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AI workloads on Hivenet
Run open-source models, RAG, and production AI workloads on the right infrastructure path.
Hivenet gives teams managed inference endpoints, GPU and CPU compute, private AI support, and storage paths for AI workloads that need reliable performance, predictable spend, and practical sovereignty on Policloud-backed infrastructure.
Open-source models
Foundational model workloads
Managed inference
GPU/CPU compute
RAG
Fine-tuning
Model hosting
S3-compatible storage
France, UAE, and US deployment paths
What teams actually run on Compute.
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.
Managed endpoint
I want to replace or reduce API calls.
Use Hivenet Inference API for OpenAI-compatible endpoints serving open-source and foundational models without operating the stack yourself.
Raw infrastructure
I want to run my own AI stack.
Use GPU/CPU rental with Hivenet when your team wants RTX 4090, RTX 5090, or vCPU instances for vLLM, TGI, SGLang, llama.cpp, PyTorch, notebooks, or custom pipelines.
Private AI
I need help building AI on sensitive data.
Use Private AI when the project needs model selection, data preparation, deployment planning, or a guided path around privacy, residency, and business requirements.
Data and storage
I need somewhere to keep datasets and documents.
Use S3-compatible storage for datasets, document stores, backups, media, generated outputs, and AI pipeline artifacts.
AI workloads Hivenet is built to support.
Production inference
Serve foundational models for production tasks such as summarization, structured extraction, classification, support automation, code assistance, and internal tools.
RAG
Build retrieval-augmented generation workflows that connect models to your documents, knowledge base, support content, or internal data.
Model hosting
Host model endpoints on managed inference or self-managed compute, depending on how much of the serving layer your team wants to operate.
Fine-tuning and experiments
Run notebooks, LoRA or QLoRA jobs, model tests, and adaptation workflows on GPU instances your team controls.
Structured extraction
Extract dates, entities, categories, fields, and structured outputs from documents, messages, records, and business workflows.
Agentic workflows
Build AI workflows that use retrieval, tool calls, and controlled execution. Hivenet can help scope the infrastructure, data, and model path when the workflow needs careful design.
Run model workloads where they fit the job.
Hivenet focuses on practical model workloads rather than treating every task as a frontier-model problem. Start with the model family, then choose managed inference or GPU/CPU rental based on the operating model your team wants.
Qwen
Strong starting point for structured extraction, RAG, multilingual tasks, and production workflow automation.
Llama
Widely adopted model family for RAG, summarization, assistants, internal tools, and model-serving experiments.
Mistral
Useful for instruction-following, summarization, tooling, and European AI workloads where open deployment matters.
DeepSeek distilled models
Suitable distilled variants can support reasoning-style workflows when the model size fits the hardware and latency target.
Smaller efficient models
Use Falcon, Gemma, Phi, and similar model classes when cost-performance and throughput matter more than maximum model size.
Choose how much of the AI stack you want to operate.
Need
Best path
What Hivenet handles
What your team handles
OpenAI-compatible endpoint
Inference API
Endpoint, serving layer, replicas, metrics, region placement
Prompts, application logic, evals, integration
Full control over the stack
GPU/CPU rental
GPU/CPU infrastructure, billing, region options
Model server, framework, scaling, observability, dependencies
AI system on sensitive data
Private AI
Guided planning, model/data/deployment support
Business requirements, data owner decisions, review, and adoption
RAG on private documents
Inference API + S3 storage / Private AI
Model endpoint and storage path where supported
Document quality, permissions, retrieval design, evals
Fine-tuning or experiments
GPU/CPU rental
GPU/CPU infrastructure
Training stack, datasets, notebooks, checkpoints
Swipe left to see more
Match the workload to the right economics.
AI costs grow quickly when every task uses a frontier API or oversized infrastructure. Hivenet helps teams test which workloads fit foundational models, dedicated endpoints, RTX GPU compute, or a guided Private AI path.
Managed inference
Predictable dedicated capacity
Per-replica pricing works well for steady production workloads that need cost visibility and regional placement.
GPU/CPU rental
Full-stack control
Rent RTX 4090, RTX 5090, or vCPU instances when your team wants to operate the model server and tune the environment directly.
Storage
Datasets and retrieval data
Use S3-compatible storage for documents, datasets, model inputs, generated outputs, and AI pipeline artifacts.
Private AI
Guided support for harder projects
Work with Hivenet when the workload needs stronger data handling, architecture planning, or custom deployment support.
Infrastructure you can trust for production AI workloads.
Hivenet AI workload paths run on Policloud-backed infrastructure designed for reliable performance, predictable spend, and clear regional deployment. The value is not hardware ownership as a claim. The value is an enterprise-grade infrastructure path built to support serious workloads.
Policloud-backed capacity
Modular infrastructure gives Hivenet a practical way to place capacity closer to energy, region, and workload demand.
Enterprise-grade reliability
Hivenet is built for workloads where predictable performance, stable access, and operational transparency matter.
Standard interfaces
Work with familiar APIs, SSH, S3-compatible tools, and OpenAI-compatible patterns where supported.
Practical sovereignty
Deployment paths, standard tools, and clear operating models make location, access, and exit easier to explain.
Built for production AI problems.
Hivenet's AI workload paths are designed around the jobs teams already run: document automation, internal knowledge tools, extraction, summarization, support workflows, and model experiments.
Example workload
Document extraction at production volume
A business automation team uses a dedicated Qwen endpoint in France for part of a production extraction workflow.
Example team
AI teams with growing API spend
Strong fit for teams already spending meaningful money on production LLM APIs and looking for predictable dedicated capacity.
Find the right AI product path.
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.
Private AI
Guided AI projects for sensitive data, custom deployments, and harder business workflows.
S3-compatible storage
Object storage for datasets, documents, backups, and AI pipeline files.
FAQ
Common questions
Bring one AI workload to Hivenet.
Share the workload, model needs, data path, region requirements, and cost target. We'll help you choose between managed inference, GPU/CPU rental, Private AI, and storage.
