Cloud architecture & DevOps for AI.
Cloud architecture and DevOps that keep AI products fast, secure and affordable. MLOps pipelines, GPU inference and LLMOps on AWS, GCP or Azure, with costs you can forecast.
Infra that scales without surprises
AI workloads punish guesswork. Over-provisioned GPUs burn cash; under-provisioned ones stall users. We size infrastructure to the real workload, measure it, and keep it right.
What our cloud & DevOps team delivers
Cloud architecture and DevOps for the whole product, including the MLOps and LLMOps layer your models depend on. Every change is codified, reviewed and reversible.
- ✓Cloud architecture review & design
- ✓MLOps & LLMOps pipelines (training, eval, deploy)
- ✓GPU and model-serving infrastructure
- ✓Infra-as-code with Terraform / Pulumi
- ✓Kubernetes & container orchestration
- ✓CI/CD pipelines (GitHub Actions, ArgoCD)
- ✓Observability for apps and models
- ✓Cost optimization & right-sizing
- ✓On-call setup & runbooks
- ✓Security & compliance (SOC 2, HIPAA)
Boring infra, on purpose
We choose well-supported tools your next hire already knows. New tech goes in only where it pays for itself.
Audit
Architecture, spend, security, observability and model serving. A clear picture of where you actually are.
Plan
A prioritized roadmap with cost and benefit per item. We debate it with you until it holds up.
Migrate
Phased migration with cutover plans, rollback procedures, and no surprise downtime.
Operate
On-call, runbooks, monthly reviews, and a cost curve that grows slower than usage.
Three ways into cloud & MLOps work
Cost depends on what you run today, how much of it moves, and your compliance scope. The architecture review is the lowest-risk place to start.
Architecture review
A written review of your cloud and AI infrastructure with prioritized recommendations.
- Cloud audit
- Cost & GPU spend analysis
- Risk register
- Roadmap
- 60-min walkthrough
Cloud rebuild
Migrate or rebuild onto a clean foundation: IaC, CI/CD, MLOps, observability, on-call.
- Full IaC
- CI/CD + model pipelines
- Observability stack
- Runbooks
- Handover training
Operate & scale
We co-own your infrastructure. SLOs, on-call, and continuous cost optimization.
- Shared on-call
- SLO management
- Cost optimization
- Incident response
- Quarterly reviews
Cloud & MLOps, questions answered
What drives the cost of a cloud or MLOps project?
The size and age of what you run today, how much has to migrate, and your compliance scope (SOC 2, HIPAA). GPU and model-serving needs add to it. The architecture review prices each roadmap item, so you can phase the work.
How long does a cloud migration take?
It depends on the number of services and data stores involved. We migrate in phases, each with a cutover plan and rollback procedure, so production keeps running while the work happens. The phase plan is part of the review.
Do you run GPU inference and LLMOps for AI products?
Yes. We build model-serving infrastructure, MLOps and LLMOps pipelines for training, evaluation and deployment, and the observability to track latency, drift and cost per request. Capacity is sized to the real workload, then measured.
Which clouds and DevOps tools do you work with?
AWS, GCP and Azure, with Terraform or Pulumi for infrastructure as code, Kubernetes for orchestration, and GitHub Actions or ArgoCD for CI/CD. We pick tools your next hire already knows.
Not sure which engagement fits? Tell us what you’re building and we’ll reply within one business day with a recommended starting point.
Often paired with cloud & MLOps
Most engagements blend two or three disciplines. These are the most common pairings.
AI integration
LLM apps, AI agents and RAG systems built into your product, with evals, guardrails and cost controls from day one.
Custom web apps
Fast, AI-ready web applications in React and Next.js, built to scale with traffic, data and your team.
QA & LLM evals
Automated testing for your whole stack, plus LLM evaluation and red-teaming for the AI inside it.
Infrastructure decides AI margins and latency in SaaS and B2B AI solutions and fintech AI solutions. See HIPAA-grade AWS infrastructure in the prior-auth AI case study, and our writing on LLM cost and latency.
Ready to scale your AI stack?
We respond within one business day, with a real human and a real opinion.