Docker as the container platform
Docker changed how software is packaged and shipped, and a decade later containerization is the default operational model for modern software delivery. For Thoughtwave engagements — especially self-hosted AI deployments, enterprise backend services, and accelerator implementations — Docker is the packaging layer for nearly every component we ship.
How Thoughtwave uses Docker
Our Docker engagements cover:
- Containerized accelerator deployments — the TWSS Commercial Credit AI platform runs as a 14-service Docker Compose stack covering Postgres, Redis, MinIO/S3, Ollama-served models, orchestration services, and UI. Self-hosted AI deployments benefit enormously from the operational consistency containers bring.
- Docker Compose for local development and small-scale production deployments where full Kubernetes complexity is not warranted.
- Container image hardening for regulated deployments — distroless base images, vulnerability scanning, signed image provenance.
- Multi-stage build patterns for optimized production images that keep attack surface small.
- Docker-based CI/CD for consistent build-test-deploy pipelines across client environments.
- Bridging to Kubernetes for clients where scale and workload patterns justify the orchestrator upgrade.
For self-hosted AI deployments specifically, Docker Compose is often the right operational model for the initial production rollout — simpler to operate than Kubernetes, sufficient for most workload sizes, and a clean migration path to Kubernetes later when scale demands it.
Authentication and governance
Docker deployments integrate with the client's secrets-management infrastructure (Vault, Kubernetes Secrets, AWS Secrets Manager) and container registry (ECR, GHCR, Artifactory) authentication models. For regulated deployments, image signing, admission control, and runtime security (Falco, runtime scanning) align to the client's security posture.
When Docker is enough vs when Kubernetes wins
For small-to-medium self-hosted AI deployments, Docker Compose is typically sufficient and operationally simpler. For high-availability production workloads, multi-node scaling, or deployments where the operational team already runs Kubernetes for other workloads, Kubernetes is the right target. Our engagements choose based on the workload's actual operational requirements, not on architectural fashion.