Show HN: Llama Workspace – An Open Source ChatGPT Teams Alternative

Show HN: Llama Workspace – An Open Source ChatGPT Teams Alternative(llamaworkspace.ai)

29 points by c990802 1 year ago | 8 comments

Hey HN! We are happy to share with you our latest project: Llama Workspace (https://llamaworkspace.ai).

It's an open-source ChatGPT Teams alternative made with the needs of businesses and organizations in mind.

It has everything you can expect from ChatGPT, plus first class support for users and roles management, as well as advanced collaboration features. We've made it very easy to self-host anywhere you like, and we also provide a cloud version. So no excuse for trying it out!

Why using Llama Workspace instead of ChatGPT Teams?

- You have access to all the major Large Language models in one place, including GPT-4, Claude Sonnet or Gemini. - It brings savings of about 70% - 85% when compared to ChatGPT Teams (see website for more info on this). - You can integrate it with your own code, like AI agents, and provide a single platform to access AI.

Here's the Github link: https://github.com/llamaworkspace/llamaworkspace

I would love to hear your ideas, experience, and feedback about the product! What should we implement next?

ganeshkrishnan 1 year ago |

>Llama Workspace connects to AI providers like OpenAI and Anthropic via API. In the API, each word for the most commonly used model (GPT-4o at the time of writing) costs approximately $0.000025. Therefore, to break even with the ChatGPT Teams license, which costs $30/seat/month, you would need to generate 1,200,000 words.

I would debate this one. As a "normal" user of chatgpt with around 10-20 questions a day, I can exceed this $30 per month because

1) it's not a word per se but token. A lot of the words are tokenized.

2) for each chat, for each question all the context above is re-sent and counts for cost.

I think pivoting towards self hosting llama models and charging for them might be more profitable to you and to the users.

c990802 1 year ago | |

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.

aaronharnly 1 year ago |

Thank you and congratulations!

How (in your opinion) does this project compare to https://www.librechat.ai?

(My company selected LibreChat to host our internal “GPT Teams” implementation after surveying and evaluating the current state of tools as of about six months ago, but I’m sure it’s a rapidly evolving space.)

c990802 1 year ago | |

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!

janderson215 1 year ago |

Very cool!

What is the recommended resource usage for self-hosted? I don’t see it in that section on your site.

c990802 1 year ago | |

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

vanillax 1 year ago |

What does this do that ollama webUI cant do? Just curious

c990802 1 year ago | |

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!