A fine-tuned model is only as good as the infrastructure running it. We build the production-grade foundation that holds everything together — hardware sizing, Kubernetes orchestration, inference serving, vector databases, observability, cost controls, and the security boundary around the whole stack.
What we build
GPU cluster design
Right-sized hardware for your workload — single H100 to multi-node A100 clusters. We don't oversell; we match iron to actual inference volume.
Inference serving
vLLM, TGI, or Triton deployed behind a load balancer with auto-scaling, request batching, and graceful degradation.
Vector & RAG stack
Qdrant, Weaviate, or pgvector for retrieval. Chunking strategies, embedding pipelines, reranking — all tuned to your corpus.
Observability
Token usage, latency percentiles, hallucination rates, cost per request, model drift — all in Grafana dashboards your team can read.
Guardrails & safety
Prompt injection defense, PII redaction, output filtering, rate limiting, jailbreak detection — before your model faces users.
Security & compliance
SSO, VPC isolation, encryption at rest and in flight, audit logs, role-based access. SOC 2 / HIPAA / ISO 27001 ready.
Deployment targets we support
- Bare metal / on-premise — your datacenter, your network, full isolation
- AWS — EC2 P5/P4d, SageMaker, Bedrock integration
- Azure — NC/ND series VMs, AKS, Azure OpenAI-hybrid patterns
- Google Cloud — A3/A2 instances, GKE, Vertex AI integration
- Hetzner / OVH / Lambda Labs — when cost matters more than enterprise contracts
- Edge devices — Jetson, Mac Mini M-series, NUC-class hardware for distributed inference
Cost optimization
A properly designed AI stack runs at 10–30% of the cost of using public APIs at scale, once you cross ~100M tokens/month. We do the math upfront — break-even analysis, 3-year TCO, and honest advice when it's cheaper to just keep paying OpenAI. We've told clients "don't self-host yet" more than once.
What handover looks like
We don't build a black box and walk away. Every deployment comes with Terraform/Ansible scripts, runbooks, architecture documentation, and a knowledge transfer session with your team. Your engineers can rebuild the entire stack from source control — because they already have the source and they already know how.