AI Infrastructure

AI infrastructure is infrastructure built or adapted to support LLM and agent-based systems in production, addressing concerns that general-purpose infrastructure does not cover well: GPU compute provisioning, model lifecycle management, inference-specific scaling, non-deterministic output handling, and eval/observability integration.

Details

Inference is GPU-bound with bursty demand patterns, model artifacts are large and versioned independently of application code, and outputs are non-deterministic - making quality assurance dependent on evals complementing deterministic tests, since non-deterministic outputs cannot be fully covered by assertion-based testing alone. Latency and cost tradeoffs are shaped by token-based pricing, prompt caching, and batch inference, none of which map to conventional request-based scaling.

AI infrastructure spans multiple layers. At the compute layer: GPU provisioning, inference runtimes, and request batching. At the middleware layer: AI gateways for routing, rate limiting, and provider abstraction, alongside observability tooling that tracks model-specific metrics like token usage, latency, and output quality. At the platform layer: agent hosting and eval runners for higher-level orchestration.

Specialized security boundaries - sandboxes for code execution, guardrails for output filtering - address risks unique to systems with non-deterministic, potentially harmful outputs.

Examples

• Model hosting - serving model weights behind an API with GPU allocation, autoscaling, and request queuing
• AI gateways - routing, rate limiting, and provider abstraction across multiple inference providers
• Observability for AI systems - tracing, token usage tracking, and output quality monitoring
• AI search/RAG infrastructure - vector databases, embedding pipelines, and retrieval services
• Eval runners - automated evaluation pipelines for measuring model and system output quality
• Sandboxes - isolated execution environments for LLM-generated code execution
• Agent hosting platforms - managed environments for deploying and scaling agent workloads