You're getting downvoted but what you said is true. The cost of self-hosting (and achieving +70 tok/sec consistently across the entire context window) has never been low enough to justify open source as a viable competitor to proprietary models of OpenAI, Google, and Anthropic.

That's true for Flash 2.0 at $0.40/mtok output. GPT-4.1-nano is the same price and also surprisingly capable. I can spend real money with 2.5 flash, with those $3.50/mtok thinking tokens, worth it though. OP is an inference provider, so there may be some bias. Open source can't compete on context length either, nothing touches 2.5 flash for the price with long context--I've experimented with this a lot for my agentic pricing system. Open source models are improving, but they aren't really any cheaper right now, R1 for example does quite well performance wise, but it uses a LOT of tokens to get there, further limiting the shorter context window. There's still value in the open source models, each model has unique strengths and they're advancing quickly, but the frontier labs are moving fast too and have very compelling "workhorse" offers.

With tools like Ollama, self-hosting is easier than hosted. No sign-up, no API keys, no permission to spend money, no worries about data security, just an easy install then import a Python library. Qwen2.5-VL 7B is proving useful even on a work laptop with insufficient VRAM - I just leave it running over a night or weekend and it's saving me dozens of hours of work (that I then get to spend on other higher-value work).

  Implement a JavaScript 2048 game that exists as flat file(s) and does not require a server, just the game HTML, CSS, and js. Make it compatible with firefox, at least.

You'd be surprised how often people in enterprise can be left waiting months to get an API key approved for an LLM provider.

There is a wide range of opinions on what should be considered sensitive data. Many people would classify a vast majority of their data as sensitive.