Inference & Deployment
Ship models to users with predictable latency and cost. We design serving tiers for real-time, streaming, and bursty traffic, backed by observability and SLO-driven autoscaling.
What we deliver
- Serving patterns — Dedicated replicas, serverless-style scale-to-zero, and queue-based workers for heavy jobs.
- Global footprint — Place inference close to users across 60+ regions; optional edge for ultra-low latency.
- Model routing — OWS Forge aggregates external and self-hosted models with a single API surface and usage controls.
- Optimization — Quantization, batching, KV-cache tuning, and hardware-specific runtimes where it matters.
Typical engagement
- 1Discovery — workload profile, SLOs, data residency, and budget.
- 2Architecture — cluster topology, APIs, and integration points.
- 3Pilot — limited production or benchmark phase with clear exit criteria.
- 4Scale — hardening, FinOps, and continuous optimization.
Architecture & security
Designs are adapted per customer: VPC-style isolation, encryption in transit and at rest, secrets management, and least-privilege access to control planes. We document data flows for security review and support private connectivity options where required.
Success metrics
We align on measurable outcomes — training throughput (tokens or samples per dollar), inference p99 latency, cost per 1M tokens, job completion rates, and uptime against agreed SLOs. Dashboards and monthly reviews keep both teams honest.
Related products
This solution composes OWS products. Your team can start from any layer and expand.