
pgModeler
DbSchema
erwin Data Modeler
Toad Data Modeler
ER/Studio
SQL Developer Data Modeler
SQL Database Modeler
Moon Modeler
Langfuse
Helicone AI
LangSmith
LangChain
Openlayer
Braintrust.dev
Portkey
PromptLayer
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.
pgModeler
LangfuseBased on our record, Langfuse should be more popular than pgModeler. It has been mentiond 28 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.
I wonder how this compares to pgModeler (https://pgmodeler.io/) which I've been using the most in the recent years, would love is someone who had tried both could share some observations. - Source: Hacker News / about 1 year ago
I usually go with the FOSS https://pgmodeler.io Its feature-rich, and its ability to compare database schemas makes updating and applying diffs much easier. - Source: Hacker News / about 1 year ago
Co-creator of Trek here. Trek generated migration files based on the diff between a pgModeler(1) schema definition and existing migration files. Trek also helps deploying those migrations. I'd be happy to respond to any questions here :) 1) https://pgmodeler.io/. - Source: Hacker News / over 2 years ago
PgModeler is an open source tool that does diagramming as well as database management, including asking if you want to cascade when trying to drop tables. UI is a big quirky but once you get used to it, itโs very nice. I swear by it. https://pgmodeler.io. Source: almost 4 years ago
Here is the one I have used in the past, https://pgmodeler.io/. Source: about 4 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 / 2 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 / 21 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 / 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 1 month ago
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