Production Infrastructure for AI - From Fine-Tuning to Serving at Scale.
About building production ML/AI infrastructure - GPU cluster orchestration, LLM serving, ML pipelines, RAG architecture, vector databases, and cost-optimised GPU workloads on Kubernetes.
Engineering teams deploying AI/ML workloads to production who need reliable, scalable, cost-effective GPU infrastructure - not a data science notebook.
The Problem We Solve
⚠ Your ML models work in notebooks but deploying to production is manual, fragile, and takes weeks
⚠ GPU costs are spiralling because nobody is optimising scheduling, spot instances, or right-sizing
⚠ Your team is building RAG pipelines with duct tape - no proper vector database, chunking strategy, or retrieval evaluation
⚠ Model serving is unreliable - cold starts, OOM kills, and no autoscaling based on inference demand
⚠ You want to fine-tune LLMs or run open-source models but don't have the infrastructure expertise to do it efficiently
What's Included
✓ GPU cluster orchestration on Kubernetes - NVIDIA GPU Operator, node autoscaling, multi-GPU scheduling
✓ LLM serving infrastructure - vLLM, TGI (Text Generation Inference), Triton Inference Server, Ollama for dev
✓ ML pipeline design - Kubeflow Pipelines, MLflow, ZenML, or Metaflow for reproducible training workflows
✓ RAG infrastructure - vector database deployment (Qdrant, Weaviate, Pgvector, Pinecone), chunking strategies, retrieval evaluation
✓ Fine-tuning infrastructure - LoRA/QLoRA on your own data, distributed training across GPU nodes, experiment tracking
✓ Model registry and versioning - MLflow Model Registry, Weights & Biases, DVC for data versioning
✓ Cost optimisation - spot/preemptible GPU instances, Karpenter GPU-aware autoscaling, GPU sharing (MIG, MPS, time-slicing)
✓ Inference autoscaling - KEDA or Knative for scale-to-zero, request queuing, batching optimisation
Engagement Process
01
AI Infrastructure Assessment
Evaluate current ML workflow, GPU utilisation, serving architecture, and cost profile
02
Architecture Design
Design GPU cluster, serving infrastructure, pipeline architecture, and data flow
03
Build & Migrate
Deploy infrastructure, migrate models, implement pipelines, validate performance
04
Optimise & Scale
Cost optimisation, autoscaling tuning, monitoring dashboards, team training
Technology Stack
KubernetesNVIDIA GPU OperatorvLLMTGITritonOllamaKubeflowMLflowZenMLRayQdrantWeaviatePgvectorPineconePyTorchHugging FaceWeights & BiasesDVCKEDAKarpenter
Frequently Asked Questions
Can you help us run open-source LLMs instead of OpenAI?
Yes. We deploy and optimise Llama, Mistral, Mixtral, Gemma, and other open models on your own infrastructure. You control your data, avoid per-token costs, and can fine-tune for your specific use case.
Kubernetes or dedicated ML platforms like SageMaker?
Kubernetes gives you the most flexibility and avoids vendor lock-in. SageMaker/Vertex AI can be faster to start with but harder to customise. We help you choose based on your team's skills and long-term strategy.
How do you handle GPU cost optimisation?
Spot/preemptible instances for training (60-90% savings), GPU sharing for smaller models, scale-to-zero for inference endpoints, and right-sizing based on actual utilisation data. Most teams we work with reduce GPU costs by 40-60%.
Do you build the ML models themselves?
No. We build the infrastructure that your data science team uses to train, deploy, and serve models. We are infrastructure engineers, not data scientists. We make sure your models run reliably and cost-effectively in production.
What about RAG pipelines - is that infrastructure or application?
Both. We handle the infrastructure layer: vector database deployment, embedding pipeline, retrieval API, and scaling. Your team handles the application logic: prompt engineering, chunking strategy, and evaluation. We can advise on architecture patterns.
Ready to talk ai & ml infrastructure?
Book a free 30-minute architecture review. We'll assess your setup and give you an honest recommendation.