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Launch self-serve GPU compute for demanding work.
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Compute - Private AI
Build AI on sensitive data with a path your team can explain.
Hivenet helps teams design and run private AI workloads using the right mix of managed inference, GPU compute, storage, and engineering support. Bring your data, model requirements, region needs, and business workflow. We'll help map the path from first test to production.
RAG
Document extraction
Internal assistants
Model hosting
Private data workflows
Open source and foundational models
Policloud-backed infrastructure
France, UAE, and US deployment paths
A guided path for AI workloads that need more than an API key.
Some AI projects are too sensitive, custom, or operationally important to drop into a generic API. Private AI with Hivenet helps teams align the model, data, infrastructure, deployment region, and operating model before moving toward production.
Model selection
Match the workload to an open-source or foundational model, endpoint path, GPU configuration, or managed inference option.
Data preparation
Structure documents, datasets, or retrieval material so the AI system has the right context from the start.
Infrastructure path
Choose between Hivenet Inference API, GPU/CPU rental, S3-compatible storage, or a private deployment path based on the workload.
Application support
Build the AI workflow around a real use case: search, document review, extraction, assistants, support automation, or decision support.
Rollout planning
Move from first test to controlled deployment with technical review, workload testing, and support planning.
Use AI where generic tools are not enough.
Internal knowledge assistants
Build assistants over internal documents, policies, support material, or technical knowledge, with a clearer infrastructure and data path.
Document review and extraction
Extract fields, summaries, dates, entities, classifications, and structured outputs from contracts, invoices, records, or operational documents.
RAG on private data
Connect foundational models to business documents, retrieval workflows, and controlled storage paths.
Customer-facing AI products
Build AI features for users or customers when region, latency, quality, and deployment control matter.
Compliance and risk workflows
Support screening, review, classification, and monitoring workflows where data handling and auditability need careful design.
Research and decision support
Use AI to summarize, search, compare, or interpret internal datasets and documents for expert teams.
From workload review to controlled deployment.
Private AI starts with one concrete workload. We look at the business goal, data path, model fit, quality bar, infrastructure needs, and region requirements before recommending a build path.
Technical consultation
Share the use case, data type, user group, security constraints, and success criteria.
Model and infrastructure fit
Identify whether the workload belongs on Inference API, GPU/CPU rental, S3 storage, or a private deployment path.
Data preparation
Prepare documents, datasets, retrieval material, or application data for testing and evaluation.
Focused test
Test a defined workload with a specific model, dataset, region, and cost target.
Production plan
Move forward when quality, latency, cost, security, and the operating model make sense.
Private AI connects the right product paths.
Need
Hivenet path
Best fit
Managed endpoint for foundational models
Inference API
You want an OpenAI-compatible endpoint without operating the serving layer
Full stack control
GPU/CPU rental
Your team wants GPU/CPU instances and manages the model server, framework, and dependencies
Datasets and retrieval material
S3-compatible storage
You need object storage for documents, datasets, model inputs, generated outputs, or pipeline files
RAG on private data
Private AI + Inference API + S3 storage
You need help designing retrieval, data preparation, model serving, and evaluation
Custom AI workflow
Private AI
You need guidance on model choice, data path, application logic, deployment, and rollout
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Start with safe data paths, then choose the AI stack.
Private AI projects often involve data that cannot be treated casually. Hivenet helps teams design the workload around practical control over location, infrastructure path, access model, operational interface, and exit route.
Location
Plan deployment paths across available regions, including France, the UAE, and the US, depending on the product and workload.
Infrastructure path
Use Policloud-backed infrastructure paths for suitable AI, compute, and storage workloads instead of routing everything through default hyperscaler APIs.
Access model
Choose the right operating model: managed endpoint, GPU/CPU rental, storage workflow, or private deployment path.
Operational interface
Work with standard tools and interfaces where supported, including OpenAI-compatible API patterns, SSH, S3-compatible tools, and documented APIs.
Exit route
Standard interfaces, open-source model paths, and clear data paths reduce the risk of building around a proprietary dead end.
Enterprise-grade infrastructure for private AI workloads.
Private AI with Hivenet runs on infrastructure paths designed for sensitive workloads that need reliable performance, cost visibility, and regional deployment. The value is not hardware ownership as a claim. The value is a trusted platform path your team can evaluate and explain.
Policloud-backed capacity
Modular infrastructure gives Hivenet a practical way to place capacity closer to energy, region, and workload demand.
Reliable workload path
Hivenet helps teams match the AI workload to the right endpoint, instance, storage, and deployment model.
Standard interfaces
Use familiar interfaces such as OpenAI-compatible APIs, SSH, S3-compatible tools, and documented APIs where supported.
Practical sovereignty
Deployment paths, standard tools, and clear operating models make location, access, and exit easier to discuss with customers and internal stakeholders.
Test before you commit.
A private AI project should be evaluated against your real data, quality bar, latency target, cost target, and deployment constraints. The goal is to prove the workflow before expanding it.
Quality evaluation
Test the model against real prompts, documents, expected outputs, and review criteria.
Cost and performance fit
Compare managed inference, GPU/CPU rental, and private deployment options against the expected workload volume.
Data and access review
Check what data is used, where it lives, who can access it, and how the system should be operated.
Production readiness
Review monitoring, support needs, rollout risk, user adoption, and procurement requirements before scaling.
Priced around the workload.
Private AI pricing depends on the use case, data scope, model path, infrastructure requirements, region, support needs, and rollout plan. Start with a workload review so Hivenet can recommend the right path before quoting a deployment.
Self-managed components
GPU/CPU rental uses published GPU and CPU pricing where available.
Managed inference
Inference API uses per-replica pricing for dedicated endpoints once the public pricing path is approved.
Private AI project support
Pricing can include workload review, architecture planning, implementation support, deployment assistance, and rollout guidance.
Built for sensitive and business-critical AI work.
Hivenet's Private AI path is designed for teams working with internal knowledge, business documents, regulated workflows, and AI systems that need careful deployment decisions.
Example workload
Private document workflows
A team brings internal documents, extraction targets, and region requirements. Hivenet helps test model quality, data preparation, infrastructure path, and rollout fit.
Example team
Teams that cannot send everything to a default API
Strong fit for companies with sensitive data, customer obligations, jurisdiction needs, or workflow-specific quality requirements.
Explore the product paths behind Private AI.
Hivenet Inference API
Managed OpenAI-compatible endpoints for foundational models.
GPU/CPU rental
GPU and CPU instances for teams that want to operate their own AI stack.
S3-compatible storage
Object storage for datasets, documents, backups, and AI pipeline files.
AI workloads
Route production AI workloads to the right Hivenet product path.
FAQ
Common questions
Bring one private AI workload to Hivenet.
Share the use case, data type, model needs, region requirements, and success criteria. We'll help you decide whether the right path is managed inference, GPU/CPU rental, S3 storage, or a private AI project.
