How to Choose Your AI App Stack
This article provides a framework for evaluating and selecting the right AI app stack based on your use case, team, budget, and deployment requirements.
Why it matters
Choosing the right AI app stack is critical for delivering effective AI solutions that meet business needs and constraints. This article provides a structured approach to evaluating options and making informed decisions.
Key Points
- 1The use case, not the tool, should drive the stack choice
- 2Model and platform are separate decisions with different evaluation criteria
- 3Low-code platforms are a legitimate option for most AI apps
- 4Latency requirements impact the required architecture
- 5Switching costs are real but manageable with upfront planning
Details
The article outlines a decision framework for choosing an AI app stack in 2026. It emphasizes that the right combination of model, platform, and infrastructure should be determined by the specific use case, not by hype or popularity. Key considerations include the AI task (e.g., text classification, data extraction), latency requirements (sub-second vs. asynchronous), data sources, and accuracy thresholds. For the model choice, the article recommends using the cheapest option that meets the accuracy needs, with frontier models for high-stakes tasks, mid-tier models for general business use, and lightweight models for high-volume, speed-sensitive applications. On the platform side, the article suggests that low-code options are viable for most SMB use cases, offering faster time-to-market and lower costs compared to custom code, unless there are unusual latency or integration requirements.
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