The Problem with Cloud AI APIs
Every prompt sent to a public LLM API is data leaving your network. For a casual chatbot, that's fine. For anything that touches customer records, contracts, financial data, medical information, or proprietary research — it's a non-starter.
Regulators have noticed. GDPR Article 28, NIS2, and sector-specific rules increasingly require organizations to know exactly where their data is processed. "We trust the API provider" is not a compliance answer.
The alternative — building your own inference platform — typically means months of engineering, a Kubernetes cluster, a DevOps team, and ongoing maintenance most organizations don't have the appetite for.
We built Paralon Enterprise to remove that trade-off.
What Paralon Enterprise Is
Paralon Enterprise is the same orchestration platform that powers our public network — deployed entirely on your infrastructure. You get:
- An OpenAI-compatible API running on your hardware
- A dashboard for fleet management, monitoring, and team controls
- A lightweight agent that turns any GPU machine into an inference node
- Zero external calls — no telemetry, no model weights phoning home, no inference data leaving your network
You install. Your team uses. Nothing crosses your perimeter.
How It Works
1. Install the Agent
The agent runs on Linux (NVIDIA GPUs) and macOS (Apple Silicon). One command per machine. It connects outbound only — no inbound ports, no VPN tunnels, works behind NAT and corporate firewalls.
2. Hardware Auto-Registers
The agent detects the GPU model, VRAM, CPU cores, and memory. It reports specs to your private control plane. The node is now part of your inference fleet.
3. Models Allocated Intelligently
The orchestration engine matches model size and architecture to compatible hardware. A 70B model lands on the H100 cluster; a smaller model gets routed to your Mac mini fleet. Load is balanced across healthy workers in real time.
4. Teams Get OpenAI-Compatible Access
Each team gets API keys, quotas, and rate limits. Your developers point their existing OpenAI SDK code at your private endpoint and ship.
What Runs on It
Today, Paralon Enterprise serves:
- LLM inference via vLLM (Linux/NVIDIA) and Ollama / llama.cpp (Apple Silicon)
- Generic Docker workloads — STT, TTS, custom training jobs, validator nodes
Anything you can package as a Docker container can run on a Paralon node.
Heterogeneous Hardware Support
Most "enterprise AI platforms" assume a homogeneous cluster of identical NVIDIA GPUs. Reality is messier — organizations end up with H100s in the data center, RTX cards under engineers' desks, Mac Studios in design teams, and DGX systems in R&D.
Paralon treats them all as a single fleet:
- NVIDIA GPUs — H100, A100, A6000, L40S, RTX 5090 / 4090 / 3090 / 4060 Ti, V100
- Apple Silicon — M2 Max, M3 Pro, M4
The orchestration layer handles model placement automatically. You don't tune per-GPU configs. You don't write deployment YAMLs.
Data Sovereignty Is Not a Feature — It's the Default
The architecture has no path for data to leave your network. There's no telemetry endpoint we silently call. There's no model registry that fetches weights through our servers at inference time. There's no usage analytics phoning home.
When you deploy Paralon Enterprise:
- Models are downloaded once during setup, from sources you specify (HuggingFace, your private registry)
- Inference happens on your hardware
- Logs stay on your hardware
- Audit trails stay on your hardware
For organizations subject to GDPR, NIS2, or internal data residency policies, there's no data-flow diagram to draw. Data simply doesn't move.
The platform supports fully air-gapped deployment for environments with no outbound internet access at all.
Deployment Tiers
We offer three tiers:
- Team — for small teams bringing AI in-house. Up to 10 GPU nodes, self-hosted on your infrastructure, full inference pipeline, dashboard, and email support.
- Business — for organizations scaling AI across teams. Unlimited nodes, custom branding, multi-team access controls, usage analytics, and priority support.
- Enterprise — for regulated, mission-critical deployments. Dedicated support engineer, SSO, audit logs, compliance reports, air-gapped deployment option, custom SLA, and on-site onboarding.
Pricing scales by deployment size, not per token or per GPU-hour. You pay a fixed license — your inference volume can grow without your bill changing.
Who This Is For
Paralon Enterprise is built for organizations that:
- Cannot send sensitive data to public LLM APIs (banking, healthcare, legal, defense, pharma)
- Have existing GPU investments they want to fully utilize
- Need an OpenAI-compatible API but in their own perimeter
- Want to avoid Kubernetes complexity for an internal AI platform
- Operate under GDPR, NIS2, or industry-specific data residency rules
Our first university partnership — with Universitatea Româno-Americană — uses Paralon to give research staff access to GPU compute without exposing research data outside the institution.
What's Not in the Box
We're upfront about what Paralon does and doesn't do.
Paralon Enterprise handles: orchestration, model placement, load balancing, agent management, monitoring, team access controls, and the OpenAI-compatible API layer.
Paralon Enterprise does not include: the GPU hardware (you bring it), foundation model training (we serve, not train), data labeling tools, or vector database. We integrate with what you already have.
Get a Demo
If you're evaluating self-hosted AI infrastructure, we'd rather show than tell. Email [email protected] with a short note about your environment — GPU mix, target workloads, compliance requirements — and we'll set up a 30-minute walkthrough.
Run AI on your terms. On your hardware. With your data staying exactly where it should.
