70%
Lower inference cost
Through batching and autoscaling
p95 180ms
Response latency
Down from 2.4 seconds
2M+
Users served
Scaled without infrastructure rewrites
The challenge
Cognixa's prototype worked on a single GPU but was too slow and expensive to serve. Latency spiked under load, costs were unpredictable, and there was no separation between training, inference and evaluation environments.
How we fixed it
We built a GPU infrastructure with autoscaling spot pools for training and reserved capacity for inference, deployed vector databases for RAG, implemented MCP servers for tool use, and added cost guardrails and observability on every request.
AILLMGPURAG