Solution brief
Batch & Analytics
When latency is flexible, batch inference slashes cost per token. OWS schedules large jobs across spare capacity and integrates with your data lake outputs.
What we deliver
- Economics — Spot-style and queue-based execution to minimize $/1M tokens for enrichment workloads.
- Data movement — Connectors for object storage; partitioned outputs for downstream analytics.
- Reliability — Retries, idempotency keys, and dead-letter handling for long pipelines.
- Scale — Back OWS PowerGrid to absorb peaks without over-provisioning baseline clusters.
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.