Key facts
| API compatibility | Plugsky exposes OpenAI-compatible /v1/chat/completions |
| Pricing model | Plugsky self-serve is flat monthly with unlimited fair-use usage; no per-token billing |
| OpenAI pricing | Per-token billing across its model catalogue |
| Model access | Plugsky: 30+ models under one plan |
| Endpoints live now | Chat, streaming, JSON mode, function calling, embeddings, RAG, agents |
| Coming soon | Audio, images, moderation, files, batch, fine-tuning, assistants, responses |
| Deployment | Plugsky: cloud, VPC, on-prem, air-gapped |
| Product status | Core inference endpoints live; specialist endpoints on the roadmap |
TL;DR
- Keep your OpenAI SDK — change the base URL and model name.
- Plugsky is flat-rate on self-serve; OpenAI bills per token.
- 30+ models under one Plugsky plan versus OpenAI's own catalogue.
- OpenAI still leads on specialist endpoints and ecosystem depth.
- Plugsky adds VPC, on-prem and air-gapped deployment options.
How it works, step by step
- Audit which OpenAI endpoints your application actually calls.
- Check each against the Plugsky capability matrix for live or coming-soon status.
- Create a Plugsky key on the free plan and change the base URL.
- Map model names and run your existing test suite against both providers.
- Compare quality, latency and cost on the same evaluation set.
- Cut over core chat and embeddings first, then plan specialist workloads.
- Monitor errors and usage analytics during the first production week.
Try it yourself
Open the OpenAI cost calculator →
API compatibility: the migration surface
Plugsky implements OpenAI-compatible endpoints, so the migration is usually a configuration change: point the client at the Plugsky base URL, update the model name, keep the SDK. Chat completions, streaming, JSON mode, function calling, embeddings and RAG patterns map directly.
Check the capability matrix before moving specialist workloads. Audio, images, moderation, files, batch, fine-tuning, assistants and responses are roadmap items on Plugsky today, so applications that depend on them should keep those calls on OpenAI until the endpoints ship.
Pricing model
OpenAI bills per token across its catalogue, so cost scales with prompts, completions, retries and agent loops. Plugsky self-serve plans are flat monthly with unlimited fair-use usage and no per-token charges or overage fees, with RPM, TPM and concurrency limits as the throughput constraint.
For steady production traffic, flat-rate converts an uncertain line into a fixed one. For low or experimental usage, per-token remains efficient. Run the comparison with your measured tokens and the live pricing page.
Model access and routing
OpenAI gives you its own model family, tightly integrated with its platform features. Plugsky gives you 30+ models through one endpoint and one plan, including small fast models for classification and formatting, frontier models for reasoning, and embedding models for retrieval.
That breadth matters when cost and latency drive routing decisions: a request classifier does not need a frontier model, and switching between tiers should not require a new vendor, key or contract.
Deployment and data residency
OpenAI operates its own cloud with regional processing options and enterprise controls. Plugsky offers region selection plus VPC, on-prem and air-gapped deployment for regulated teams, alongside SSO, RBAC, audit logs, DPA and BYOK options.
If your compliance requirements stop at the provider's regional guarantees, either platform can work. If inference must run inside your own infrastructure, that is where Plugsky's deployment range becomes decisive.
Honest comparison
| Factor | Plugsky | OpenAI | Self-hosted |
|---|---|---|---|
| API compatibility | OpenAI-compatible endpoints | Native OpenAI API | Framework-specific |
| Pricing | Flat monthly, no per-token billing on self-serve | Per-token across the catalogue | GPU capex plus ops |
| Model access | 30+ models under one plan | OpenAI model family | Only what you host |
| Specialist endpoints | Audio, images, files, batch and fine-tuning coming soon | Broad and mature | You build them |
| Deployment | Cloud, VPC, on-prem, air-gapped | Provider cloud regions | Your infrastructure |
| Migration effort | Base URL and model name | Reference implementation | Full rebuild |
Frequently asked questions
Can I keep using the OpenAI SDK with Plugsky?
Yes. Plugsky exposes OpenAI-compatible endpoints, so you change the base URL and model name and keep your existing SDK code. Chat, streaming, JSON mode, function calling and embeddings behave as expected.
Is Plugsky cheaper than OpenAI?
It depends on usage. OpenAI bills per token; Plugsky self-serve plans are flat with unlimited fair-use usage and no per-token charges. Compare your measured token spend with the flat plan price on the live pricing page.
Which OpenAI features are not available on Plugsky yet?
Audio, images, moderation, files, batch, fine-tuning, assistants and responses are coming soon. Core chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live.
How many models does Plugsky offer?
30+ models through one API and one flat plan on self-serve, from small fast models to frontier reasoning and embedding models.
Does Plugsky support function calling and JSON mode?
Yes, both are live, along with streaming. The docs list the capability matrix per model so you can check support before routing workloads.
What about data residency?
Plugsky offers region selection plus VPC, on-prem and air-gapped deployment options for enterprises. OpenAI provides regional processing and enterprise controls within its own cloud.
How long does migration take?
Core chat and embedding workloads often migrate in an afternoon: change the base URL, map model names and run your tests. The effort concentrates in specialist endpoints and provider-specific features.
Can I run both providers side by side?
Yes, and it is a sensible transition pattern. Keep specialist workloads on OpenAI while moving core inference to Plugsky, then consolidate as the remaining endpoints become available.