Now with lower costs with Active Idle scaling

Elastic compute

for AI workloads

Scalable compute for AI and production.

Built for performance and efficiency.

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Elastic Compute Platform

Infrastructure Layer

Elastic Compute enables teams to scale infrastructure dynamically for AI and production systems.

Scale Smarter

Scale and optimize compute resources seamlessly, automate provisioning, and keep workloads performant without unnecessary overhead and operational complexity.

Operate Efficiently

Scale and optimize compute resources seamlessly, automate provisioning, and keep workloads performant without unnecessary overhead and operational complexity.

99.99%

availability

Global

deployment

Secure

by design

Traditional compute infrastructure is built around fixed capacity planning, forcing teams to estimate peak demand in advance and provision resources accordingly. This model often results in excess idle capacity during low usage periods or performance degradation when demand exceeds planned limits.

Elastic Compute replaces rigid provisioning with a dynamic, workload-aware compute model. CPU and memory resources are allocated and released automatically based on real-time demand signals, allowing applications to scale seamlessly without manual intervention or predefined capacity rules.

By continuously adapting to workload requirements, Elastic Compute delivers consistent performance under load while improving resource efficiency. This approach ensures infrastructure costs remain closely aligned with actual usage, creating a resilient and scalable foundation for modern applications.

Conventional compute infrastructure is built on static capacity planning, requiring teams to forecast peak demand and provision resources long before workloads are deployed. This approach introduces structural inefficiencies—either over-provisioning infrastructure that remains idle for extended periods or under-provisioning systems that experience degraded performance during unexpected demand spikes.

Elastic Compute introduces a workload-aware compute model that dynamically adjusts resource allocation in real time. Instead of relying on fixed capacity or predefined scaling rules, CPU and memory are provisioned and released automatically based on live workload signals. This allows applications to scale seamlessly as demand changes, without manual intervention, operational overhead, or rigid infrastructure constraints.

By continuously aligning compute resources with actual workload requirements, Elastic Compute delivers predictable performance under load while maximizing resource efficiency. This adaptive approach reduces infrastructure waste, simplifies operations, and ensures costs closely reflect real usage—providing a resilient, scalable foundation for AI-driven and production-grade applications.

Running compute infrastructure in production introduces ongoing operational challenges, including capacity planning, scaling policies, and cost optimization. Teams are often required to balance performance reliability against infrastructure efficiency, leading to complex workflows and increased operational overhead as systems grow in scale and complexity.

Elastic Compute simplifies these challenges by abstracting capacity management away from application teams. By automatically adapting compute resources to workload demand, the platform removes the need for manual scaling decisions, threshold tuning, or constant infrastructure monitoring. This allows teams to operate systems with greater confidence and fewer operational risks.

As a result, organizations can deploy and scale applications more efficiently while maintaining consistent performance and predictable costs. Elastic Compute enables teams to focus on application logic and delivery, while the platform ensures infrastructure remains responsive, efficient, and aligned with real production requirements.