LaunchDarkly
ConfigCat
Optimizely
Flagsmith
Unleash
Split.io
PractiTest
Google Marketing Platform
Langfuse
Helicone AI
LangSmith
LangChain
PromptLayer
Braintrust.dev
Openlayer
Portkey
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.
LaunchDarkly LangfuseLaunchDarkly might be a bit more popular than Langfuse. We know about 39 links to it since March 2021 and only 28 links to Langfuse. 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.
This is a realistic scenario, because Claude, ChatGPT, and Gemini all recommend LaunchDarkly. But when you ask these questions of your agent, the response comes from a single model that was asked just once. Itโs subject to the same training bias and nondeterminism as any prompt. In my research, the tool recommendations can vary considerably. - Source: dev.to / about 1 month ago
One common runtime control is a feature flag, which is a configurable switch that changes application behavior without requiring a redeploy. In ML systems, feature flags can be used to route users between model versions, limit exposure to selected cohorts, or revert quickly to a known-safe model when problems appear. Tools such as LaunchDarkly provide this kind of runtime control. - Source: dev.to / 2 months ago
This kind of goes without saying since it's the opposite of the first don't I listed, but it's worth restating and giving some examples. Using tools from third parties means taking advantage of what they have done so you don't have to do that work. This means you are free to build things that make your app special. I like to use feature flag tools for this. Some examples are LaunchDarkly, Split, and AWS App... - Source: dev.to / about 2 years ago
Taplytics is a broad A/B testing platform for marketing teams. While DevCycle is a feature flagging tool built for developers. Taplytics actually has feature flagging, but DevCycle is much more focused and plans to compete directly with incumbents like LaunchDarkly by building a better developer experience (more on how later). But with Taplytics they built so many features and every customer was using them in a... - Source: dev.to / over 2 years ago
I had a custom rule added to Little Snitch that blocked the following domains: launchdarkly.com, clientstream.launchdarkly.com, mobile.launchdarkly.com. Source: over 2 years ago
In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse โ step by step. - Source: dev.to / about 1 month ago
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 / about 2 months 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 / 2 months 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 / 2 months 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 / 3 months ago
ConfigCat - ConfigCat is a developer-centric feature flag service with unlimited team size, awesome support, and a reasonable price tag.
Helicone AI - Open-source LLM Observability for Developers
Optimizely - A/B testing you'll actually use.
LangSmith - Build and deploy LLM applications with confidence
Flagsmith - Flagsmith lets you manage feature flags and remote config across web, mobile and server side applications. Deliver true Continuous Integration. Get builds out faster. Control who has access to new features. We're Open Source.
LangChain - Framework for building applications with LLMs through composability
