AWS has increased pricing for EC2 Capacity Blocks for ML by approximately 15% across all regions where the service is available. The price adjustment affects organizations reserving dedicated GPU capacity for large-scale machine learning workloads, with rates rising uniformly across AWS’s most powerful ML instances, including P5en, P5e, P5, and P4d, powered by NVIDIA GPUs, as well as Trn2 and Trn1 instances that use AWS Trainium. For example, a p5e.48xlarge instance featuring eight NVIDIA H100 GPUs now costs $39.80 per hour, up from $34.61, while the p5en.48xlarge with eight H200 GPUs increased from $36.18 to $41.60 per hour.
While AWS has stated since the 2023 launch that Capacity Block pricing adjusts based on supply and demand, cloud economist Corey Quinn noted in a LinkedIn post that this update differs from typical dynamic pricing:
This was AWS updating the published base rates on their pricing page… $34.608/hr became $39.799/hr uniformly across every region. That’s a policy decision, not supply/demand.
EC2 Capacity Blocks for ML allow organizations to reserve GPU instances within Amazon EC2 UltraClusters, AWS’s high-performance computing infrastructure optimized for distributed ML training requiring hundreds or thousands of GPUs. Unlike standard reserved instances or savings plans, Capacity Blocks guarantee access to specific instance types for defined time periods, typically ranging from one day to several weeks.
The significance of this change extends beyond the immediate cost increase. Steve Wade, founder of Platform Fix, captured the broader implication in the same LinkedIn post from Quinn:
The precedent is set. That’s the part that matters. Once the door is open, it doesn’t close. Every FinOps team just added a new line to their risk register.
Nathan Peck, product steward at Portainer, contextualized the shift within broader economic forces:
Beware of inflation and the weakening of the US dollar outpacing efficiency gains from Moore’s Law. That’s the real tipping point that changes everything about the cloud model. Static prices that don’t keep up with inflation are technically a continuous price drop. The moment hyperscalers can’t keep that game going, all of a sudden buying your own hardware up front looks way better.
The price adjustment reflects real supply chain pressures in the cloud infrastructure market. David Lee, managing director and technology executive at Wells Fargo, commented:
We’re going through another COVID-type supply crunch, especially memory and switches. Prices for everything are going up.
However, the constraint may not be what many expect. James S., a senior DevSecOps engineer, pointed out:
The supply in this case is electricity. The CEO of Microsoft has said he has warehouses full of GPUs that haven’t been installed yet. He doesn’t have anywhere to put them.
Limited alternatives compound the practical impact. As one practitioner observed on Reddit’s r/aws community:
Capacity Blocks are really the only way you can even use these instance types. Rarely can you ever spin one of these up on demand. So in effect, it’s a way for them to advertise one price (on-demand) while actually charging more.
This scarcity means organizations have few options to absorb the increase. Moreover, the price adjustment affects even customers with enterprise discount agreements, since those discounts are typically percentage-based rather than fixed amounts – a 15% public price increase translates into a 15% effective cost increase regardless of the negotiated discount rate.
Questions remain about whether this represents an AWS-specific adjustment or reflects broader industry trends. Spencer T., a strategic solutions engineer at Snowflake, noted that the increase appears focused on P5e instances with NVIDIA H200 GPUs, suggesting “Nvidia may have raised prices on cloud providers, which isn’t a precedent being set, more of price being passed from suppliers onto consumers.”
It remains unclear whether Google Cloud Platform or Microsoft Azure will implement similar adjustments for their GPU offerings, though industry observers suggest the underlying cost pressures affect all hyperscalers.
For ML teams and FinOps practitioners, the price increase reinforces the importance of workload optimization and cost management discipline. As Ivo Pinto, a principal cloud architect, observed:
Although not surprising given current GPU and RAM prices, what is important is understanding the service you use and its pricing scheme.
The pricing update is currently in effect across all AWS regions where EC2 Capacity Blocks for ML are available, with AWS’s next scheduled pricing review set for April 2026. Detailed pricing information is available on the AWS EC2 Capacity Blocks pricing page.
