c990802

When building our workflows system at https://easyverifactu.com using trigger.dev has given us a few hard sweats.

Sharing our story and learnings here!

I'd say that the main difference is that openwebui is not so focused on making the product "teams first". Yes, it has users, but access to data like documents uploaded for RAG are shared across all the users in the same workspace.

We are going in a direction where access to data is scoped with permissions. We do it via apps (same as "GPTs" in ChatGPT). As a user you upload documents to an app, and then have an advanced sharing system to control who has can access it.

We will be doubling down on making company documents and data accessible from the app, which we aren't sure it's a priority got Openwebui.

hope that answers your question!

Depends on usage, but the stack which is essentially Nodejs + Postgres is relatively low footprint. The most demanding workload is "next build", which you can do outside your server.

As a rule of thumb, you could start with the following setup:

if you run "next build" on the server: 2CPU / 4Gb RAM if you upload the built artifacts to the server: 1CPU / 2Gb RAM

The data we provide about savings is based on the actual usage of our early adopting companies. It includes data from marketers, legal counsels, developers and support agents.

So for example, your scenario of 20 questions a day for 30 days: - Monthly queries 20*30 = 600 queries/mo - Model gpt-4o ($2.5/M input, $10/M output) - Average input tokens 1,000 (actuals are much lower) - Average output tokens 400

=> Cost per run $0.0065 => Monthly cost $3.90

PS: True, We deliberately used "word" and not token, in some context, so that anyone can easily understand it.

Thanks for the question! I was kind of expecting it :)

It is indeed a rapidly evolving space and I think choosing LibreChat was a great choice. We only have good words for them!

In our case, we'd like to put our efforts on addressing specific needs of businesses & organizations that aren't always well covered by similar open source projects. Foundational things like granular permissions, advanced auth, usage reports, spend control and content moderation are top priorities in our roadmap. And then, we'd like to invest heavily in data integrations and RAG.

Finally, we'd like to make it extremely easy to self-host by making the deployment and upgrading process a breeze.

Hope this takes us to a place where people find value in our work!

Congrats on the launch. The project looks amazing.

I've built an open-source ChatGPT UI designed for team collaboration.

Github Link: https://github.com/joiahq/joia

Benefits vs the original: - Easy to invite entire teams and centralize billing - Talks to any Large Language Model (eg: Llama 2, Mixtral, Gemini) - Collaborative workspace to easily share GPTs within the team, similar to how Notion pages work - Savings of 50%-70% vs ChatGPT's monthly subscription

Tech stack: NextJS, Trpc and Postgres. All wonderful technologies that have helped me develop at the speed of thought.