What's the best deployment stack for AI apps in 2026?
Blog post from Northflank
In 2026, deploying an AI application involves managing a complex stack that includes six distinct layers: frontend, backend API, database, vector store, model inference, and background jobs, with observability integrated across all these layers. Teams have the option to either assemble specialized tools for each layer or use a full-stack platform like Northflank, which supports all layers, including GPU workloads and managed databases, from a single control plane. Northflank allows for seamless deployment of the entire stack, providing CI/CD, preview environments, and built-in observability, while also offering the flexibility to integrate with frontend-specific platforms like Vercel. The choice between assembling a stack versus using a full-stack platform depends on whether a team prioritizes tool specialization or operational simplicity, with Northflank catering to teams that prefer a unified approach, including options for sensitive workloads through BYOC (Bring Your Own Cloud) deployment.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Observability | 12 | 4,261 | 791 | 201 | +16% |
| Vector Search | 12 | 1,918 | 398 | 137 | -21% |
| Secrets Management | 4 | 2,539 | 400 | 136 | +9% |
| RAG | 3 | 1,005 | 263 | 108 | -56% |
| AI Model Fine-tuning | 1 | 762 | 211 | 75 | +14% |
| Local AI | 1 | 69 | 40 | 20 | +23% |
| Real-time | 1 | 6,055 | 1,444 | 270 | -11% |
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