AI Infrastructure
Infrastructure for AI-native teams — model hosting, vector databases, and MLOps pipelines.
Purpose-built infrastructure for AI-native products — covering model hosting, vector database architecture, MLOps pipelines, and MCP server management. Designed for teams that need their AI systems to be fast, reliable, and cost-controlled under real production workloads.
What's Included
- MLOps pipeline setup and management
- LLM deployment infrastructure (self-hosted and API-based)
- GPU cluster provisioning
- Vector database setup (Pinecone, Weaviate, pgvector)
- RAG and Agentic RAG pipeline infrastructure
- MCP server setup and management
- AI API gateway and rate limiting
- Model versioning and experiment tracking
- Cost and performance monitoring for AI workloads
Tools & Technologies
- n8n
- MCP Servers
- Vector Databases
- ML Pipeline Monitoring
- GPU Compute
- Supabase
- Docker
- Kubernetes
Who This Is For
Startups building AI products who need reliable, scalable, and secure infrastructure for LLM and ML systems — from early prototypes through production scale.
Frequently Asked Questions
- Should we self-host our LLM or use a managed API provider?
- It depends on your data sensitivity, usage volume, and budget. Managed APIs (OpenAI, Anthropic) are faster to start, require no infrastructure management, and are best for variable or early-stage workloads. Self-hosted models offer full data control and predictable cost at scale — critical for healthcare or financial data. We help you model both options and design the right approach for your use case.
- What is a RAG pipeline and when do I need one?
- A RAG (Retrieval-Augmented Generation) pipeline connects a language model to your own data — documents, databases, or knowledge bases — so it can answer questions based on your specific information rather than just its training data. You need a RAG pipeline when you want your AI to work with private, proprietary, or frequently updated data.
- How do you manage GPU costs for AI workloads?
- Through right-sizing compute to actual workload requirements, using spot or preemptible instances for non-time-sensitive jobs, batching inference requests, model quantization to reduce compute needs, and building cost monitoring into your AI infrastructure from day one. We ensure GPU spend is visible, governed, and aligned with actual usage.