Best Alternatives to telemetry.dev in 2025

Observability and analytics for LLM apps

Best open-source alternative for teams that want self-hosting and data ownership. Langfuse offers tracing, prompt management, evals, and datasets with a permissive license and a large community. It is a strong pick if you need to run observability inside your own VPC or avoid vendor lock-in, though its agent-specific trace UX is less opinionated than telemetry.dev.

Best for teams already building on LangChain or LangGraph. LangSmith provides deep framework-native tracing, prompt versioning, dataset evals, and annotation queues, with tight integration into the LangChain ecosystem. Choose it if your stack is LangChain-centric and you want first-party support, but expect less flexibility if you use other orchestration frameworks.

Best open-source option for ML and LLM evaluation depth. Phoenix offers strong tracing plus robust eval and drift analysis, with OpenTelemetry-based instrumentation and a free self-hosted path. It suits teams that want rigorous offline and online evaluation alongside observability, though its agent-run visualization is less streamlined than telemetry.dev's single-trace view.

Best for fast, low-friction setup with a proxy-based approach. Helicone captures requests at the gateway level, giving instant logging, cost tracking, caching, and rate limiting with minimal code changes. It is ideal for teams that want quick visibility and cost controls without deep instrumentation, but it offers less structured agent and tool-call tracing than telemetry.dev.

Best for eval-first workflows. Braintrust combines logging, datasets, scoring, and prompt playgrounds with a strong emphasis on experimentation and CI-style evaluation. Teams that treat evals as the core of their quality process will appreciate its workflow, though its observability view is less focused on live agent run tracing than telemetry.dev.

Best for teams already using Weights & Biases. Weave adds LLM and agent tracing, evaluation, and monitoring inside the W&B platform, making it convenient if your ML workflows already live there. It is a solid choice for unified ML and LLM observability, but it is less specialized for agent-specific cost and error alerting than telemetry.dev.

Best for enterprises standardizing on Datadog. It brings LLM and agent traces into existing dashboards, alerts, and APM workflows, with strong governance and integrations. Choose it if operational consolidation matters more than specialized agent tooling, though it is heavier and pricier than telemetry.dev for pure AI-agent observability.