Spare Cores is a vendor-neutral cloud intelligence platform that helps AI/ML teams right-size their compute infrastructure using real workload telemetry instead of guesswork. The platform combines a searchable database of 5,000+ cloud servers with open-source resource tracking tools and optimization recommendations, enabling teams to cut cloud costs while maintaining performance. It offers a free tier with team dashboards and policy-based automation, plus enterprise-grade services for larger organizations.
Funding
Funding not disclosed
Founders
Product
Problem
AI/ML teams frequently over-provision cloud compute because they lack visibility into what their workloads actually need, leading to significant wasted spending. Traditional cloud cost management tools rely on estimates rather than real usage data, and the complexity of comparing options across multiple vendors makes informed decisions difficult.
Solution
Spare Cores provides a vendor-neutral cloud intelligence platform that combines a comprehensive server comparison database with open-source resource tracking tools. The platform's Navigator lets teams search and compare 5,000+ servers across 7+ providers by price, specs, benchmarks, and cost efficiency, while the Resource Tracker collects fine-grained CPU, GPU, memory, and runtime data from actual AI/ML jobs. The Advisor component turns this utilization data into ranked, explainable instance recommendations, and Sentinel adds governance features like dashboards, alerts, and policy-based automation. The platform integrates directly with existing workflows through Python-based open-source packages, supporting Metaflow and other orchestration tools without requiring teams to change how they work.
Target Audience
Primary customers are data science and machine learning teams, MLOps engineers, and cloud architects who need to optimize compute costs across multiple cloud providers. The platform also serves organizations running AI/ML workloads at scale that require detailed usage tracking and governance capabilities.
Features
- Search and compare 5,000+ cloud servers across 7+ providers with filters for vCPUs, memory, storage, GPU options, and cost efficiency metrics
- Open-source Resource Tracker package for Python and R that collects real CPU, GPU, memory, and runtime usage data from AI/ML jobs
- Automated instance recommendations based on actual utilization patterns, with ranked and explainable suggestions
- Sentinel governance suite with team-wide dashboards, historical reports, cost savings deltas, and policy-based automation
- Support for SSO and RBAC for enterprise governance needs
- Cross-cloud optimization capabilities covering major providers including AWS, GCP, Alibaba Cloud, and Hetzner
- Benchmark data showing operations per second and cost efficiency metrics (e.g., ops/s for $1/hour) for each server
- Free tier available with access to core tracking and comparison features