Dedicated Cloud
A separately qualified service environment
Evaluate a dedicated deployment when you need a defined service boundary without operating the underlying facility yourself.
Explore Dedicated CloudPACINFRAX · ENTERPRISE
Enterprise inference. Your operating boundary.
Bring model decisions, data requirements and operational responsibility into one deployment plan. Compare cloud and on-premises paths while keeping token-based inference in view.
Compare model references or prepare your deployment requirements locally. No request is submitted and no capacity is reserved.
Enterprise deployment modes are not currently enabled. Capacity, operating controls, locations and commercial terms require separate acceptance. This guide sends no request and reserves no resources.
THREE DEPLOYMENT DECISIONS
Choose where the workload runs. Define who operates it.
Dedicated Cloud, BYOC and On-Premises describe different ownership and access boundaries. Evaluate the data lifecycle and responsibility split alongside model quality, traffic and cost.
Bring Your Own Cloud
Your cloud account, an agreed service role
Evaluate BYOC when workloads must run in a customer-controlled cloud account and service access must be explicitly delegated.
Explore Bring Your Own CloudOn-Premises
Your facility and hardware lifecycle
Evaluate an on-premises deployment when facility, hardware and network operations need to remain within your organization’s operating boundary.
Explore On-PremisesCompare an operating boundary.
Showing Dedicated Cloud requirements. Selection is local and does not qualify a deployment.
Workflow overview
Dedicated Cloud: agree the boundary before the runtime.
- Customer application
- Approved cloud boundary
- Qualified model runtime
Keep responsibility explicit.
Evaluate a dedicated deployment when you need a defined service boundary without operating the underlying facility yourself.
Customer project authority and service operator authority stay separate.
| Decision | Responsibility to agree and verify |
|---|---|
| Hardware | The selected infrastructure provider owns physical hardware operations; the service and customer responsibilities require an agreed contract. |
| Runtime and scheduling | Assign model serving, patching, rollout and recovery to named service operators before activation. |
| Location and data scope | An explicitly approved provider and region; availability remains unverified. |
| Networking | Agree ingress, egress, private connectivity and administrative access with the selected provider. |
| Key custody and access | Define who holds encryption keys and which workload identities may use them; dedicated capacity alone establishes no custody guarantee. |
| Updates and rollback | Agree version pinning, compatibility checks, maintenance windows and rollback ownership. |
| Incident ownership | Define provider, service and customer escalation boundaries; no support window or response time is currently offered. |
| Exit and deletion | Agree export formats, credential revocation, artifact deletion and retained financial records before purchasing. |
Proposed responsibility questions, not a signed operating agreement. Data location, isolation and access must be verified for the exact deployment.
ENTERPRISE · USD COMMERCIAL SCOPE
Choose the buying model. Then the placement.
Separate model input/output token units from reserved resources, storage, transfer and any contracted platform or support charges.
Token-based inference remains part of the platform direction. Model input and output usage, capacity commitments and service fees describe different obligations. Reference prices do not establish a quote, invoice or entitlement.
Enterprise Token Pool is a proposed committed-usage service mode. Dedicated Cloud combines a capacity scope with platform and support terms; Enterprise Platform software can run in BYOC or On-Premises placements. A token commitment is not a physical isolation boundary. Every option needs approved USD terms and a separate data policy.
Before connecting a workload
- Exact provider, region and runtime configuration
- Isolation evidence for the intended workload
- Provider/service incident and maintenance responsibilities
- Model rights, artifact provenance and versioned evaluation evidence
- Data retention, diagnostics, revocation, export and recovery policies
- Named operating parties and accepted commercial/support scope
No certification, zero-data-retention policy, uptime, response time, savings or region availability is established by choosing a deployment mode.
ENTERPRISE PLATFORM SCOPE
Define the controls around every workload.
These modules describe requirements to review for an enterprise deployment. The public gateway, enterprise policy enforcement and operational services are not enabled; a module description is not control acceptance.
Gateway and project authority
Map departments to explicit organization/project roles, workload credentials and quota policy. Customer access never grants operator authority. Check the documented API subset rather than assuming every OpenAI parameter is supported.
Inspect the supported local contract →Data policy and guardrail evaluation
Define permitted data classes, processing locations, retention and diagnostic access. Evaluate PII handling and guardrails with approved test cases, including misses and false positives. No active PII filter, zero-data-retention policy or compliance certification is claimed.
Review data handling requirements →Model and adapter governance
Pin the model revision, runtime and tokenizer. Compare multiple models against one workload; LoRA adapters require their own rights, provenance, base-model compatibility and access boundary. Adapter training, loading and production routing remain unavailable.
Review adaptation requirements →Usage, budgets and operations
Separate estimated, pending and verified usage. Agree budget exposure, metering, rate revision and incident ownership. Unknown execution remains unresolved: disconnect or cancellation does not settle spending, and a status screen does not prove operational health.
Compare usage and commercial scope →GLOBAL ENTERPRISE WORKLOADS
Start with the decision your application must support.
Illustrative buyer jobs, not deployed customer solutions. Each needs an accepted model, data policy, operating boundary and representative evaluation.
Regulated financial and customer workflows
Evaluate document review, service assistance and information retrieval with source grounding, role-aware access and accountable human review. Define prohibited decisions and escalation before using regulated or personal data.
Enterprise AI products
Build a workload specification for an internal copilot or customer-facing AI feature. Compare model revisions, failure behavior and cost under the product’s actual traffic; integration and commercial delivery require separate acceptance.
Realtime voice and chat
Measure end-to-end p95 latency, text time to first token and completion quality under representative demand. Voice requires separately accepted speech capabilities and turn-handling tests; the current local gateway is text-only. No latency threshold or voice service is promised.
FROM WORKLOAD TO ACCEPTANCE
A proof of concept needs a decision, not just a demo.
Scope the workload
Pin the task, model revision, representative traffic, data classification and allowed locations. Name the customer decision owner and operating parties.
Agree the evidence
Define quality, p95 time to first token, completion latency, input/output token cost and failure criteria. Establish the baseline and test conditions before comparison.
Test the boundary
Use an authorized dataset and accepted capacity. Include overload, incomplete streams, lost cancellation acknowledgements, access revocation and recovery.
Review or stop
Reject unauthorized data movement, inadequate task quality, unbounded cost exposure or unresolved execution state. Agree thresholds before testing; no POC runs on this website.
ILLUSTRATIVE WORKLOAD
An internal document assistant
A team wants answers grounded in approved documents with source references. First decide who may read each document, where processing may occur, and how answers will be assessed.
- Baseline and task quality
- Unknown. Define a versioned question set, citation rubric and failure categories before testing.
- Traffic and latency
- Unknown. Record prompt/output lengths, concurrency and arrival pattern; measure p95 time to first token and full completion latency.
- Token cost
- Unknown. Track input and output separately with model/rate revision; infrastructure and support costs need their own scope.
- Failure and stop criteria
- Agree quality, data, cost and recovery thresholds. Stop admission on unresolved authority or bounded-exposure breaches; a cancel request does not prove compute stopped.
This is an example evaluation design, not a customer deployment, measured benchmark or performance commitment.
Prepare the requirements for your next conversation.
Use the local workload planner to organize fixed categories without entering documents, prompts or contact information. Nothing is sent to a sales team.