Key facts
| Deployment | On-prem, private cloud or air-gapped options exist |
| Model access | 30+ models behind one OpenAI-compatible API |
| Identity | SSO and per-user access controls are standard requirements |
| Audit | Prompts, tool calls and administration events should be logged |
| Data residency | Region selection plus in-boundary deployment |
| Endpoint status | Chat, streaming, tools, JSON mode, embeddings, RAG and agents live |
| Coming soon | Audio, image, moderation, files, batch, fine-tuning, assistants and responses |
| SLA | Published terms, SLA and status page |
TL;DR
- On-prem keeps prompts and data inside your boundary.
- Insist on SSO, audit logs and usage controls, not just a chat interface.
- One OpenAI-compatible API simplifies application integration.
- Plan model updates and patching before rollout, not after.
- Private cloud is often the practical mid-point between local and SaaS.
How it works, step by step
- Define the users, data classes and compliance requirements.
- Choose the deployment boundary: on-prem, private cloud or air-gapped.
- Confirm identity integration, audit logging and access controls.
- Validate model quality on real tasks with an evaluation set.
- Pilot with one department and measure adoption and support load.
- Document data flows, retention and acceptable-use rules.
- Plan capacity, updates and a scaling path before broad rollout.
Try it yourself
Open the private LLM deployment estimator →
Why enterprises move off public chat tools
Public chat services are productive and convenient, but they place prompts, pasted documents and generated output outside the organisation's boundary. For regulated teams that is often the end of the discussion: the data classes involved simply cannot be processed there.
The second driver is governance. A private alternative can be connected to the corporate directory, constrained by role-based access, and logged for audit. That makes AI usage observable and revocable, which matters when the same tool will touch contracts, code and customer records.
What to require from a private alternative
Treat the chat window as the smallest part of the evaluation.
- Deployment boundary: on-prem hardware, a private cloud region, or a fully air-gapped environment.
- Identity: SSO with your directory, plus per-user and per-group permissions.
- Audit: request, tool and admin logs with retention and access rules.
- Model catalogue: the models your teams need, behind one API, with documented versions.
- Integration: an OpenAI-compatible surface so internal apps reuse the same interface.
- Operations: a clear update path, SLA and support model.
Missing identity or audit capability turns a privacy project into a shadow IT project.
Rollout and operations
Start narrow. Pick one department with a concrete task, agree evaluation criteria, and run a time-boxed pilot with usage measurement. Most adoption problems are workflow problems, not model-quality problems, so involve the people doing the work early.
Then scale deliberately: capacity headroom, an update cadence, a named owner and a review of what data is being indexed. Plugsky provides an OpenAI-compatible API with 30+ models, region selection plus VPC, on-prem and air-gapped deployment, and published terms, SLA and status. Chat, streaming, tools, JSON mode, embeddings, RAG and agents are live; audio, image, moderation, files, batch, fine-tuning, assistants and responses are coming soon. See pricing for plan details.
Honest comparison
| Concern | On-premise local | Plugsky private deployment | Public chat service |
|---|---|---|---|
| Data path | Stays inside your network | Region choice plus private options | Leaves your network |
| Identity | Your directory and controls | SSO and platform controls | Provider accounts |
| Audit | Your logs and retention | Platform logs plus your own | Limited to provider terms |
| Models | What fits your hardware | 30+ models on one API | Provider catalogue |
| Operations | You run everything | Managed or co-managed | Provider-managed |
Frequently asked questions
What does on-premise mean for AI?
Inference runs on hardware you control, inside your network, so prompts, documents and outputs do not leave your boundary. It can be fully air-gapped or connected only to internal systems.
Do we lose model quality compared with public tools?
Some top-tier models are not available for local hosting, but the open-weight ecosystem is strong. Choose the best model that fits your hardware and validate it on your own tasks.
What about single sign-on?
Treat SSO as a hard requirement. A private chat tool without directory integration creates shadow accounts and undermines the access controls the project was meant to provide.
How do we audit usage?
Log requests, model and version, tool calls and administrative changes, with defined retention and access. Audit logs are often the deciding factor in procurement reviews.
Is air-gapped deployment realistic?
For some regulated environments, yes. It requires a deliberate update process for models and runtimes, because nothing can be fetched automatically.
What is the fastest path to a pilot?
Use a private cloud deployment with region selection, SSO and audit logging, run one department for a set period, and expand only after measuring usage and support load.
Can we mix on-prem and hosted models?
Yes. Keep sensitive workloads in-boundary and route permitted tasks to a managed endpoint. One OpenAI-compatible interface keeps both paths usable from the same application.