DevOps for AI companies

DevOps for AI companies that turns models into reliable products

A model working in a notebook is not yet a production service. Customers expect stable answers and predictable latency, while the business needs sustainable inference economics.

We build the layer between research and revenue: reproducible environments, controlled releases, elastic compute and visibility across model and backend behaviour.

Make AI environments reproducible

Docker images pin the application, framework, CUDA libraries and system dependencies across development, CI and production. Terraform provisions GPU and CPU pools, storage, networking and access as reviewed code.

Where justified, Kubernetes separates online inference, batch jobs and supporting APIs. NVIDIA GPU Operator standardises drivers, runtimes and device discovery. Scheduling rules stop costly GPUs being consumed by work that belongs on CPUs.

Release models without changing behaviour blindly

Each release links the model, application code, configuration and evaluation results. A registry such as MLflow connects with CI/CD so a version can be promoted, audited and restored.

Pipelines scan images, test APIs and apply model-specific gates: evaluation quality, latency, memory use and cost per request. Shadow traffic or canary deployment limits exposure. If errors, response time or quality signals degrade, traffic returns to the previous version.

Turn GPU spend into product economics

We benchmark batch size, concurrency, memory, throughput and cold starts before selecting hardware. Kubernetes autoscaling reacts to queue depth, request concurrency and inference-server metrics instead of CPU utilisation alone.

Separate pools keep live inference away from training and batch jobs. Scale-to-zero can suit asynchronous workloads; interruptible capacity can reduce cost when jobs support checkpointing. Dashboards connect GPU utilisation and spend with requests, tokens or completed jobs.

Monitor the service and model together

Prometheus, Grafana and NVIDIA DCGM track GPU memory, utilisation, queue time, throughput, time to first token, p95 latency and errors. OpenTelemetry traces requests through the API, retrieval layer, model server, database and external providers.

Model monitoring can include evaluation scores, input changes, fallback rate and product-defined quality signals. Alerts tell the team whether to scale, roll back or investigate data and dependencies.

Give researchers a paved path to production

Data scientists receive a documented route to package, evaluate and submit a model without managing servers. Least-privilege access separates datasets, credentials and artifacts by environment. Logs record changes without unnecessarily exposing prompts, customer data or secrets.

Your team receives target architecture, Terraform code, Docker builds, Kubernetes and GPU configuration, CI/CD and model-promotion workflows, dashboards, cost controls, rollback procedures and runbooks.

DevOps for AI companies helps models reach users faster, makes GPU burn measurable, reduces release risk and keeps production behaviour understandable to engineering and product teams.

AI workload infrastructure

GPU / CPU pools, batch and online workloads, storage layout and network throughput tuned to the actual model profile.

Model deployment

Reproducible model releases with clear versioning, canary rollouts and instant rollback.

CI/CD for AI products

Pipelines for backend services, model containers, evaluation runs and infrastructure changes.

Monitoring and logging

Service metrics, model-level metrics and structured logs — plus alerting on real user impact.

Cost-aware infrastructure

GPU scheduling, autoscaling and workload placement designed around the actual budget.

AI infrastructure flow

Development → Build → Test → Container Registry → Deployment → Inference / Backend Services → Monitoring → Logs → Alerts → Rollback.

No overengineering

AI companies need infrastructure based on real workloads, budget, team velocity and production requirements — not a copy of a hyperscaler blog post.