Key facts
| Provider | Google Vertex AI — Google Cloud's enterprise platform for models, pipelines and MLOps |
| API style | Google Cloud APIs and SDKs, with IAM, regional endpoints and Model Garden access |
| Plugsky API | OpenAI-compatible /v1/chat/completions — change the base URL, keep your SDK |
| Models | 30+ models from free to frontier behind one API key |
| Pricing | Flat monthly plans with unlimited fair-use usage; no per-token billing on self-serve |
| Free tier | Free plan with plugsky-micro and plugsky-lite, no card; 14-day full-access trial |
| Deployment | Plugsky cloud, your VPC, on-prem or air-gapped; region choice for residency |
| Live vs roadmap | Chat, streaming, JSON mode, function calling, embeddings, RAG, agents live; audio, images, moderation, files, batch, fine-tuning, assistants, responses coming soon |
TL;DR
- Vertex AI is powerful when your platform team already lives in Google Cloud.
- The common friction points: GCP dependency, API complexity and usage-based spend.
- Plugsky gives OpenAI-compatible access to 30+ models with flat monthly pricing.
- Region choice plus VPC, on-prem and air-gapped deployment serve regulated teams.
- Honest trade-off: Vertex's MLOps, data tooling and governance depth are hard to replicate.
How it works, step by step
- Separate what you use Vertex for: model inference, pipelines, or both.
- Decide whether Google Cloud dependency is acceptable long term.
- Create a Plugsky account and map the Vertex models your apps call.
- Move inference traffic to an OpenAI-compatible client; leave pipelines where they run.
- Compare total cost: platform fees, egress, engineering time and API usage.
- Keep Vertex for data-heavy MLOps work while consolidating inference.
Original data
Try it yourself
Open the Google Vertex AI cost calculator →
What Vertex AI is good at
Vertex AI is a full platform, not just an inference endpoint. Model Garden gives access to Gemini and third-party models, pipelines and the model registry handle lifecycle management, and IAM with VPC Service Controls fits enterprise governance. For teams already standardized on BigQuery and Google Cloud, the integration is genuinely useful.
The trade-offs appear when you only need inference: platform complexity, GCP coupling, regional model availability, and usage-based billing that requires active cost management as traffic grows.
Where Plugsky fits
Plugsky focuses on the inference layer. One OpenAI-compatible API exposes 30+ models, from free chat tiers to frontier reasoning, with a free plan (plugsky-micro and plugsky-lite) and a 14-day full-access trial. Self-serve plans are flat monthly with unlimited fair-use usage (live pricing), which removes token-level cost anxiety from product planning.
Deployment options cover regulated buyers: Plugsky cloud, your VPC, on-prem or air-gapped, with region selection for residency. Chat, streaming, JSON mode, function calling, embeddings, RAG and agents are live; audio, images, moderation, files, batch, fine-tuning, assistants and responses are coming soon.
Choosing the right layer
Vertex and Plugsky solve different problems, so the honest answer is often both.
- Use Vertex for pipelines, training infrastructure and Google-native data integration.
- Use Plugsky for portable inference across models and tiers.
- Avoid duplicating model access in both places without a reason.
- Review residency obligations before routing regulated data anywhere.
One caution: model access is not the same as platform capability. If your pipelines, feature stores and governance workflows are built on Google Cloud, keep them there and treat Plugsky as a serving layer rather than a wholesale replacement.
Honest comparison
| Capability | Plugsky | Vertex AI | Building in-house |
|---|---|---|---|
| API style | OpenAI-compatible drop-in | Google Cloud APIs, IAM and regional endpoints | You define the schema |
| Model access | 30+ models, one key, model as a parameter | Gemini plus Model Garden partners | You host each model |
| Billing | Flat monthly, unlimited fair use (see live pricing) | Usage-based with GCP platform costs | GPU + ops cost |
| Residency | Region choice, VPC, on-prem, air-gapped | Google Cloud regions and controls | You control the infrastructure |
| Free tier | plugsky-micro + plugsky-lite, no card | Check Google Cloud terms | None |
| Honest gap | MLOps and data-platform depth | Pipelines, registry and Google data integration | You build everything |
Frequently asked questions
What is Vertex AI?
Vertex AI is Google Cloud's enterprise ML platform, covering model access through Model Garden, training and pipeline tooling, and governance integrations.
Why choose a Vertex AI alternative?
Teams usually want simpler inference access, portability away from a single cloud, predictable monthly pricing, or deployment in their own environment.
Can I move Vertex inference calls to Plugsky?
Inference moves are straightforward with an adapter: callers switch from Google Cloud SDKs to OpenAI-format HTTP calls. Pipeline and data tooling stays where it runs.
Is there a free plan?
Yes — plugsky-micro and plugsky-lite are free with no credit card, and a 14-day full-access trial covers paid models.
How is Plugsky priced?
Flat monthly self-serve plans with unlimited fair-use usage and no per-token billing. See the live pricing page.
Does Plugsky replace Vertex AI entirely?
No. For MLOps, training pipelines and Google data integration, Vertex remains the deeper platform. Plugsky focuses on portable, predictable inference.
Can I deploy Plugsky privately?
Yes — VPC, on-prem and air-gapped deployments are available, with region selection for data residency.