Infrastructure Guide
AI Infrastructure Planning Guide (2026) - Capacity and Cost Planning
AI infrastructure planning: API-first for <1M tokens/month (low risk), self-host for >10M tokens/month (lower cost at scale). Plan for: compute, storage, bandwidth, redundancy, monitoring.
Direct answer
AI infrastructure planning: API-first for <1M tokens/month (low risk), self-host for >10M tokens/month (lower cost at scale). Plan for: compute, storage, bandwidth, redundancy, monitoring.
Fast path
- Volume: estimate monthly token usage, query volume, peak concurrency.
- Model: choose API vs self-hosted based on volume and privacy needs.
- Compute: size GPU/CPU resources for latency requirements.
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Implementation Steps
- Volume: estimate monthly token usage, query volume, peak concurrency.
- Model: choose API vs self-hosted based on volume and privacy needs.
- Compute: size GPU/CPU resources for latency requirements.
- Storage: plan for vector DB, cache, logs, model weights.
- Scale: design for 3-5x peak capacity, implement auto-scaling.
Frequently Asked Questions
When to self-host AI models vs use API?
Self-host AI when: >10M tokens/month (cost savings), data privacy requirements, custom fine-tuning needed, latency <100ms required. Use API when: <1M tokens/month, variable demand, limited ML expertise, rapid iteration needed.
How much GPU for AI inference?
GPU for AI inference: Llama 7B (1x A10G, $0.50/hr), Llama 70B (4x A100, $12/hr), GPT-4 class (8x H100, $32/hr). Estimate: concurrent users × tokens/sec × model size. Cloud GPU is 2-3x cheaper than API at scale.
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