Cloud & MLOps Architecture

AI infra that scales without burning cash

Cost-aware, secure, multi-cloud AI infrastructure — the foundation your AI product will live on. Typical timeline: 4–10 weeks. Delivered by senior engineers from Bhubaneswar, Odisha to clients worldwide.

What we cover

• Reference Architecture
• GPU / Inference Optimization
• CI/CD for ML
• Observability
• FinOps

What you get

• Cloud landing zone
• Inference optimization
• MLOps pipelines
• Cost & latency dashboards

What we build with

• AWS
• GCP
• Azure
• Terraform
• Kubernetes
• Ray
• Triton

How an engagement looks

1. Ideate: Problem framing, user research and AI opportunity mapping.
2. Validate: Technical feasibility, data audit, POC and risk de-risking.
3. Architect: System design, model choice, infra blueprint and evals plan.
4. Build: Senior pod ships in weekly increments with demos and tests.
5. Deploy: Cloud deployment, CI/CD, guardrails and observability.
6. Scale: Cost, latency and quality optimization as usage grows.

Cloud & MLOps Architecture — FAQ

How do you keep AI infrastructure costs under control?

We design cost-aware, multi-cloud AI infrastructure with GPU and inference optimization, caching, and FinOps dashboards for cost and latency — often cutting per-query cost 40–60% without changing the model.

Do you set up CI/CD and observability for ML?

Yes. We deliver a cloud landing zone, MLOps pipelines (CI/CD for models), inference optimization and full observability so your AI product is deployable, monitored and safe to iterate on.

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Contact ThoughtCell Global: email [email protected] · LinkedIn linkedin.com/company/thoughtcell-global. Headquartered in Bhubaneswar, Odisha, India · serving clients worldwide.