Chatbase Reviews

1. christophepais

   about 3 years ago
   Bad work

   Chatbase Evaluation: Definitely, after many hours of uploading documents that I have been refining, it continues to respond with absurd or incorrect answers, and sometimes even claims to have no information when it had answered correctly just 10 minutes before. A significant waste of time and money.

Langfuse Reviews

Chatbase mentions (12)

• Show HN: Put ChatGPT on any website in 5 seconds. No signup required

  Congrats on the launch. How is it different from - https://chatbase.co/ https://sitegpt.ai/. - Source: Hacker News / almost 3 years ago

• ChatGPT trending down hard in a lot of places (although not in India)

  More and more people won’t use the base product directly, but downstream products such as https://chatbase.co, Microsoft Copilot integrated in all office applications, upcoming robots such as OpenAI Neo, etc. Source: about 3 years ago

• Paid AI to train on company docs?

  Chatbase.co is a good one. You can upload any text related file or pdf. Pretty user friendly and there's a lifetime deal on appsumo right now. Source: about 3 years ago

• Upload multiple pdfs and ask questions about these pdfs

  I know there are several services out there such as chatbase.co (https://www.chatbase.co/) What service do you use? Source: about 3 years ago

• What GPT/AI apps do you actually use? I'll share my favorites

  Chatbase.co is a web app where you can embed pdf-s and then ask anything about it. It's an awesome tool to create questions for my college lecture notes and find answers in lengthy documents to easy questions (e.g.: How many times can I take an exam for free?). There is a free version which is enough most of the time for these kinds of use cases. Source: about 3 years ago

Langfuse mentions (28)

• Strands Agents + Langfuse Evaluations

  In this project we will build a Python banking assistant agent using Strands Agents and make it observable and continuously evaluated using Langfuse — step by step. - Source: dev.to / 10 days ago

• Best AI Monitoring Tools in 2026: LLM, Agent, and MCP Observability Compared

  Langfuse is the open-source standard for LLM observability. It traces every LLM interaction — prompts, completions, latency, token usage, cost — 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 / 28 days ago

• What is an LLM evaluation harness? A deep dive into lm-eval-harness

  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

• How to track LLM costs per customer in production

  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

• Per-user cost attribution for your AI APP

  Same approach works with Langfuse, Phoenix, Braintrust, or your existing OTel pipeline — the metadata.userId pattern is the universal part. - Source: dev.to / about 2 months ago