LocalLLaMA

From an idealistic point of view, you can implement function calling easily in your team. Use the context free grammar plugins that are now available to ensure that the LLM outputs match your function calling format. Then build your own dataset on your typical workloads and prepare a pipeline to finetune new models on it.
As open-source models will continually improve, you can use that pipeline to fine tune for your task for a few bucks on a few cloud GPUs. You should be prepared to switch from model to model and handle your fine tuning in your team. That way you will be able to keep up with the cutting edge (of open source) and still have full control. You can allways chose that a model is good enough and keep using it forever.

From a serious business point of view: You are in serious trouble because you relied on a single, very hard to replace core service for your whole startup. Don't make that mistake again. First and foremost, make sure that your backend becomes flexible enough to switch the LLM service provide on short notice. Then, you will probably want to integrate support for MS azure's version of GPT3.5. MS appears to have access to all models up to at least GPT4 and moreover appears to have a commercial licence on that. So basically MS provides you with a perfect drop in solution.

You might still want to persue the open-source route, because it gives you full control over your core service. Depending on the size of your startup, you probably should implement at least two separate solutions to the threat of OpenAI shutting down.

Then again, it's entirely possible that OpenAI services will keep operating. The situation is still completely fluid. But I guess MS is your best bet, particularly if the whole team actually migrates to MS.