day retention, one project, and two seats, requiring no credit card. Paid plans offer increased ingestion units, longer data retention, more projects, and additional seats, with no automatic overage charges.
Beyond basic tracing, telemetry.dev provides features like MCP analytics, alerts for cost and errors, prompt management with versions and rollouts, and evaluation tracing. It helps pinpoint slow steps, inspect failed calls, and compare spend, latency, and errors across models, providers, and environments. Users can search and filter traces by name, status, environment, framework, or session, ensuring they can quickly find the data they need to maintain reliable and cost-effective AI applications.
- CategoryMLOps & Model Deployment
- Pricing ModelFreemium
- DeploymentCloud
- PlatformsWeb
telemetry.dev's Core Features
LLM Observability for AI Agents
OpenTelemetry-based Tracing
Inspect Token Usage, Cost, Latency, and Errors
First-Party SDKs for TypeScript and Python
Integrations with OpenAI, Anthropic, Vercel AI SDK, LangChain
Support for Standard OTLP/HTTP Exporters
Cost Calculation from Token Usage and Model Pricing
Prompt and Response Capture Control
Built-in and Project-Specific Redaction Patterns
Email Alerts for Cost, Budgets, Agent Loops, and Errors
MCP Analytics for AI Agents
Prompt Management with Versions and Rollouts
Evaluation Tracing for AI Runs
Search and Filter Traces by Various Criteria
Free Plan Available with 30,000 Ingestion Units
How to use telemetry.dev?
Create API Key: Generate a project API key within telemetry.dev.
Configure Integration: Set up a telemetry.dev integration for your SDK, provider, or framework, or configure an OTLP/HTTP exporter.
Instrument Application: Integrate the SDK or OpenTelemetry into your AI application to send traces.
Run Instrumented Request: Execute an AI request or agent run with the instrumentation enabled.
Inspect Traces: Open the generated trace in your telemetry.dev project to analyze model calls, tool steps, usage, and errors.
Monitor and Alert: Set up alerts for cost, budgets, agent loops, and model or tool errors to proactively manage your AI applications.
telemetry.dev's Use Cases
- Debugging AI Failures
- Monitoring AI Spend
- Performance Optimization
- Ensuring AI Reliability
- Proactive Alerting
- Sensitive Data Management
- AI Agent Development
- Model and Provider Comparison
