On August 31, 2026, AWS announced it was recognized as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025. The AWS Machine Learning Blog shared the news, but the underlying report is what matters for teams choosing where to run AI workloads.
For product builders, this recognition is a signal, not a verdict. Forrester’s evaluation weighs current offerings against strategy, and a Leader position suggests AWS has both the breadth and the roadmap to support demanding AI infrastructure needs. But the practical question is: does that translate into better outcomes for your specific workloads?
What the Forrester Wave Evaluates
Forrester’s Wave reports assess vendors on a set of criteria that typically include current offering, strategy, and market presence. In the AI infrastructure category, that means looking at compute options (GPUs, Trainium, Inferentia), networking, storage, and the managed services that tie them together. AWS’s inclusion as a Leader implies strong scores across these dimensions, but the supplied blog post doesn’t detail the specific scores or how other vendors compared.
What we do know: AWS has invested heavily in custom silicon like Trainium and Inferentia, and offers a broad portfolio from EC2 instances to SageMaker and Bedrock. That breadth is likely a factor in Forrester’s assessment, but it also means builders face a complex decision tree when choosing the right service for a given job.
Why This Matters for Product Builders
If you’re building AI-powered products, infrastructure choices affect cost, latency, and scalability. A Leader designation can give you confidence that a vendor is investing in the right areas, but it doesn’t replace your own evaluation. For example, if you’re running real-time inference, you might prioritize low-latency options like Inferentia over general-purpose GPUs. If you’re training large models, you need to consider distributed training support and cost efficiency.
The AWS blog post emphasizes the company’s “comprehensive” approach, but the real value is in the details: which services were evaluated, what strengths were noted, and where AWS might have gaps. Without the full Forrester report, we can’t see those specifics, but the recognition suggests AWS is competitive on both execution and vision.
What to Do Next
Don’t switch your infrastructure based on a single analyst report. Instead, use this as a starting point to evaluate your own needs:
- Benchmark your workloads: Test AWS’s AI services against your actual models and data. Look at cost per inference, training throughput, and scalability.
- Consider the ecosystem: AWS’s strength is its integration with other services like S3, Lambda, and Bedrock. If you’re already on AWS, that’s a plus.
- Watch for gaps: Even Leaders have weaknesses. The Forrester report likely notes areas for improvement, so seek out the full report or analyst commentary to understand where AWS might not be the best fit.
For product builders, the takeaway is straightforward: AWS is a credible choice for AI infrastructure, but your decision should be based on your specific requirements, not just a vendor’s market position. Run your own tests, compare costs, and keep an eye on how the infrastructure landscape evolves.
Sources
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.