Tabnine has been doing this for years longer than copilot with more users and better ratings. Not a monopoly (except that MSFT has so much marketing clout). The LLM space that we build on has gotten much more diverse after MSFT bought OpenAI and closed collaboration for many industry participants including ourselves. (We used to run on GPT2 but changed to an agnostic strategy that can use any LLM when MSFT would not let us use GPT3) Take a look at the new innovation with T5 from SFDC, AI21, Google, Meta all building powerful models.
Transparently we need your help in ensuring that this is not a monopoly.
Yes we at Tabnine have been at this for 7+ years and we believe that this is the current best solution. Only train on fully permissive code and then specialize those models privately for each team/org/org.
Does this change if the AI only uses fully permissive code? We took that approach at Tabnine and would be interested if the communities primary concern is disregard of licensing or the use of AI at all.
It looks like you used Tabnine local only model in the past (awesome for security, connectivity) if you used it in "cloud only" mode you would see much lower CPU and get longer snippets and completions. If you are with a company we are also happy to build and run all of this with custom models and in your own VPC or datacenter. Please contact us if you are interested and happy if you are also just an IC to get you Tabnine Pro.
Disclosure: I am from Tabnine.
Clarification - a user can run Tabnine on their laptop only OR as a part of Tabnine Enterprise we can run the large cloud models in your VPC (on GPU's) for your entire team of developers. Your code is your code and you can run it anywhere.
If you would like a custom model on your code (or language) this is one of the unique capabilities of Tabnine. We have done this on lots of code already and do it all the time for companies across the globe. Your code is your code. Transparently I am with Tabnine.
A model is only as god as the code that it trains on and the people it learns from. In your example above we can build a custom model on "best practices" code and not the companies codebase. Our work here has focused on working with ISV's like AWS, GCP, etc as they will have best practices for their tech. We then pull that together for a team in a custom model.
The code-search application is completely distinct from the IDE-assistant application. They do not share any code, have completely different pipelines for training the backend and completely different datasets. The specific source you mentioned will not be part of the training dataset for Tabnine's IDE assistant. Hope this clarifies.
Cost wise the biggest spend is building the large model (CODEX for Copilot). While CODEX is close sourced there are a number of other good open source transformer models from SFDC (T5), AI21, Meta that are the foundational models we use. Going forward we will continue to partner with the community to enable these large models to reach developers in code-time.