Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
LastMile AI
Embeddable
Luzmo
Metabase
Looker
Vizzly
POWr
Sisense
Tableau
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.
Build Remarkable Analytics Experiences. No more 'Build vs. Buy'. Embeddable is the embedded analytics tool where you own the front-end code and we handle everything else. Now you can build fully-bespoke, fast-loading charts and dashboards in your app without the engineering costs. Delight your customers, reduce engineering overheads, and deliver your dream experience, fast. Compatible with all major databases. Cloud & Self-hosted. Multi-tenancy. Open source component library + more
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Embeddable's answer:
Software companies who care about the UX and loading speed of their customer-facing analytics.
Embeddable's answer:
Get the best of 'Build vs. Buy' in one stack-agnostic solution. Embeddable gives you full control over the frontend of your analytics experience, and handles the backend for you. No longer do you have to choose between a limited out-of-the-box solution, or building everything from scratch.
Embeddable's answer:
Embeddable is from the team behind Trevor.io -- a popular internal BI tool which also allows you to embed dashboards into your app. We realised embedding dashboards from a BI tool into your app wasn't the 'dream solution', and building analytics from scratch was super expensive... so we built Embeddable from the ground up to enable teams to deliver fully-bespoke, highly-performant analytics in their apps for their customers in 10% of the time.
Embeddable's answer:
Embeddable's answer:
If you want full control over the UX of your customer-facing analytics experience, but don't want to invest months of developer time on building and maintaining a fully-custom build -- OR -- if you're using an embedded analytics too already that loads slowly and doesn't look and feel like the rest of your platform.
Based on our record, Langfuse seems to be a lot more popular than Embeddable. While we know about 28 links to Langfuse, we've tracked only 2 mentions of Embeddable. 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.
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 / 13 days 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 1 month 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 / about 1 month 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 / about 1 month 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 2 months ago
Then comes data modeling. BI tools such as Embeddable need to know how different tables and fields relate to each other. Someone has to define what terms like โtop customerโ or โQ3 revenueโ actually mean. Without this, the AI won't know where to look or how to answer even basic questions. - Source: dev.to / about 1 year ago
Itโs still pretty new but build by an experienced team. Itโs commercial software though. https://embeddable.com/. - Source: Hacker News / over 2 years ago
Helicone AI - Open-source LLM Observability for Developers
Luzmo - From data to decisions, damn fast. Embed beautiful, easy-to-use dashboards in your SaaS product in days, not months.
LangSmith - Build and deploy LLM applications with confidence
Metabase - Metabase is the easy, open source way for everyone in your company to ask questions and learn from...
LangChain - Framework for building applications with LLMs through composability
Looker - Looker makes it easy for analysts to create and curate custom data experiencesโso everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.