Key facts
| Service model | Build managed AI offerings on an OpenAI-compatible API under your own brand |
| Multi-tenant control | Separate API keys or endpoints per client for isolation and attribution |
| Usage visibility | Usage analytics per key for chargeback and capacity planning |
| Private options | VPC, on-prem and air-gapped deployments for regulated clients |
| Model range | 30+ models behind one API, from small to frontier tiers |
| Pricing model | Flat monthly self-serve plans; no per-token billing on self-serve |
| Evaluation path | Free plan with 2 free AI models, then a 14-day full-access trial |
| Endpoint roadmap | Audio, images, moderation, files, batch and fine-tuning are coming soon |
TL;DR
- Sell an AI service line without building or operating GPU infrastructure.
- Isolate clients with separate keys or endpoints and meter usage per key.
- Offer private or air-gapped options to regulated clients on the same codebase.
- Flat monthly plans make your margin easier to model than per-token pass-through.
- Start free, productize with one repeatable deployment pattern, then scale.
How it works, step by step
- Define the service catalogue: assistant, document search, workflow agents or a packaged industry solution.
- Design a tenant model - separate API keys per client, or dedicated deployments for regulated accounts.
- Build one reference architecture with RAG, logging and a support runbook you can repeat.
- Decide which capabilities you manage centrally and which stay client-specific.
- Price the service around outcomes and support, using flat platform costs for the underlying usage.
- Run a pilot with two clients, measure support load, then templatize the onboarding.
- Add private or air-gapped deployment for accounts that cannot use shared cloud regions.
Try it yourself
Open the LLM cost calculator →
Package AI as a service line
MSPs win by productizing, not by rebuilding platforms. Start from services you already sell and attach AI to them:
- Knowledge assistant: managed search and Q&A over the client's documents, with citations.
- Workflow automation: agents that read and update ticketing, CRM or finance systems.
- Document operations: summarization, extraction and routing for heavy paperwork clients.
- Industry pack: a repeatable build for one vertical where you already have references.
Each package needs a fixed architecture, an onboarding checklist and a support runbook.
Multi-tenant architecture with keys and metering
Keep tenants separated at the credential layer: issue a distinct API key per client and environment, store keys in your secrets manager, and never share a production key across accounts. Usage analytics per key give you chargeback data and a signal for capacity planning.
For clients with stricter isolation needs, move that tenant to a dedicated endpoint or a private deployment. Because the API is OpenAI-compatible, the application code carries over unchanged.
Residency and private options for regulated clients
Banks, governments and healthcare clients will ask where data is processed. Plugsky supports region selection for cloud deployments and private modes: VPC hosting in the client's tenancy, on-prem installation and air-gapped environments. Document the data flow for each tenant and keep it in your proposal template.
See white-label AI API for the packaging angle and private AI endpoint for the isolation pattern.
Margin and support economics
Per-token pass-through makes client invoices unpredictable and your margin hard to defend. Flat monthly platform plans turn the underlying cost into a fixed line item, so you can price on seats, documents or outcomes. Budget for support: the fastest path to unhappy clients is an assistant that answers confidently from stale documents, so make index refresh part of the managed service.
Honest comparison
| Capability | Plugsky for MSPs | Building a GPU stack | Reselling a consumer AI tool |
|---|---|---|---|
| Infrastructure | Managed OpenAI-compatible API | You buy, host and operate GPUs | Vendor-controlled |
| Tenant isolation | Keys or endpoints per client; private deployment options | You design isolation | Usually account-level only |
| White-label delivery | API behind your brand and support process | Full control | Limited branding options |
| Usage attribution | Per-key usage analytics | You build metering | Vendor reporting |
| Cost model | Flat monthly plans on self-serve | Capital plus operations | Per-seat markup |
| Time to market | Weeks | Quarters | Days, with weak differentiation |
Frequently asked questions
Can we present the AI under our own brand?
Yes. The API sits behind your application and support process, so clients interact with your interface and your service agreements. Review the current white-label scope with the Plugsky team before you commit to a client contract.
How do we keep client data separate?
Issue distinct API keys per client and environment, store them in your secrets manager, and use dedicated endpoints or private deployments where stricter isolation is required.
How do we bill clients?
Price on your own units, such as seats, documents or workflows. Flat monthly platform plans make the underlying usage predictable, so margin does not swing with token volume.
What about clients who need on-prem?
Plugsky supports VPC, on-prem and air-gapped deployments. Build the pilot in the cloud, then move the same OpenAI-compatible calls into the client environment.
Do we need ML engineers?
No. Integration work is standard API and retrieval engineering. The managed service layer - onboarding, index refresh, evaluation and support - is where your team adds value.
How do we start cheaply?
Use the free plan with two free models to build the first demo, then the 14-day full-access trial to validate quality on client-like data before signing a paid plan.
Which endpoints can we include today?
Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live. Audio, images, moderation, files, batch, assistants, responses and fine-tuning are coming soon.