While everyone wants to build AI applications, few engineers know how to choose the right infrastructure. Through building AI systems across different scales and constraints, I’ve learned that infrastructure decisions made early determine what’s possible later, and bad choices create expensive problems that persist for years.

Most infrastructure guides give you a technology list without helping you decide. This guide provides decision frameworks that help you choose the right infrastructure for your specific situation, not generic recommendations that might not fit your needs.

The Infrastructure Decision Framework

Start with your constraints, not with technologies:

Understand Your Requirements

Scale requirements. How many users? How many requests per second? What’s your growth trajectory?

Latency requirements. What response times do users expect? What’s acceptable during peak load?

Availability requirements. What uptime do you need? What’s the cost of downtime?

Budget constraints. What can you spend monthly? How does that scale with usage?

Team expertise. What technologies does your team know? What’s the learning budget?

Categorize Your Workload

Interactive AI (chat, search, recommendations). Needs low latency, high availability, moderate throughput.

Batch processing (document analysis, data pipelines). Can tolerate higher latency, needs high throughput, cost-sensitive.

Hybrid (interactive with batch components). Different requirements for different parts of the system.

Real-time processing (streaming data, live analysis). Needs consistent low latency, often requires specialized infrastructure.

For architecture foundations, see my guide to AI system design patterns.

Compute Infrastructure Decisions

Choosing where your code runs:

Managed Container Services

When to choose. You want to focus on application code, not infrastructure. Your scale is moderate. Your team isn’t Kubernetes-native.

Options. AWS ECS/Fargate, Google Cloud Run, Azure Container Apps. Each has tradeoffs in features, cost, and integration.

Tradeoffs. Limited customization, potential vendor lock-in, but dramatically reduced operational burden.

Best for. Most AI applications that don’t have extreme scale or specialized requirements.

Kubernetes

When to choose. You need maximum flexibility. You have complex orchestration requirements. Your team has Kubernetes expertise.

Options. Managed (EKS, GKE, AKS) vs self-managed. Managed reduces ops burden while preserving flexibility.

Tradeoffs. Significant complexity, steep learning curve, but unmatched flexibility and portability.

Best for. Large-scale deployments, multi-cloud requirements, organizations with platform teams.

Serverless Functions

When to choose. Your workload is event-driven and bursty. You want zero idle cost. Individual functions are short-running.

Options. AWS Lambda, Google Cloud Functions, Azure Functions. Timeouts and cold starts vary.

Tradeoffs. Cold start latency, execution time limits, harder debugging. But truly pay-per-use with zero ops.

Best for. Event handlers, preprocessing pipelines, lightweight API endpoints.

GPU Compute

When to choose. Self-hosting models that need GPU inference. Training or fine-tuning models.

Options. Cloud GPU instances (expensive), GPU-specific providers (CoreWeave, Lambda Labs), on-premises.

Tradeoffs. Expensive but necessary for certain workloads. Availability can be limited.

Best for. Self-hosted model inference, model training, specialized workloads.

For deployment specifics, see my guide on deploying AI with Docker and FastAPI.

AI Service Decisions

Choosing how to access AI capabilities:

Managed AI APIs (OpenAI, Anthropic, Google)

When to choose. You want simplest integration. You don’t have specialized model requirements. You prioritize development speed.

Tradeoffs. Data leaves your environment, costs scale linearly, no customization. But fastest time-to-value.

Best for. Most applications, especially early stage. Validate your product before optimizing infrastructure.

Cloud Provider AI Platforms (Bedrock, Azure OpenAI, Vertex)

When to choose. You want managed APIs with cloud integration. You need enterprise features. You’re already invested in a cloud provider.

Tradeoffs. Some vendor lock-in, pricing varies. But better integration with cloud services, enterprise compliance.

Best for. Enterprise deployments, multi-model strategies, organizations with cloud commitments.

Self-Hosted Models

When to choose. You have strict data privacy requirements. You need model customization. You have predictable high-volume usage.

Tradeoffs. Significant operational burden, GPU costs, expertise requirements. But full control and potentially lower costs at scale.

Best for. Privacy-sensitive applications, specialized domains, very high-volume use cases.

My guide on MLOps best practices covers self-hosted model operations.

Storage Infrastructure Decisions

Choosing where your data lives:

Vector Databases

Managed options (Pinecone, Weaviate Cloud). Easiest operation, scales automatically. Higher cost per query.

