Put every workload in the right place.
Zyvrek evaluates whether AI workloads should stay, optimise in place, use new capacity elsewhere or switch, based on real workload economics, performance evidence, availability and operational constraints.
Not tied to one cloud
Region, reliability, security
Not headline GPU price
Stay, place, switch, or don’t
Cheapest GPU does not mean lowest cost.
Zyvrek evaluates the complete workload decision: what is technically possible, what is economically avoidable, and what survives the customer’s real operating constraints.
Model the workload
GPU, runtime, portability, state, data, region, security, reliability, latency, interruption tolerance and commercial commitments.
Filter viable supply
Evaluate only provider capacity that can actually satisfy the workload. Unknown hard-constraint data does not silently pass.
Calculate true cost
Include avoidable spend, commitments, source egress, migration engineering, downtime, failure cost and transition risk.
Weight performance evidence
Separate controlled benchmarks from customer-verified source performance. Modelled opportunity is not presented as verified savings.
Check the market again
Re-check price, availability and qualification before advancing a recommendation. If the preferred option disappears, evaluate the next qualified fallback, or STAY.
Earn the right to automate
Start read-only. Shadow recommendations first; use controlled pilots to measure realised outcomes before broader automation.
Five outcomes. No forced answer.
A useful optimisation system must be able to recommend doing nothing when the economics do not justify change.
Current placement remains the best qualified option.
Improve economics without changing provider.
Put new capacity elsewhere without forcing migration.
Move when qualified economics and evidence support it.
No candidate safely satisfies the workload constraints.
Prove the decision before giving Zyvrek control.
Production control is not the starting point. Each stage earns the right to progress to the next.
Find qualified opportunity
Understand workload economics and determine whether a meaningful opportunity exists without touching production.
Test persistence
Zyvrek makes current recommendations without executing them, measuring recurrence, availability and qualification over time.
Measure reality
Run a limited set of non-critical workloads and measure realised economics, reliability and fallback behaviour.
Scale only after proof
Broader execution is considered only after customer evidence and production outcomes support it.
Built to reject fake savings.
Zyvrek combines market observations, workload constraints, customer economics and performance evidence before recommending a placement decision.
normalized compute-market observations across Zyvrek’s historical and continuously collected market intelligence.
Modeled is not verified.
Zyvrek separates modelled opportunity from customer-verified results. Customer telemetry, economics and operational constraints must pass validation before an opportunity can graduate toward a pilot.
Start with your data, not your production environment.
Zyvrek evaluates workload economics, constraints and performance against qualified compute alternatives. Where sufficient historical market evidence overlaps your workload history, we replay past decisions. Where it does not, we can validate the opportunity through a read-only live shadow study.
Compute, commitments, credits, storage, network and marginal avoidable cost.
GPU, runtime, data, region, security, reliability, latency, restartability and portability.
What is genuinely routable, not merely cheaper on a public price page.
Qualified opportunity, exclusions, confidence, recurrence and recommended placement action.
Tell us about your compute estate.
Enough to qualify the opportunity, nothing sensitive at this stage.
Stop guessing where compute should run.
Start with a read-only evaluation. If there is no meaningful qualified opportunity under your real constraints, there is nothing to buy.