Agentic compute metasearch for AI workloads

The Skyscanner of AI compute—cost-aware, carbon-aware, provider-agnostic

clusy.io continuously scans cloud, on-prem, and decentralized GPU markets to dispatch every workload to the cleanest, most cost-efficient venue—without compromising policy guardrails or operational control.

Execution snapshot

AML retraining · Tier-1 bank

Carbon-first + budget guardrails

1/3

48TB dataset • 8×A100 • finish <9h • P2P encrypted handoff

Cost delta

-38%

vs. on-demand

CO₂ avoided

-61%

30-day rolling

Provider distribution

Live mix
CUDOS hydropower52%
On-prem Milan28%
AWS us-east-120%
CUDOS hydropowerOn-prem MilanAWS us-east-1 failover

Time-shift to 02:00 UTC to ride hydropower surplus and preserve USD 40k cap.

AI compute orchestration tailored to your workload

Battle-tested, enterprise-grade platform with built-in carbon optimization, verification, and complete workflow control.

Industry playbooks

Six production-ready blueprints covering finance, health, retail, AV, media, and industrial workloads.

Deployment time
under 2 wks
Combined savings
45-70%
Providers bridged
12+
Protocols
P2P tunnels
  • Clarify workloads in natural language; supervisors auto-fix intents before any human review.
  • Blend on-prem, cloud, and decentralized GPU inventory while enforcing carbon and cost guardrails.
  • Stream structured intents over encrypted P2P channels—no intermediate storage, no replicas to delete.
01

Financial services

Financial Services: Fraud Detection Model Training

65% cost ↓ · 58% CO₂ ↓

A major bank trains fraud detection models on 50TB of transaction data. clusy.io automatically routes to CUDOS decentralized GPUs during off-peak hours, reducing training costs by 65% and CO₂ emissions by 58% compared to always-on cloud instances.

Nightly AML + fraud retraining across 50TB of data

CUDOS off-peak GPUsAuto carbon guardrailsP2P encrypted transfer
02

Healthcare + life sciences

Healthcare: Medical Imaging Inference at Scale

42% cost ↓ · 35% CO₂ ↓

A hospital network processes 10,000+ medical scans daily. clusy.io routes inference workloads across AWS, GCP, and on-prem TPU pods based on real-time pricing and carbon intensity, maintaining <100ms latency while cutting costs by 42%.

Serve 10k CT scans/day with <90ms latency

TPU Pods europe-west4Latency-first orchestrationHIPAA-safe clarifications
03

Retail + marketplaces

E-commerce: Recommendation Engine Batch Updates

70% CO₂ ↓ · 33% cost ↓

An online retailer updates product recommendations nightly. clusy.io time-shifts batch jobs to run during peak renewable energy windows (3-6 AM local time), achieving 70% lower carbon footprint while meeting SLA requirements.

Nightly catalog refresh in renewable windows

Grid-aware schedulingBudget guardrailsSpot orchestration
04

Autonomous systems

Autonomous Vehicles: Simulation Workloads

3x throughput · 28% cost ↓

An AV company runs millions of driving simulations monthly. clusy.io distributes workloads across cloud GPU clusters and on-prem infrastructure, optimizing for cost during development phases and performance during validation runs.

Millions of Monte Carlo sims every month

Burst-to-cloud routingIntent chainingContinuous validation
05

Media + streaming

Media & Entertainment: Video Processing Pipeline

55% cost ↓ · 48% CO₂ ↓

A streaming platform processes 4K video uploads. clusy.io routes encoding jobs to the cheapest available provider (AWS, GCP, or CUDOS) based on current spot pricing, reducing processing costs by 55% while maintaining quality standards.

4K encode backlog cleared before 7 a.m.

