Key facts
| API surface | OpenAI-compatible /v1/chat/completions; change base_url and model name |
| Deployment | Plugsky cloud, your VPC, on-prem or air-gapped |
| Data residency | Region selection and sovereign deployment options |
| Models | 30+ models behind one API, including long-context and multilingual options |
| Access control | Scoped API keys and request logs; enterprise RBAC and SSO |
| Pricing | Flat monthly self-serve plans; see the live pricing page |
| Free tier | Free plan with 2 free AI models (plugsky-micro, plugsky-lite), no card |
| Contracting | DPA and SLA documents available for review |
TL;DR
- Keep student records, research data and unpublished work inside a boundary you control: region choice, VPC, on-prem or air-gapped.
- Evaluate residency, student privacy, auditability, model choice and exit before you commit.
- 30+ models behind one OpenAI-compatible API, so teaching tools and research pipelines do not change with deployment.
- Start on the free plan or the 14-day full-access trial, then move shared services to a private deployment.
- Separate departmental corpora and scope keys so access follows faculty, project and role boundaries.
How it works, step by step
- Map where student records, research datasets, course material and grant reporting live today and which systems are in scope.
- Write down residency, retention, student-privacy and access requirements, and who signs off on each.
- Shortlist deployment modes: managed cloud, VPC, on-prem or air-gapped, per faculty and dataset sensitivity.
- Run a pilot on the free plan or the 14-day full-access trial with a fixed set of real teaching and research questions.
- Score answers, citations, refusals, latency and cost on the same test.
- Complete security, DPA and SLA review, with exportable logs and a documented exit plan.
- Roll out faculty by faculty and re-run the evaluation after each model or deployment change.
Original data
Try it yourself
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What sovereign AI means for universities
Sovereign AI for universities means the models, prompts and retrieved data stay under your jurisdiction and institutional control. In practice that means a deployment you can place in your own region or campus network, with identity, logging and retention governed by the controls your institution already runs.
The business case is not secrecy for its own sake: it is keeping student records, research datasets, unpublished papers and grant material inside a boundary you can audit, while still using modern models through a standard API.
The five things to evaluate
Score every candidate on these five areas, and require evidence rather than claims:
- Residency and deployment: region choice, and whether VPC, on-prem and air-gapped options actually exist.
- Data handling: whether prompts and outputs are used for training, how long they are retained, and which subprocessors are involved.
- Access and audit: scoped API keys, RBAC and SSO for enterprise, and request-level logs you can export.
- Model choice and portability: how many models you can route between, and how hard it is to move away.
- Commercials and exit: predictable pricing, SLA terms, and a documented data-return path.
Deployment modes and their trade-offs
Cloud is fastest: a managed, region-based deployment with residency selection. VPC puts inference inside your own cloud account and network boundary. On-prem runs in your data center; air-gapped runs with no outbound connectivity at all. The trade-off is operational: the further you move from managed cloud, the more you own upgrades, capacity and monitoring.
Universities often start on managed cloud for evaluation, then move the same OpenAI-compatible workload to a private deployment when student or research data must stay inside institutional systems. Because the API surface does not change, teaching tools and scripts usually stay put.
A scorecard approach to procurement
Run a structured evaluation: define ten questions that represent real teaching and research work, test two or three models, and score answers, citations and refusals. Add a security review covering identity, key rotation, logging and data flow, then a commercial review covering pricing predictability, SLA and exit.
Begin on the free plan or the 14-day full-access trial, document the decision, and keep the evaluation set so you can re-run it after every model or deployment change.
Honest comparison
| Evaluation area | Plugsky | Vendor cloud-only | Building in-house |
|---|---|---|---|
| Residency | Region selection plus sovereign deployment options | Usually vendor regions only | Fully under your control |
| Deployment modes | Cloud, VPC, on-prem, air-gapped | Managed cloud only | Your infrastructure |
| Model choice | 30+ models behind one API | Single vendor catalogue | You host each model |
| Auditability | Scoped keys and request logs; enterprise RBAC and SSO | Vendor-defined logging | You build logging |
| Pricing | Flat monthly self-serve plans; see live pricing | Often per-token or per-seat | GPU plus operations cost |
| Time to production | Fast on managed cloud; private options for sensitive workloads | Fast but fixed residency | Months of build and ops |
Frequently asked questions
What makes an AI deployment sovereign?
Sovereign deployment keeps models, prompts and data under your jurisdiction and control: you choose the region, own the boundary, and keep identity, logging and retention under your existing governance.
Can we keep data inside our own country or network?
Yes. Plugsky supports region selection plus VPC, on-prem and air-gapped deployments, so inference and data can sit where institutional policy or regulators require.
Does Plugsky train on our data?
For strict requirements, choose a private or air-gapped deployment so content stays inside your environment. Review the current data-handling terms and DPA for cloud plans before rollout.
Is there a free option for teaching and evaluation?
Yes. The free plan includes two free AI models with no card, and the 14-day full-access trial lets you test more capable models before committing a budget line.
How do we avoid lock-in?
Plugsky is OpenAI-compatible: applications use a standard API, so you can change base_url and model name with minimal code changes, and keep your evaluation set to compare alternatives.
Can departments keep their own data separate?
Yes. Use per-faculty or per-project document collections with scoped keys, so retrieval and access stay within the groups you approve.
Can it support multilingual teaching and research?
Yes. Route between models on one API, including multilingual options, and keep language-specific evaluation questions in your test set before you commit.