Per outcome
Best for finite training, evaluation, data-processing and project-defined workloads.
ExampleFine-tune and reach an agreed evaluation threshold
- Defined acceptance criteria
- Clear project scope
- No infrastructure management
Pay for the result—not the uncertainty.
Traditional clouds charge for every GPU hour. Hyperfusion structures managed AI pricing around the completed task, giving product and finance teams a cost they can forecast.
Who carries the infrastructure risk?
Traditional cloudYou do
Idle GPU time · failed runs · orchestration · overages
Hyperfusion managed AIWe do
Defined workload · transparent scope · predictable commercial model
Turn a technical requirement into a commercial scope.
See how a workload becomes a commercial scope before you ever speak to sales.
i
Illustrative output only. Production pricing requires validation of model, volume, SLA and data requirements.
Scope an AI workload Live estimate
Commercial modelPer completed task
Indicative monthly band$2.4K–$3.1K
Recommended architectureManaged Qwen endpoint
Move variability away from your budget.
A simpler commercial model makes AI products easier to price, approve and scale.
Commercial considerationTraditional cloudHyperfusion
Pricing modelGPU hours and usageDefined workload or capacity
Cost predictabilityVariable and difficult to forecastScoped before production
Failed managed runsCustomer absorbs the computeCommercially structured around the outcome
ScalingCustomer orchestrates instancesManaged or reserved architecture
Billing transparencyMultiple technical line itemsWorkload-level commercial view
Different workloads need different forms of predictability.
Hyperfusion can combine task pricing, predictable monthly inference and reserved infrastructure.
Production products
Predictable monthly service
Best for steady inference workloads where finance teams need an operating envelope.
ExampleMultilingual support assistant at forecast monthly volume
- Managed scaling
- Performance target
- Regional endpoint
Reserved capacity
Best for ML teams that need dedicated GPU infrastructure and full runtime control.
ExampleDedicated H100 cluster for custom inference
- Single tenant
- Root access
- Managed operations optional
We take responsibility for infrastructure efficiency.
Hyperfusion optimizes the model, GPU allocation and operating layer so customers can focus on the product rather than resource overruns.
No bill-shock positioningUpfront workload scopeRegional performanceManaged optimization
Questions finance and engineering ask together.
Managed AI tasks can be structured around the outcomes or predictable service envelopes. Dedicated infrastructure is typically scoped as reserved capacity. The final model depends on workload characteristics.
Workload type, expected volume, model requirements, latency target, data residency, availability target and required management level.
Yes. Existing request patterns, average context length and peak concurrency can be used to map the application to an equivalent open-weight endpoint.
Yes. Dedicated endpoints, single-tenant clusters, support commitments and governance requirements are scoped through an enterprise agreement.
Bring the workload. Leave with a price and architecture.
Define the target outcome, production volume and operational requirements.