What's the best deployment stack for AI apps in 2026?

In 2026, the deployment of AI applications involves a complex stack comprising six primary layers: frontend, backend API, database, vector store, model inference, and background jobs, with observability integrated across all layers. Instead of focusing on a single tool, the emphasis is on understanding how these components fit together to form a cohesive system, often requiring either a mix of specialized tools or a unified full-stack platform. Northflank offers a comprehensive solution by running all aspects of the AI app stack —including GPU workloads and managed databases— from a single control plane, thus simplifying deployment, management, and observability. This approach can be contrasted with assembling a stack of specialized tools, which provides flexibility but incurs integration overhead. Observability, a crucial yet often neglected component, addresses specific AI needs such as token usage and latency tracking. The choice between a full-stack platform and an assembled stack depends on the team's expertise and the desire to balance specialization with operational simplicity.

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