AWS vs CoreWeave for AI: A Practical Guide for AI/ML Teams
The AI infrastructure market has split into two camps: broad general-purpose cloud platforms that do everything, and specialized GPU clouds that do one thing extremely well. AWS and CoreWeave represent opposite ends of that spectrum.
This guide compares AWS and CoreWeave across seven dimensions that matter most to AI/ML engineers and data teams: compute power, developer tooling, data infrastructure, pricing, scalability, security, and ecosystem support.
Development teams compare AWS and CoreWeave because the choice affects everything from training cost to deployment speed. At the moment, AWS offers the widest menu of cloud services on the planet. CoreWeave built its entire business around GPU density and Kubernetes workloads.
AI/ML engineers and data teams care about three things above all: access to the latest GPUs, predictable cost per training run, and minimal operational overhead. The right platform depends on where a team’s needs lie on the spectrum between general cloud needs and pure GPU compute. And for teams that need performance, cost efficiency, and data sovereignty in India, we cover where Neysa fills gaps that neither AWS nor CoreWeave addresses.