DevOps for AI Startups
AI / LLM App DevOps & Infrastructure
Infrastructure for AI and LLM applications, GPU provisioning, model deployment, and inference infrastructure, approached with the same DevOps discipline as any other production system.
When AI/LLM infrastructure needs DevOps discipline
- GPU infrastructure is expensive and hard to right-size.
- Model deployment is manual, or handled ad hoc outside your normal release process.
- Inference costs and latency aren't well understood.
- There's no monitoring specific to model performance or usage.
AI and LLM infrastructure still needs the fundamentals, automation, monitoring, and cost control, just applied to a newer kind of workload.
What this covers
LLM app deployment
Deploy LLM-powered applications with a real release process, not a manual one.
Learn more →GPU infrastructure
Provision and right-size GPU infrastructure for training or inference.
Learn more →Inference infrastructure
Infrastructure built around inference latency and cost, not just raw compute.
Learn more →MLOps setup
CI/CD and operational practices applied to model deployment.
Learn more →Vector databases
Infrastructure for vector databases used in retrieval-augmented applications.
Autoscaling for AI workloads
Scale inference capacity with demand instead of over-provisioning.
Cost & monitoring
Visibility into GPU spend and model performance, not just application metrics.
Ways to work with us
Not sure which fits? Tell us the problem on a free call and we'll recommend one.
Fixed-scope project
A defined setup with a clear deliverable and timeline.
Managed support (retainer)
We keep your infrastructure healthy month to month.
Hourly / as-needed
Short, specific tasks without a long commitment.
Dedicated engineer
An engineer from our team focused on your account.
White-label for agencies
We deliver DevOps under your brand for your clients.
Tools we work with
- AWS
- Azure
- GCP
- Docker
- Kubernetes
- Terraform
- Jenkins
- GitHub Actions
- GitLab CI
- Prometheus
- Grafana
- Datadog
- Ansible
- Helm
- Linux
What you actually receive
- AI/LLM infrastructure architecture
- GPU provisioning setup
- Model deployment pipeline
- Monitoring for inference & cost
- Documentation & runbooks
- Ongoing support plan
Exactly which of these you get depends on the engagement, we scope it on the call.
What changes for your business
- A repeatable model deployment process
- Better visibility into GPU and inference costs
- Infrastructure that scales with demand
- The same DevOps discipline applied to AI workloads as the rest of your stack
DevOps performance is commonly measured with DORA metrics, deployment frequency, lead time for changes, change failure rate, and time to restore service.
What clients say
Case studies coming soon.
Real client testimonial goes here once we have permission to publish it.
Name, role, Company
Real client testimonial goes here once we have permission to publish it.
Name, role, Company
Real client testimonial goes here once we have permission to publish it.
Name, role, Company
Questions about AI/LLM DevOps
This is an emerging area of our work, the underlying DevOps practices (CI/CD, infrastructure as code, monitoring, cost control) are the same disciplines we apply elsewhere, adapted to AI/LLM workloads.
Specific AI/LLM project history and credentials are confirmed on request, we don't claim case studies that aren't real.
Yes, right-sizing and cost visibility for GPU infrastructure is part of this engagement.
Both, from the deployment pipeline itself to the infrastructure it runs on.
Yes, as part of the infrastructure for retrieval-augmented applications.
Yes. We sign an NDA before any work starts, and you own everything we build for you.