Cloud architecture for data and model workloads
Landing zone
CI / CD
Observability
Controls
Business challenges
- AI experiments run on unmanaged environments that cannot go to production.
- Cost, identity, and network controls are added too late.
- Teams lack a repeatable path to deploy and observe models.
Our solution
Prompt Works AI designs infrastructure around the workloads you will actually run. We cover environments, identity, networking, deployment, and observability so AI systems can be operated like other critical software.
Capabilities
CI/CD for applications and model releases
GPU, inference, and cost-aware capacity planning
Identity, secrets, and network segmentation
Logging, tracing, and operational dashboards
Benefits
- A clearer path from experiment to production
- Stronger alignment with security and platform teams
- More predictable operating cost
- Repeatable environments across projects
Typical use cases
Landing zones for AI products
Model hosting and inference platforms
Secure data-science environments
Hybrid or multi-cloud deployment patterns
Implementation approach
Review current environments, constraints, and target workloads.
Define the platform baseline and guardrails.
Implement the first production path with observability.
Document operating practices for internal teams.