Rate-card fusionQuality gatesSolar-aware routing
06

Industrial + manufacturing

Manufacturing: Predictive Maintenance AI

47% cost ↓ · 40% CO₂ ↓

An industrial manufacturer trains models on IoT sensor data from 500+ production lines. clusy.io schedules training during low-demand periods, leverages carbon credits from renewable energy providers, and helps meet corporate sustainability targets.

Train predictive models for 500+ production lines

Time-shifted trainingCarbon credit syncOn-prem overrides

How it works

An agentic pipeline that transforms natural language requests into optimized compute orchestration across every provider you trust.

1

Describe the workload & guardrails

Start with plain English, CLI flags, or CI signals—"Fine-tune Llama-2 with 8×H100, under $3/hr, EU data only." The intake agent captures GPUs, datasets, deadlines, compliance rules, and whether to optimize for greenest, cheapest, or fastest turnaround.

Advanced prompts capture per-stage budgets, residency constraints, carbon caps, and preferred inventory you already contract.

2

Agentic planners build Cluster Intents

LLM supervisors normalize every requirement into a Cluster Intent spec that maps stages, checkpoints, network policy, and evidence requirements. Conflicts are auto-repaired, missing inputs are requested, and every intent is signed-off against your policy pack.

Validated intents stay encrypted end-to-end and never rest on clusy.io infrastructure—only your control plane can decrypt them.

3

Meta-marketplace quotes every provider

The Skyscanner-style broker fans out to cloud, on-prem, and decentralized GPU pools to pull live pricing, queue depth, carbon intensity, and SLA signals. The agent ranks valid clusters against your objective mix: greenest, cheapest, fastest, or weighted combo.

Custom ranking functions allow minimum carbon thresholds, spot-only routing, or contractual prioritization with deterministic fallbacks.

4

Approve, dispatch, and monitor in one click

Once you approve an itinerary, clusy.io time-shifts when needed, dispatches over P2P encrypted tunnels, and streams live telemetry back to your stack. If quotes change, the agent automatically rebalances while preserving budgets, carbon caps, and SLAs.

Carbon, cost, and performance ledgers update in real time so finance, sustainability, and infra teams see the exact impact of each run.

The complete pipeline

Intent intake: dataset parsing (CSV, JSON, Parquet) → guardrail + residency extraction → schema retrieval
Agentic planning: intent building → validation → auto-repair → clarification loops (if needed)
Marketplace: provider discovery → quote aggregation → multi-objective (greenest / cheapest / fastest) ranking
Execution & telemetry: time-shift recommendations → grid-aware dispatch → carbon, cost, and SLA tracking

Enterprise-grade platform for AI compute optimization

Built with practices that distinguish world-class AI operations from the rest.

Carbon-aware scheduling

Automatically schedule workloads when renewable energy is abundant, reducing CO₂ footprint by up to 60%. Track emissions in real time.

Multi-objective optimization

Balance cost, performance, and carbon emissions. Choose between cheapest, fastest, greenest, or optimal pathways for every workload.

Time-shift recommendations

Get intelligent suggestions for when to run workloads for maximum cost savings and minimal environmental impact with deferred execution.

P2P encrypted data transfer

Workload definitions travel through double-encrypted peer-to-peer tunnels directly between your teams and the clusters. clusy.io never saves or inspects your data.

Join the pilot

Pilot the carbon-aware orchestration layer that stays inside your control plane

Share how you run training or inference today—budgets, latency thresholds, carbon goals, residency rules. We will respond within one business day to schedule a live walkthrough and move your team into the next waitlist cohort.

  • Agentic supervisors digest natural-language briefs, CLI flags, and CI signals to compile signed Cluster Intents across the provider contracts you already hold.
  • Carbon, latency, residency, and budget guardrails are simulated before dispatch so nothing leaves your control plane until policies pass.
  • Peer-to-peer, double-encrypted tunnels keep workload intents and telemetry off clusy.io infrastructure end to end.
Waitlist form

We only use this information to coordinate onboarding and never share it with external providers.

clusy.io — Compute-Energy Optimisation