RAG, agents, and multimodal apps: choosing compute for different AI products

AI products often look similar from the outside, but their compute bottlenecks can be very different. RAG, agent automation, and multimodal generation need different evaluation criteria.

Why it matters

RAG may be limited by document quality, embedding throughput, retrieval latency, or reranking. Agents may be limited by multi-step tool calls and failure recovery. Multimodal workloads may be limited by memory and sustained generation throughput. Treating them as one generic GPU problem leads to poor choices.

How to apply it

Write down request volume, model size, context length, input and output type, whether jobs can queue, and whether responses must be real time. Use a short rental to measure actual latency, memory pressure, and failure modes before selecting a larger or longer-running setup.

Next steps

Let the product stage guide the resource stage. Use smaller or flexible rentals for validation, then scale only the bottleneck that the data exposes. Recheck the choice whenever traffic, model size, or output format changes.