Self-managed options (Weaviate, Milvus, Qdrant). More control, potentially lower cost. Operational overhead.

PostgreSQL with pgvector. Simplest if you already use PostgreSQL. Performance ceiling at very high scale.

Decision factors. Scale requirements, operational capability, budget, existing database investments.

For vector database comparisons, see my guide to vector databases.

Object Storage

Cloud object storage (S3, GCS, Azure Blob). The default for unstructured data. Cheap, scalable, reliable.

Decision factors. Mostly about which cloud you’re using. They’re functionally equivalent for most uses.

Caching Infrastructure

Managed Redis (ElastiCache, Memorystore). Operational simplicity for caching needs.

Self-managed Redis. More control, complexity. Usually not worth it unless you have specific requirements.

In-memory caching. For single-instance deployments, local caching is simpler.

Decision factors. Deployment topology, consistency requirements, operational preferences.

Relational Databases

Managed databases (RDS, Cloud SQL). Standard choice for structured data with transactions.

Serverless databases (Aurora Serverless, Cloud SQL Serverless). Good for variable workloads but cold starts affect latency.

Decision factors. Workload patterns, latency requirements, operational preferences.

Networking Decisions

How your infrastructure communicates:

API Gateways

When to use. You need authentication, rate limiting, or API management features.

Options. AWS API Gateway, Kong, custom (NGINX/Envoy).

Tradeoffs. Additional latency and cost vs built-in management features.

Load Balancing

Application load balancers. Standard for HTTP/HTTPS traffic with multiple backend instances.

Network load balancers. For TCP/UDP traffic or when you need maximum throughput.

Decision factors. Protocol requirements, traffic patterns, feature needs.

Service Mesh

When to use. Complex microservices need mTLS, observability, or traffic management.

When to avoid. Added complexity isn’t worth it for simpler architectures.

Options. Istio, Linkerd, cloud-native options.

Cost Optimization Strategies

Making your infrastructure efficient:

Right-Sizing

Start small, scale up. Begin with minimum viable infrastructure. Add capacity based on actual usage.

Monitor and adjust. Regularly review utilization. Downsize over-provisioned resources.

Autoscaling. Let infrastructure scale with demand rather than provisioning for peak.

Pricing Models

Reserved capacity. For predictable workloads, 1-3 year commitments significantly reduce costs.

Spot/preemptible instances. For fault-tolerant batch workloads, use discounted interruptible capacity.

Committed use discounts. Cloud providers reward commitment with lower prices.

Architecture Efficiency

Model routing. Sending simple queries to cheap models dramatically reduces AI costs.

Caching. Every cached response saves an AI API call.

Batch processing. Batch operations are more efficient than individual requests.

For cost optimization strategies, see my guide on AI cost management architecture.

Building for the Future

Make decisions that allow evolution:

Avoid Lock-In Where It Matters

AI APIs. Abstract provider-specific code behind interfaces. Switching should require minimal changes.

Data storage. Use standard formats and interfaces where possible.

Compute. Containers provide reasonable portability across platforms.

Plan for Scale

Stateless services. Stateless architectures scale horizontally without coordination.

Externalized state. Keep state in purpose-built stores, not in your application services.

Async processing. Queue-based architectures handle scale better than synchronous chains.

Maintain Operational Simplicity

Fewer technologies. Each technology is operational burden. Add only what you need.

Managed services preference. Operations you don’t do are operations that can’t break.

Automation. Automate deployment, scaling, and recovery. Manual operations don’t scale.

Decision Checklist

Before committing to infrastructure:

Requirements validation. Have you verified actual requirements, not assumed ones?

Team capability. Can your team operate this infrastructure?

Cost modeling. Do you understand the cost implications at current and projected scale?

Exit strategy. If this choice is wrong, how do you change course?

Simplicity check. Is this the simplest infrastructure that meets your needs?

The Path Forward

Infrastructure decisions shape what’s possible. Good decisions enable growth; bad decisions create constraints that are expensive to escape.

Start simple. Choose managed services when they meet your needs. Add complexity only when requirements demand it. Monitor costs and performance. Be willing to evolve as needs change.

The best infrastructure is the simplest one that meets your requirements. Complexity has costs, in money, operations, and development speed. Add it deliberately, not by default.

Ready to build AI infrastructure that fits your needs? To see these decisions in context with real implementations, watch my YouTube channel for hands-on tutorials. And if you want to learn from other engineers making infrastructure decisions, join the AI Engineering community where we share architecture experiences and lessons learned.