Right-sizing your cloud?Workload-aware server recommendations based on cost-efficiency, including egress costs.
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Right-size AI/ML compute with vendor-neutral cloud intelligence and real workload telemetry.

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PRICE/HOURS
CPU
RAM (GB)
CLOUD VENDOR
AWS logo
AWS logo
GCP logo
GCP logo
AWS logo
GCP logo
Hetzner logo
GCP logo
GCP logo
AWS logo
Hetzner logo
AWS logo
GCP logo
AWS logo
AWS logo
AWS logo
Hetzner logo
AWS logo
GCP logo
AWS logo
AWS logo
AWS logo
GCP logo
Hetzner logo
AWS logo
AWS logo
GCP logo
AWS logo
AWS logo
Hetzner logo
AWS logo
AWS logo
AWS logo
GCP logo
AWS logo
GCP logo
SERVER TYPE
t4g.micro
arm64
t4g.small
arm64
t4g.large
arm64
t4g.xlarge
arm64
m7a.medium
arm64
m7a.large
arm64
m7a.2xlarge
arm64
t3a.nano
arm64
t3a.small
arm64
t3a.medium
arm64
t3a.large
arm64
m4.large
arm64
m4.xlarge
arm64
t2.small
arm64
t2.medium
arm64
t2.large
arm64
t2.xlarge
arm64
t2.2xlarge
arm64
t4g.medium
arm64
t4g.medium
arm64
t4g.medium
arm64
t4g.medium
arm64
t4g.medium
arm64
t4g.medium
arm64
c6a.large
arm64
c6a.xlarge
arm64
c6a.2xlarge
arm64
t4g.large
arm64
t4g.medium
arm64
t4g.medium
arm64
t4g.medium
arm64
t4g.medium
arm64
c7i.large
x86_64
c7i.large
x86_64
c5n.large
arm64
e2-highcpu-32
x86_64
REGION
Asia Pacific
Jakarta
Europe
Milan
Asia Pacific
Jakarta
Asia Pacific
Jakarta
Asia Pacific
Hyderabad
Middle East
US East
Middle East
Middle East
Asia Pacific
Hyderabad
US East
Asia Pacific
Melbourne
Asia Pacific
Mumbai
Asia Pacific
Mumbai
Europe
Zurich
Asia Pacific
Mumbai
Asia Pacific
Hyderabad
Africa
Cape Town
Asia Pacific
Hong Kong
Middle East
Europe
Zurich
Asia Pacific
Hong Kong
Asia Pacific
Melbourne
Africa
Cape Town
Asia Pacific
Melbourne
Middle East
Europe
Milan
Asia Pacific
Jakarta
Europe
Zurich
Asia Pacific
Hong Kong
Asia Pacific
Tokyo
US East
Europe
Paris
Europe
Paris
Europe
Milan
us-west-4
Las Vegas
7
providers
5,000+
cloud servers
2M+
benchmarks
500k live
and 100M+ historical prices
<why use="spare_cores">
AI/ML cloud waste starts with guesswork. Spare Cores helps teams compare infrastructure, measure real workload usage, turn telemetry into recommendations, and govern cloud usage across jobs, projects, and teams.
Know
<navigator>
Search and compare 5,000+ servers by price, specs, benchmarks, and cost efficiency.
Track
<resource_tracker>
Track real CPU, GPU, memory, and runtime usage from AI/ML jobs with open-source tooling.
Scale
<advisor>
Turn utilization data and constraints into ranked, explainable instance recommendations.
Govern
<sentinel>
Use dashboards, reports, alerts, and team controls to catch over-provisioning and track savings.
<product id="resource_tracker">
For Data Scientists, ML engineers and AI practitioners
Add lightweight telemetry to AI/ML jobs.
Capture CPU, GPU, memory, disk and network usage.
Get reports plus basic sizing advice in your workflow.
Open-source and MPL-licensed.
<product id="sentinel">
For DS/AI/ML platform and FinOps leads
Sentinel gives teams a live view of telemetry and cost signals across their jobs to spot over-provisioning, catch drift, and turn infrastructure guesswork into measurable optimization.
Sentinel Access
360-view of every job you run in a central dashboard
Feature-rich reports, 7-day retention
Hierarchical or tag-based report breakouts
Free cloud service for individual users
Sentinel team plans
Everything in Sentinel Access, plus team support
Full historical optimization and savings analysis
Auto-tuning resource allocations
Custom data retention, SLA, SSO & RBAC
Optional on-prem or hybrid deployment
<init products="resource_tracker sentinel">
Ready to right‑size your workflows?
  1. Compare cloud servers for free with Navigator
  2. Install the open-source Resource Tracker package.
  3. Start free with Sentinel Access to see recent workflow history and alerts.
  4. Book a 30-minute call to discuss team plans or AI/ML cloud optimization needs.

                                          from resource_tracker import ResourceTracker

                                          # start trackers in the background (non-blocking)
                                          tracker = ResourceTracker()

                                          # your compute-heavy task
                                          train_model_or_render_video()

                                          # view a resource usage report at any time
                                          tracker.report()
                                          # or get resource allocation advice for future runs
                                          tracker.suggest()
                                        
<services from="spare_cores">
More ways Spare Cores helps
Assess, implement, and operate AI/ML cloud optimization with the team that built the tools.
Spare Cores Navigator
Vendor-neutral cloud intelligence covering 5,000+ cloud servers across providers, with standardized pricing, specs, and benchmarks.
Cloud Optimization Assessment
Baseline AI/ML infrastructure spend, identify workload inefficiencies, recommend and prioritize right-sizing actions.
Scalable Shiny / Streamlit Hosting
Monitoring and auto‑scaling containerized dashboards from zero to thousands of users on your cloud—or ours.
Ad-hoc AI Workload Sizing
Instantly pick the lowest-cost, right-sized infra for one-off tasks—like labeling 10 k PDFs with an LLM.
<
release_notes
>
We publish case studies, learnings from implementing Spare Cores components, and other project updates from time to time. Find some of our featured articles below: