Revolut cuts AI coding costs 90% self-hosting

Revolut says it cut AI coding inference costs by roughly 90% by making self-hosted Z.ai GLM models, served on 56 Nvidia B200 GPUs, its default instead of Claude Code or Codex. Cheap flat-cost tokens let about 2,000 engineers use AI for debugging and Jira tickets, with frontier models kept as the exception.

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A large fintech reports about a 10x inference cost cut by defaulting to self-hosted open-weights GLM instead of commercial coding services such as Claude Code and Codex. This is a data point against frontier-lab token pricing power, and may pressure Anthropic and OpenAI coding revenue if other enterprises follow. Nvidia benefits from the on-prem hardware demand, since 56 B200 GPUs were deployed and the serving optimizations ran on NVFP4. Z.ai gains credibility for Chinese open-weights models in regulated Western enterprises, though data-residency concerns are mitigated only by on-prem or regional hosting. The model-router pattern, with vision tasks sent to Gemini or GPT, suggests a hybrid enterprise architecture with frontier models used selectively.