How to Deploy an AI Model
Guide to deploying AI models to production with proper infrastructure, scaling, and monitoring.
What You'll Learn
The gap between a working AI model in a notebook and a reliable production deployment is larger than most teams expect. Production AI deployment requires decisions about serving infrastructure, scaling strategy, monitoring, cost optimization, and update mechanisms that are fundamentally different from development concerns. Should you use managed APIs from Anthropic or OpenAI, host models on platforms like Replicate or Together AI, or self-host with frameworks like vLLM? Each option comes with different tradeoffs in cost, latency, privacy, and operational complexity. Getting deployment wrong means either overpaying for infrastructure you do not need or building an unreliable system that frustrates users with slow responses and frequent errors. This guide walks you through the complete AI model deployment process, from choosing the right serving strategy to configuring infrastructure, building the serving layer, implementing monitoring, and planning for zero-downtime updates.
Step 1: Choose deployment strategy
Decide between managed APIs (OpenAI, Anthropic), model hosting (Replicate, Together AI), or self-hosted (vLLM, TGI).
Step 2: Set up infrastructure
Configure compute resources, load balancing, and auto-scaling based on expected traffic patterns.
Step 3: Implement the serving layer
Build the API endpoint with proper authentication, rate limiting, request queuing, and response streaming.
Step 4: Add monitoring and alerting
Track latency, error rates, token usage, and model quality metrics with alerting for anomalies.
Step 5: Plan for updates
Design your deployment for zero-downtime model updates and A/B testing between model versions.
Conclusion
Deploying AI models to production is a discipline that combines infrastructure engineering, cost optimization, and reliability practices. The key decisions are: use managed APIs unless you have strong reasons to self-host, implement proper autoscaling from day one, build comprehensive monitoring for latency and quality metrics, and design your deployment for zero-downtime updates. Getting these fundamentals right prevents the costly rewrites that many teams face after rushing their initial deployment. If you want to deploy AI models to production with confidence, ShipSquad's DevOps and AI engineering squads handle the full deployment lifecycle. Launch your mission at shipsquad.ai.
Frequently Asked Questions
Should I self-host or use managed APIs?▾
Use managed APIs for most cases — they're simpler and scale automatically. Self-host when you need data privacy, custom models, or cost optimization at scale.
How do I handle traffic spikes?▾
Implement request queuing, auto-scaling, and response caching. Use managed services that handle scaling automatically when possible.
What's the typical latency for AI models?▾
Managed APIs: 500ms-3s for first token. Self-hosted: 200ms-1s with proper optimization. Streaming reduces perceived latency significantly.