Stop placing workloads by habit. Start placing them by signal.
- Cross-provider region scoring on carbon, cost, and latency
- Workload scheduling into cleaner-grid windows without SLA breach
- Per-workload Scope 2 evidence with audit-ready Carbon Ledger
- Works with your current providers - no re-platforming required
Static placement is costing you on every axis
Most public cloud teams pick regions once and never revisit. The orchestration gap compounds over time.
Carbon and cost drift in opposite directions
Carbon and cost drift in opposite directions
Hyperscaler-native tools optimize cost or carbon - never both. Your cleanest region is rarely your cheapest window, and you have no systematic way to weigh the tradeoff.
Scope 2 reconstructed from invoices after the fact
Scope 2 reconstructed from invoices after the fact
Region-level invoice data arrives weeks late, carries no workload attribution, and requires manual methodology assumptions that auditors will challenge.
AI batch jobs run whenever infrastructure is free, not when the grid is clean
AI batch jobs run whenever infrastructure is free, not when the grid is clean
Training, inference, and data pipeline runs default to immediate scheduling. There is no signal linking grid carbon intensity to batch window selection.
Hyperscaler region choices made by default, not by data
Hyperscaler region choices made by default, not by data
Teams pick us-east-1 or eu-west-1 because that is where the account was created. Carbon, real-time energy price, and latency to actual users are not in that decision.
Cross-provider optimization is a spreadsheet exercise
Cross-provider optimization is a spreadsheet exercise
FinOps tools compare cost across clouds. ESG tools model emissions independently. Nothing connects both signals to the scheduling decision in real time.
Cloud bills growing faster than usage
Cloud bills growing faster than usage
Reserved capacity, spot market patterns, and scheduling inefficiencies compound. Without signal-driven placement, you pay for the convenience of predictable but suboptimal defaults.
How GREENPOW compares to the alternatives
Against hyperscaler-native tools, FinOps platforms, standalone ESG tooling, and basic orchestration.
| GREENPOW | Hyperscaler native | FinOps platform | ESG tooling | Basic orchestration | |
|---|---|---|---|---|---|
| Cross-provider region scoring | - | Cost only | - | - | |
| Carbon and cost weighted together | - | - | - | - | |
| Real-time grid carbon signal | - | - | Historical | - | |
| Workload scheduling for clean-grid windows | Limited | - | - | - | |
| Per-workload Scope 2 evidence | Aggregated | - | Estimated | - | |
| Audit-ready evidence labels | - | - | Varies | - | |
| Sovereign deployment | - | - | - | Partial | |
| No re-platforming required |
The orchestration lifecycle for public cloud workloads
Eight stages from raw signal to evidence output. Each stage produces a structured artifact that feeds the next.
Technical deployment surface
GREENPOW Public Cloud integrates at the orchestration and observation layer - no provider migration required.
Kubernetes scheduler integration
Deploy as a scheduler extender or admission webhook. Placement decisions annotate pod specs before scheduling. Works with EKS, AKS, GKE, and self-managed clusters.
Provider APIs and Terraform
Read-only provider API access for region availability, pricing, and capacity. Terraform provider outputs placement recommendations as data sources usable in your existing IaC.
Carbon signal ingestion
ENTSO-E API for European grid carbon intensity. Provider-specific renewable energy data where available. Proprietary R&D models for regions with limited direct telemetry.
Carbon Ledger evidence exports
Per-workload evidence records exported as JSON, CSV, or PDF. Webhook delivery for real-time audit pipeline integration. Permalinked evidence packs for each reporting period.
Observability stack integration
Metrics emitted to Prometheus, Datadog, or Grafana. Placement decisions and carbon records accessible via API. Dashboard templates for FinOps and sustainability reporting.
Sovereign deployment options
GREENPOW control-plane can run inside your VPC or on-premise. Signal ingestion, scoring, and evidence generation do not require data to leave your environment.
How teams use public cloud orchestration
AI batch scheduling across regions
Training and inference jobs run immediately on available capacity in whichever region the team defaults to. Carbon impact is unknown until the monthly ESG report.
GREENPOW holds the job queue until the next clean-grid window in the lowest-carbon eligible region. Carbon per training run drops and the evidence is attached to each job record.
Multi-region Scope 2 reporting for regulated finance
The compliance team reconstructs Scope 2 from hyperscaler invoices, applies market-average emission factors, and produces estimates that auditors flag as methodology-deficient.
Every workload carries a time-stamped, region-attributed Carbon Ledger entry with an evidence label. Auditors receive a permalinked evidence pack - no reconstruction required.
Cross-provider region selection for a new data service
The architecture team runs a spreadsheet comparison of AWS eu-west-1, Azure West Europe, and GCP europe-west1 on cost and latency only. Carbon is not in the decision.
GREENPOW scores all three on carbon intensity, energy price, latency to the target user region, and residency fit simultaneously. The recommendation arrives with a ranked explanation.
Continuous cost and carbon rebalancing for SaaS platform
Reserved capacity allocations are set once per quarter. Carbon and cost drift accumulate between reviews. The FinOps team and sustainability team operate independently.
GREENPOW continuously re-scores regions and surfaces rebalancing recommendations when carbon or cost conditions shift materially, with expected savings and carbon delta attached.
Technical questions
Does GREENPOW require migrating away from our current cloud providers?
No. GREENPOW operates as an orchestration and intelligence layer above your existing provider relationships. You keep your accounts, contracts, and reserved capacity. GREENPOW adds signal-driven placement decisions on top.
How does carbon intensity data get into GREENPOW?
GREENPOW ingests real-time carbon intensity from ENTSO-E for European grids, from provider-published renewable energy feeds where available, and from GREENPOW R&D models for regions with limited direct telemetry. Every figure carries a disclosure label.
What happens when a clean-grid window conflicts with an SLA?
SLA constraints are hard filters applied before any carbon or cost optimization. If no clean window exists within the SLA boundary, GREENPOW selects the least-carbon eligible slot that satisfies the SLA. It never violates a declared constraint to improve a carbon score.
How does the Carbon Ledger evidence hold up to auditors?
Each Carbon Ledger record carries: a workload identifier, a region, a timestamp, an energy consumption figure, a carbon intensity figure with source, a carbon total, an evidence label (Observed/Measured/Modeled/R&D validated), and a methodology reference. Audit packs are permalinked and versioned. Auditors can reproduce any figure from the raw signal archive.
Can GREENPOW integrate with our existing Kubernetes setup?
Yes. GREENPOW deploys as a Kubernetes scheduler extender or admission webhook and annotates pod specs with placement decisions before scheduling. It works with EKS, AKS, GKE, and self-managed clusters. No changes to your existing workload manifests are required.
What is the latency impact of signal-driven placement?
Placement decisions are computed in-process and are typically sub-50ms. For workloads where scheduling decisions happen ahead of execution (batch, async), latency is not on the critical path. For real-time scheduling, GREENPOW can operate in advisory mode, returning recommendations alongside existing scheduling.
Carbon Ledger: per-workload Scope 2 evidence
Public Cloud Optimization ships with the Carbon Ledger as a built-in capability. Every workload, tenant, and region gets a time-stamped Scope 2 figure with an evidence label (Observed, Measured, Modeled, R&D validated) and a permalinked evidence pack your auditors will accept.
See the live Carbon LedgerWhen to pick a sibling product instead
Model your workload placement
Tell us your workload profile, provider mix, and carbon reporting requirements. We will map an orchestration path together.