Docker & Containerization
Containers are settled technology — which is exactly why nobody reviews the Dockerfile anymore, and why we keep finding 4GB images running as root, built from unpinned base layers, carrying a package manager, three shells, and a security finding for every sprint. The foundation everyone stopped looking at is still the foundation.
We keep it disciplined: multi-stage builds that ship the application and nothing else, non-root runtimes, pinned and scanned base images, SBOMs your security team can actually consume, and build pipelines fast enough that engineers stop resenting them. It's the ground floor of the platform work we do everywhere else — and for AI workloads with model weights and GPU runtimes, image discipline pays twice.
Image engineering
Multi-stage, minimal, non-root images — smaller attack surface, faster pulls, cleaner scans.
Supply-chain security
Pinned bases, vulnerability scanning, signing, and SBOMs wired into CI.
Build performance
Layer caching and build architecture that turn ten-minute builds into one-minute builds.
AI workload containers
GPU runtimes, model weights, and inference servers packaged for fast cold starts.
Dev-prod parity
Compose and devcontainer setups where "works on my machine" means it works in prod.
Why do our images keep failing security scans?
Almost always inheritance: bloated base images carrying hundreds of packages nobody uses. Moving to slim or distroless bases, pinning digests, and rebuilding on a schedule eliminates most findings structurally instead of whack-a-mole patching them.
Our Docker builds take forever. Fixable?
Nearly always, and dramatically. Proper layer ordering, cache mounts, and multi-stage separation of build and runtime typically cut build times by 5–10x. Slow builds are a compounding tax on every engineer, every day — among the highest-ROI fixes we do.
What's different about containerizing AI workloads?
Size and startup. Model weights and CUDA runtimes balloon images to tens of gigabytes, which wrecks autoscaling cold starts. We separate weights from images, stream or mount models at start, and tune for the scale-from-zero behavior inference traffic demands.
Put Docker & Containerization to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
Book a deployment →