RAG vs Fine-Tuning: Choosing the Right LLM Approach for Your Product
Both RAG and fine-tuning improve LLM performance on your specific use case — but they solve different problems. Here's how to choose.
MLOps / DevOps
Docker
Containerization for consistent, portable AI and application deployments
50M+Images on Docker Hub
10×Faster than VM deploy
OCIOpen container standard
2013Container revolution began
HOW WE USE IT
Docker in our stack
Docker is foundational to every application we ship. Multi-stage Dockerfiles, Docker Compose for local development, and container security scanning are standard practice. We build lean, secure images that run identically in development and production.
CAPABILITIES
What we deliver
- Multi-stage Dockerfile optimization
- Docker Compose for local development environments
- Container image security scanning
- GitHub Actions + Docker CI/CD pipelines
- Private registry setup (ECR, GCR, ACR)
- Docker networking and volume management
USE CASES
How we apply Docker
ML Training Container
GPU-enabled Docker container for reproducible ML training with CUDA, all dependencies pinned.
Local Dev Environment
Docker Compose stack replacing dev server setup docs — one command launches the full application stack.
CI Build Pipeline
Multi-stage Docker build that runs tests, security scans, and produces a minimal production image.
Engineering Stack
Built with the tools that matter
38 production-grade technologies — every one battle-tested in shipped products.
Frequently Asked Questions
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Docker containers start in seconds (vs. minutes for VMs), share the host OS kernel for lower overhead, and produce reproducible build artifacts that eliminate environment inconsistency between development and production. For AI services, Docker ensures the exact same Python version, CUDA version, and package dependencies run in every environment. VMs are still preferred for strong workload isolation or when running multiple OS types on the same host.
A production Docker setup includes: multi-stage Dockerfiles that separate build and runtime images to minimize attack surface and image size, a private container registry (ECR, Artifact Registry, ACR) with vulnerability scanning, non-root user execution inside containers, health checks for orchestrator integration, and a CI/CD pipeline that builds, tests, scans, and pushes images on every merge to main. For AI workloads, we include CUDA base images optimized for the target GPU environment.
Containerizing an existing application and setting up a CI/CD pipeline to build and push Docker images typically takes 2-4 weeks. A full containerized deployment on ECS or Kubernetes including observability and security hardening takes 4-8 weeks. For complex multi-service applications with Docker Compose development environments, add 1-2 weeks for local development workflow setup.
FROM OUR CLIENTS
Built with teams who ship
The team took our AI concept from whiteboard to production in 10 weeks. The architecture they designed handles 10x our expected load with no issues.
Insights
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SERVICES THAT USE DOCKER
Related service offerings
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