Key facts
| Gemini API surface | Native Gemini API plus an OpenAI-compatible endpoint for chat; Vertex AI uses Google SDKs and authentication |
| Gemini pricing model | Usage-based per-token billing; a free tier exists in AI Studio for prototyping |
| Gemini deployment | Google AI Studio and Vertex AI regional endpoints with Google Cloud residency controls |
| Plugsky API compatibility | OpenAI-compatible /v1/chat/completions; change the base URL and model name |
| Plugsky pricing model | Flat monthly self-serve plans with unlimited fair-use usage; no per-token billing |
| Plugsky model access | 30+ models behind one API, from free to frontier tiers |
| Plugsky multimodal status | Vision models are live; audio, image and video generation are coming soon |
| Plugsky deployment and residency | Plugsky cloud, your VPC, on-prem and air-gapped; region selection for residency |
TL;DR
- Gemini is the stronger choice for native audio, video and image workloads today.
- Plugsky standardises on one OpenAI-compatible API and flat monthly self-serve pricing.
- Vision is live on Plugsky; audio and image generation are coming soon.
- Vertex AI offers Google Cloud residency controls; Plugsky adds VPC, on-prem and air-gapped options.
- Free plan with 2 free AI models (plugsky-micro, plugsky-lite) plus a 14-day full-access trial.
How it works, step by step
- Classify your Gemini usage: text chat, vision, audio, video, image generation or embeddings.
- Create a Plugsky account and API key on the free plan (no card required).
- Map the text and vision workloads to Plugsky models and keep media-generation workloads on Gemini for now.
- Point a copy of your OpenAI-SDK code at the Plugsky base URL and fix any Gemini-specific parameters.
- Run evals on the same prompts and compare quality, JSON adherence, latency and long-context behaviour.
- Move text and vision traffic first, measure for a week, then expand per workload as capabilities ship.
- Keep Gemini for media generation and use a router to send each request to the right provider.
Try it yourself
Open the Gemini API cost calculator →
Where Google Gemini wins
Gemini's native multimodality is the clearest advantage: the same family handles text, images, audio and video, and Google's media models cover image and video generation. Vertex AI adds enterprise controls, regional endpoints and Google Cloud identity, while AI Studio provides a fast, low-friction prototyping tier. Long-context work is also a strength. If your product depends on processing audio or video natively, or you are already committed to Google Cloud, Gemini is difficult to replace with a text-first provider.
Where Plugsky wins
Plugsky wins when text, vision and agents are the core of the product and neutrality matters. One OpenAI-compatible endpoint serves 30+ models across families, so a single integration covers cheap classification, frontier reasoning and vision. Self-serve plans are flat monthly with unlimited fair-use usage, which is easier to budget than per-token billing across several model types. The free plan includes two free AI models and there is a 14-day full-access trial. Enterprise deployments add VPC, on-prem and air-gapped options beyond any single cloud's footprint.
What to test before switching
Test per modality, not per platform. For text, compare quality on your prompts, function-calling reliability and JSON adherence. For vision, compare image understanding accuracy on your own image set and check token accounting for large images. For long context, measure quality near the limits rather than at the middle. Then verify latency from your users' regions and the data path for each provider. Media generation and native audio remain Gemini territory until Plugsky's coming-soon endpoints ship, so keep those tests separate from the chat migration.
Migration path from Gemini to Plugsky
If you already use Gemini's OpenAI-compatible endpoint, the chat switch is a base URL, API key and model ID change: base_url='https://api.plugsky.com/v1'. If you use the native Gemini SDK or Vertex AI libraries, add a small adapter that maps contents and parts to chat messages. Migrate text first, then vision. Keep media generation on Gemini behind a provider interface so the application code does not care which platform serves each modality, and expand the Plugsky share as new endpoints become available.
Honest comparison
| Capability | Plugsky | Google Gemini | Building in-house |
|---|---|---|---|
| API compatibility | OpenAI-compatible chat completions | Native Gemini API plus OpenAI-compatible endpoint | Full rewrite |
| Pricing model | Flat monthly self-serve, unlimited fair use | Per token plus a prototyping tier | GPU + ops cost |
| Model access | 30+ models across families, one API | Gemini family plus Google media models | You host each model |
| Multimodal coverage | Vision live; audio, image and video generation coming soon | Native text, image, audio and video | You integrate each |
| Deployment and residency | Cloud, VPC, on-prem, air-gapped; region choice | Google Cloud regions via Vertex AI | You control |
| Migration effort | One line for OpenAI-style chat | Adapter for the native Gemini SDK | Months |
Frequently asked questions
Can I keep using the OpenAI SDK with Plugsky?
Yes. Plugsky exposes an OpenAI-compatible chat completions endpoint, so you change the base URL and model name and keep your SDK code.
Does Plugsky support image input?
Yes — vision models are live on Plugsky. Audio, image generation and video are marked as coming soon, so keep those workloads on Gemini for now.
How does Plugsky pricing differ from Gemini?
Gemini bills per token and offers a prototyping tier in AI Studio. Plugsky self-serve plans are flat monthly with unlimited fair-use usage and no per-token charges. See the live pricing page for current plans.
Is Plugsky a drop-in replacement for Vertex AI?
No. Vertex AI uses Google SDKs and authentication. Plugsky is OpenAI-compatible, so native Vertex code needs an adapter.
Can Plugsky run in our own cloud?
Yes. Enterprise deployments support VPC, on-prem and air-gapped environments with region selection for data residency.
Which workloads should stay on Gemini?
Media generation, native audio processing and any feature that depends on Google Cloud services are the strongest candidates to keep.
How much migration work is involved?
OpenAI-style chat and vision calls are a base URL and model mapping. Native Gemini SDK code needs a translation layer.
Can I run both platforms together?
Yes. Use a provider interface or router so each request goes to the platform that supports the required modality at the best cost.