Granulate provides an AI‑driven platform that continuously profiles cloud‑native workloads and applies kernel‑level tuning—such as CPU frequency scaling, thread affinity, and memory placement—in real time without code changes. The system integrates with AWS, Azure, GCP and Kubernetes, delivering higher CPU efficiency, lower latency, and measurable cloud‑cost reductions. Users monitor performance and savings through a web console or API for data‑driven capacity planning.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises running cloud-native workloads often experience low CPU and memory utilization due to static configuration and lack of workload-specific tuning, leading to inflated compute expenses and degraded application performance.
Solution
Granulate delivers an AI-driven optimization platform that continuously profiles running workloads and applies kernel-level adjustments in real time, without requiring code changes or redeployment. The system leverages machine‑learning models to predict optimal scheduling, thread placement, and resource allocation parameters for each workload instance. By injecting these optimizations at the operating system level, Granulate improves CPU efficiency and reduces latency, translating into lower cloud spend and higher throughput. The platform integrates with major public‑cloud providers and supports container orchestration frameworks, enabling seamless deployment across heterogeneous environments. Users access performance metrics and cost impact reports through a web console or API, facilitating data‑driven capacity planning.
Target Audience
The primary customers are DevOps, SRE, and cloud engineering teams at mid‑size to large enterprises that run latency‑sensitive or compute‑intensive applications on public‑cloud infrastructure.
Features
- Autonomous workload profiling using low‑overhead telemetry agents
- Real‑time kernel‑level tuning (e.g., CPU frequency scaling, thread affinity, memory page placement)
- Machine‑learning inference engine that continuously adapts optimizations to workload behavior
- Native integrations with AWS, Azure, GCP, and Kubernetes clusters for automated policy enforcement
- No‑code deployment model that operates on existing VM or container instances
- Centralized dashboard with per‑service utilization, latency, and cost‑savings analytics
- RESTful API for programmatic control and integration with CI/CD pipelines
- Enterprise‑grade security with role‑based access control and encrypted data transmission