Choosing Between DeepSeek, Qwen, Kimi, and GLM at Scale

The useful part

Choosing Between DeepSeek, Qwen, Kimi, and GLM at Scale Six months ago, my cloud bill was scaring me. We were running everything through a single Western LLM provider, and the cost of inference was eating our runway alive. That's when I started digging seriously into the Chinese model ecosystem — not as a political statement, but as a CTO who needs to survive another quarter.

How it works

• I spent the last few months running real workloads through all four families, mostly through Global API's unified endpoint so I could swap models without rewriting my integration code.
• Every prompt you send, every agent you build, every fine-tuning dataset you curate — all of it gets baked into a particular API shape, a particular billing model, a particular set of rate limits.
• Switch providers later and you're looking at weeks of migration work, not hours.
• Some of them are production-ready in ways that would make a venture capitalist weep with joy.
• Pricing is wildly inconsistent across the board, which is exactly the kind of thing that makes vendor lock-in dangerous.

What to take from it

Because all four families I've tested expose OpenAI-compatible APIs, the switching cost is dramatically lower than it was a year ago.</p... If you're trying to figure out which one belongs in your stack, here's what I learned — including the bills. Here's the thing nobody tells you in the early days: your LLM provider decision compounds.

Example or evidence

• The Chinese model ecosystem gives us something we desperately need: a credible second, third, and fourth option.

Details worth keeping

DeepSeek, Qwen, Kimi, and GLM aren't just "cheap alternatives" anymore. That's vendor lock-in, and it's the silent killer of startup ROI.