MLOps and AI Infrastructure is an ongoing engagement, usually starting at 4 to 8 weeks, for deploying models, watching latency and drift, and keeping token cost under control. It includes deployment pipelines, vector database tuning, alerting, and infrastructure as code.
Included
- CI/CD pipelines for model and prompt deployment
- Vector database architecture and retrieval tuning
- Cost monitoring and token-usage optimization
- Latency and drift monitoring with alerting
- Infrastructure-as-code for reproducible environments
38%
Cost and observability overhaul cut AI infrastructure spend 38%
from our workCost reduction
47%
Multi-model routing cut a media platform's inference bill by 47%
from our workLower inference spend
52%
Self-hosted open-weight model serving cut per-request cost by 52%
from our workLower cost per request