Key facts
| API surface | OpenAI-compatible /v1/chat/completions; one integration for all models |
| Tenant isolation | Separate API keys or endpoints per tenant for attribution and control |
| Routing | Model routing lets one feature call different model tiers by task |
| Retrieval | Embeddings and RAG are live for per-tenant knowledge |
| Structured output | JSON mode keeps AI output inside your application schemas |
| Deployment | Cloud, VPC, on-prem or air-gapped for enterprise customers |
| Pricing model | Flat monthly self-serve plans; no per-token billing on self-serve |
| Endpoint roadmap | Audio, images, moderation, files, batch and fine-tuning are coming soon |
TL;DR
- Ship AI features with API calls, not GPUs, queues and model operations.
- Isolate tenants with separate keys or endpoints and meter usage per key.
- Route easy tasks to small models and hard tasks to frontier models.
- Offer residency and private deployment to close enterprise deals.
- Keep prompts, retrieval and schemas in your codebase where they belong.
How it works, step by step
- Choose one feature where AI changes the outcome, not just the demo, such as search, summarization or drafting.
- Wrap the model call behind your own service layer so provider, model and prompts can change without touching product code.
- Issue per-tenant or per-environment keys and record usage for attribution and limits.
- Add routing so routine requests use small models and complex ones escalate.
- Ground answers with tenant-scoped embeddings instead of putting customer data in prompts.
- Handle failures with retries, timeouts and a graceful degradation path.
- Add private deployment options when enterprise buyers require residency.
Try it yourself
Open the AI workload router simulator →
Build the feature, not the platform
Model infrastructure is a full-time product: serving, scaling, version pinning, GPU capacity and on-call rotations. Every hour spent there is an hour not spent on your actual product. An OpenAI-compatible API removes that layer while keeping your code portable, because the call shape is the same one your team already knows.
Keep everything that encodes your product's value in your own codebase: prompts, retrieval, schemas, evaluation sets and the UX around the output.
Multi-tenant keys and cost controls
Multi-tenancy is mostly credential design. Issue a distinct key per tenant or environment, store keys in your secrets manager, and use usage analytics per key for internal chargeback and quota enforcement. Never let one tenant's traffic share a production credential with another.
Where a customer needs stronger isolation, move that tenant to a dedicated endpoint or private deployment. The same requests keep working because the API surface does not change.
Routing and reliability
Not every request deserves the largest model. Route classification, extraction and short summaries to fast, inexpensive models, and reserve frontier models for reasoning-heavy tasks. Model routing lets you express that policy in one place instead of scattering model names through the codebase.
Add timeouts, bounded retries and a fallback model. Degrade the feature gracefully rather than failing the page, and measure error and fallback rates as product metrics.
Closing enterprise deals
Enterprise buyers ask about residency, isolation and key management before they ask about model quality. Plugsky supports region selection and VPC, on-prem and air-gapped deployment, with enterprise SSO and RBAC options for console access. Document these options once, then reuse the security appendix in every deal.
Honest comparison
| Capability | Plugsky | Building model infrastructure | Single-vendor AI API |
|---|---|---|---|
| Engineering focus | Your product features | Serving, GPUs, on-call | Your product, if compatible |
| Model flexibility | 30+ models behind one API | Whatever you deploy and maintain | Vendor catalogue only |
| Tenant isolation | Keys or endpoints per tenant; private deployments | Custom isolation work | Usually account-level |
| Reliability | Managed platform with status page | You build redundancy | Vendor-managed |
| Cost model | Flat monthly self-serve plans | Capacity planning and utilization | Per-token |
| Time to first release | Days | Quarters | Days, with migration risk |
Frequently asked questions
Do we need ML engineers to ship an AI feature?
No. Integration is standard API engineering: a service wrapper, keys, retrieval and evaluation. Keep that layer thin so models and prompts are configuration, not rewrites.
How should we handle per-tenant data?
Store tenant content in your own database, embed it per tenant, and pass only the minimum context into the model call. Never mix tenant vectors or contexts in one retrieval namespace.
How do we keep costs predictable for finance?
Self-serve plans are flat monthly with fair-use usage, so bills do not scale with token traffic. Use per-key analytics for internal chargeback. See the live pricing page for current plans.
What if a customer requires residency?
Region selection covers many needs, and VPC, on-prem and air-gapped deployments cover stricter requirements. The API stays OpenAI-compatible, so no product code changes.
Can we offer our own API on top?
Yes. Wrap the platform in your own service layer and expose the interface your customers expect. See the white-label API guide for packaging patterns.
Which endpoints are live for product features?
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.
How do we start testing?
Use the free plan with two free models for early development, then the 14-day full-access trial to validate frontier-model quality on real workloads.