Coinbase has cut its AI spending by nearly half, and is attempting to set open-weight models including GLM 5.2 and Kimi 2.7 as default options.
2 hours ago
Coinbase CEO Brian Armstrong published a post stating that to sustain exponential growth in token usage while keeping AI spending stable, the key is not to introduce usage friction or spending alerts, but rather better default models, routing, and caching mechanisms. Coinbase is testing using open-weight models like GLM 5.2 and Kimi 2.7 as defaults via its LLM gateway, while still encouraging engineers to select the right model for each task. He noted that 91% of employees have never hit their usage caps, so instead of lowering quotas and adding alerts, the company shifted to lower-cost default models. For model routing, Coinbase preprocesses prompts in its custom workflow and routes tasks to the most suitable model based on cache hit rates and model pricing. For example, the planning phase may require an advanced model, but using an advanced model during execution would be overkill. The company believes that in the future, humans should not choose models—AI should handle this task automatically. Armstrong also said that cache misses are the easiest way to drive up costs. All of Coinbase’s requests are cache-aware to reuse hot caches as much as possible; for instance, after proper cache implementation, LibreChat’s cache hit rate rose from 5% to 60%. Additionally, Coinbase requires engineers to keep contexts streamlined, including starting new sessions when switching tasks, narrowing file context ranges, and disconnecting unused tools. The goal is not to curb AI usage, but to build infrastructure that can support exponential growth. Through these practices, Coinbase has cut its AI spending by nearly half, while token usage continues to grow.
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