
Langfuse
Helicone AI
LangChain
Portkey
Braintrust
Humanloop
Braintrust.dev
Build and deploy LLM applications with confidence

AIBASEDTOOLS
LLMWise
OpenAI
LMArena rankings × OpenRouter pricing. Find your model, then get emailed when a cheaper, stronger one appears.

Which is more popular?
Based on our record, gptbased seems to be more popular. It has been mentioned 1 time since March 2021.
Website, pricing, platforms and company facts side by side.
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| Website | langchain.com | gptbased.com |
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What each product offers, as listed by its team.


Possible disadvantages
Possible disadvantages
An editorial look at what each product does well and who it suits.


Overall verdict
Why this product is good
Recommended for
LangSmith is recommended for AI developers, machine learning engineers, and businesses aiming to build, test, and optimize applications based on language models. It is particularly useful for teams that require robust evaluation tools and a streamlined process for managing and deploying language-driven applications.
Overall verdict
Why this product is good
Recommended for
Walkthroughs and reviews on video.
🦜🛠️ Getting started with LangSmith - Integrating with LANGCHAIN powered Web Applications & Chatbots
No gptbased videos yet. You could help us improve this page by suggesting one.
How often each product is chosen within a category, 0–100% relative to the other.


Share your experience with using LangSmith and gptbased. For example, how are they different and which one is better?
Recommendations tracked on public social media and blogs since March 2021.


Tracking LangSmith since Jul 2023.
I think GLM 5.2 having a higher ELO than Opus 4.8 shows that they did it [https://gptbased.com]. - Source: Hacker News / 3 months ago
When comparing LangSmith and gptbased, you can also consider the following products.

Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.
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Open-source LLM Observability for Developers
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Use GPT, Claude, Gemini, and more from one account. No separate model subscriptions required to start.
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Framework for building applications with LLMs through composability
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