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Anthropic is reportedly facing significant challenges related to compute capacity and rate limits on its AI models, particularly its Claude model. Concerns have been raised about the company's ability to meet growing demand, with users noting that the API usage has surged, leading to frequent rate limitations. Observers have pointed out that Anthropic's Sonnet model is being widely adopted across various applications, contributing to the strain on its resources. Comparatively, other AI labs do not seem to experience similar issues with capacity or rate limits. The situation raises questions about whether Anthropic's existing partnerships, such as those with AWS, will be sufficient to alleviate these constraints.
the reason Anthropic is often at capacity is that they have the best universally useful model, specifically for fast back and forth codegen which uses tons of tokens t/s on glif for Claude also noticably faster in CET mornings vs afternoons when US users come online
Anthropic is constantly rate-limited because its API usage has EXPLODED. All the code editors and several agentic systems use Sonnet as their primary model. o1 is a bit too slow even though it's a better model, and GPT-4o has deteriorated and fallen behind Sonnet The new…
Anthropic seems like the only big lab which is perpetually running out of inference compute. I don't hear the same complaints about rate limits and capacity crunches about anyone else. do they really have that much less? maybe sonnet is a much bigger model than 4o.
