Dedicated Model Hosting: Single-Tenant Infrastructure for Production Inference

Dedicated model hosting eliminates the performance variability, security risks, and unpredictable costs of shared inference infrastructure. With Hyperbolic’s dedicated hosting solution, AI teams can now get reserved GPU capacity, single-tenant isolation, and predictable spend without managing their own hardware.

Operational limitations of shared inference

Shared inference endpoints result in four significant disadvantages for production applications.

Tail latency instability. Shared endpoints result in multi-tenant scheduling contention, queueing variance, and throttling create latency jitter that cannot be fully solved by retries.

Throughput variability from noisy neighbors. When other tenants spike traffic, you can see drops in tokens per second, increased time to first token, and reduced concurrency even if your own demand is steady. For real-time systems and agentic pipelines, this results in an inconsistent and degraded user experience for your production application.

Expanded security surface area. Even with strong controls, shared environments increase risk around isolation boundaries, operational access paths, and audit scope. For sensitive prompts or regulated data, the risk profile can be unacceptable.

Unpredictable spend at steady scale. Usage-based pricing is attractive for bursty workloads, but steady high-volume inference often benefits from reserved capacity. Variable billing complicates budgeting, procurement, and unit economics modeling precisely when the business needs clarity.

Service definition

Hyperbolic’s dedicated model hosting is a single-tenant inference infrastructure provisioned for one customer. The environment includes dedicated GPUs and servers reserved exclusively for the customer, isolated networking, and a private, customer-only API endpoint. Customers deploy their own fine-tuned or trained models, or selected open-source models, on a dedicated stack, meaning no shared compute resources or serving layer (e.g., GPUs, nodes, runtime processes, or caches) with other customers.

Benefits for production teams

Dedicated hosting delivers five key benefits for production AI teams:

Performance isolation. Performance isolation improves tail behavior by removing contention and reducing queueing variance. This supports stable tokens per second, predictable concurrency, and consistent time to first token under sustained load.

Cost predictability. Cost predictability comes from reserved capacity. Dedicated infrastructure converts inference into a stable operating cost rather than a variable spend line item that scales unpredictably with usage spikes.

Enterprise reliability. Enterprise reliability. Enterprise reliability is backed by guaranteed uptime targets and dedicated support to address any issues that arise during critical workloads.

Security and compliance readiness. Our dedicated hosting supports HIPAA, SOC2, and GDPR requirements by eliminating shared compute and storage layers.

Controlled scaling. Scaling becomes controlled and intentional. Capacity growth is planned through hardware additions and configuration changes rather than reactive throttling or uncertain multi-tenant availability.

Shared Endpoints VS Dedicated Hosting

Comparison Factor

Shared endpoints (multi-tenant inference)

Dedicated hosting (single-tenant inference)

Performance determinism (p95/p99, TTFT)

Variable due to cross-tenant contention, queueing, throttling

Stable tail behavior from reserved capacity and isolated scheduling

Throughput and concurrency

Noisy-neighbor effects can reduce TPS and concurrency unpredictably

Predictable TPS and concurrency aligned to provisioned hardware and tuned serving config

Isolation and security boundary

Shared serving plane expands audit surface and blast radius

Dedicated serving plane and compute reduce shared surface area and simplify audit scope

Cost model

Usage-based; harder to budget at steady scale

Reserved capacity; predictable monthly spend and unit economics

Reliability and support

Best-effort posture is common; incidents can be fleet-wide

SLA-backed uptime targets with dedicated operational response and escalation

Control and configurability

Limited knobs; standardized policies

Customer-scoped tuning (engine config, batching, KV-cache behavior) and controlled change management

Included capabilities

The package includes:

Dedicated compute hardware reserved exclusively for the customer workload

Private networking and isolation with production-grade privacy expectations including no prompt logging

Model deployment using our proprietary inference engine, vLLM or SGLang exposed through a private customer-only API

Persistent storage to store model weights and embeddings, supporting reliable loading and stable serving behavior

Optional load balancing and DDoS protection that can be integrated using third-party solutions, such as Cloudflare where required

Support and SLAs with specific guarantees determined by deployment requirements, but structured around explicit uptime commitments and operational response expectations

Conclusion

Production inference is not just a deployment detail. It is a performance system, a reliability system, a security boundary, and a cost structure. Dedicated model hosting provides single-tenant infrastructure that makes inference behavior stable, auditable, and scalable, enabling teams to meet deterministic latency targets, keep full sovereignty over models, and operate with predictable spend as they grow.