Langfuse VS withOrbit.io
Compare Langfuse VS withOrbit.io and see what are their differences
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Langfuse
Helicone AI
LangSmith
LangChain
Braintrust.dev
Portkey
Openlayer
PromptLayer
withOrbit.io
Helicone AI
Intellize.ai
Overseer AI
Langfuse is an open-source LLM engineering platform designed to empower developers by providing insights into user interactions with their LLM applications. We offer tools that help developers understand usage patterns, diagnose issues, and improve application performance based on real user data. By integrating seamlessly into existing workflows, Langfuse streamlines the process of monitoring, debugging, and optimizing LLM applications. Our platform's robust documentation and active community support make it easy for developers to leverage Langfuse for enhancing their LLM projects efficiently. Whether you're troubleshooting interactions or iterating on new features, Langfuse is committed to simplifying your LLM development journey.
Orbit is an observability platform for AI-powered applications. It helps developers and teams track LLM costs, latency, and errors - broken down by feature, task, and customer.
Key Features:
Cost tracking by feature, not just monthly totals Latency and error monitoring per API call Task and customer attribution for agentic workflows One-line SDK integration (Node.js & Python) Works with OpenAI, Anthropic, and Gemini Real-time dashboard with usage analytics Use Cases:
Understand which AI features are most expensive Debug slow or failing LLM calls Attribute AI costs to specific customers or workflows Optimize prompts and reduce spend Pricing: Free tier available. No credit card required.
Langfuse withOrbit.ioNo features have been listed yet.
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withOrbit.io's answer:
Built by a Senior PM who kept seeing the same problem: AI features that looked fine but quietly burned margin. No tool answered "what part of my product is expensive?" So I built one.
withOrbit.io's answer:
Next.js, TypeScript, Supabase, and Node.js/Python SDKs for client instrumentation.
withOrbit.io's answer:
Currently in public beta with early adopters. Free tier available.
withOrbit.io's answer:
Orbit focuses on feature-level cost attribution, not just API logs. While other tools show you traces and totals, Orbit answers "which feature is burning my budget?" with a one-line SDK integration.
withOrbit.io's answer:
Simpler setup (one line of code), built-in cost tracking by feature/task/customer out of the box, and native support for agentic workflows where a single user action triggers multiple LLM calls.
withOrbit.io's answer:
Developers, FinOps, Product managers, and engineering teams shipping AI-powered features in production. Especially those using OpenAI, Anthropic, or Gemini who need visibility into what's driving their LLM costs.
Based on our record, Langfuse seems to be more popular. It has been mentiond 27 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Langfuse is the open-source standard for LLM observability. It traces every LLM interaction โ prompts, completions, latency, token usage, cost โ and provides the tooling to debug, evaluate, and optimize LLM applications in production. Think of it as "Datadog for LLM calls" with a focus on prompt engineering workflows. - Source: dev.to / 19 days ago
You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / 29 days ago
Gateway or proxy attribution. A reverse proxy in front of the model-provider API records the request, computes the cost, and exposes per-customer breakdowns. Open-source options include Helicone, LiteLLM, Langfuse, and OpenLLMetry. Hosted equivalents serve as the AI cost observability layer for teams that want centralized visibility: LangSmith, Datadog LLM Observability, Arize Phoenix. Adds a network hop.... - Source: dev.to / 30 days ago
Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline โ the metadata.userId pattern is the universal part. - Source: dev.to / about 1 month ago
Harness-level logging and traces. If you're running agents through an orchestration layer - LangChain, LangGraph, CrewAI, or similar - ship traces to an observability tool. Langfuse is a solid open-source option for LLM tracing: every tool call, every input/output, timestamped. That's your audit trail. You really appreciate when the investigation "what did the agent do and when?" takes less than a minute. - Source: dev.to / about 2 months ago
Helicone AI - Open-source LLM Observability for Developers
LangSmith - Build and deploy LLM applications with confidence
Intellize.ai - AI-first observability platform
LangChain - Framework for building applications with LLMs through composability
Overseer AI - Handle AI Governance with a Simple, Custom Policy-Driven API
Braintrust.dev - Rapidly ship AI without guesswork