Cerebras API access may be harder for small real-time agent teams
A small AI startup building a real-time needs very fast inference. Its target is steady throughput of about 1,000 to 2,000 , with low delay for most requests. The team wants Cerebras API access for fast ASIC inference in production, not a large group of H100 chips for .
It has been waiting for months. The concern is that a large OpenAI deal has reserved much of Cerebras’ near-term inference capacity for one major customer. For teams that are not large cloud-scale buyers, the API waitlist now feels effectively unreachable.
The frustration is also tied to the view that Cerebras is public but still seems to have little usable compute available for smaller customers.
Key points
- Real-time need low delay and high inference throughput.
- The stated target is about 1,000 to 2,000 .
- The team wants production ASIC inference, not training hardware.
- A large OpenAI-Cerebras deal may limit near-term API capacity for smaller customers.
- access can block agent products even before token cost becomes the main issue.