Building AI-Ready Infrastructure: What Most Teams Get Wrong

Everyone wants to run AI. Few teams have infrastructure that can actually support it at scale. Here's what separates teams that ship AI products from those that stay stuck in proof-of-concept.

Compute Is Not the Bottleneck You Think

Yes, GPUs matter. But most AI failures aren't compute failures — they're data pipeline failures, latency failures, and cost management failures.

The Data Layer

• Feature stores: Centralize feature computation to avoid training-serving skew
• Vector databases: Pinecone, Weaviate, or pgvector for embedding search
• Data versioning: DVC or Delta Lake — your model is only as good as your data lineage

Serving Infrastructure

Model serving has different requirements than traditional APIs:

• GPU memory management — batching requests efficiently
• Model caching and warm-up strategies
• Fallback logic when models are unavailable
• Cost-aware routing between model tiers

Observability for AI

Standard APM tools aren't enough. You need model-specific monitoring: drift detection, output quality scoring, token usage tracking, and latency percentiles per model version.

Build the data infrastructure first. The models will follow.