What is the best Vertex AI alternative for developers in 2026?

Key facts

API compatibility	OpenAI-compatible /v1/chat/completions (drop-in base URL change)
Models	30+ models in one catalogue, from free tiers to frontier reasoning
Pricing	Flat monthly self-serve plans with unlimited fair-use usage
Free tier	plugsky-micro and plugsky-lite, no card required
Trial	14-day full-access trial for stronger models
Deployment	Plugsky cloud, your VPC, on-prem and air-gapped
Scope	Inference API, not a full ML platform or training service

TL;DR

• Vertex is a full ML platform; Plugsky is an inference API. Scope them separately.
• Keep Vertex for training, pipelines, IAM-heavy workflows and Google services.
• Move portable inference to an OpenAI-compatible endpoint with flat pricing.
• Region selection plus VPC, on-prem and air-gapped options cover residency needs.
• Splitting platform from inference is usually cheaper than replacing the platform.

How it works, step by step

1. List Vertex usage by category: training, pipelines, inference, embeddings, Google integrations.
2. Mark the inference workloads that do not depend on Google-specific features.
3. Create a Plugsky key on the free plan and replay recorded prompts.
4. Move OpenAI-style call sites to the Plugsky base URL and model names.
5. Compare quality, latency and operational cost on a full traffic cycle.
6. Keep Vertex for platform work and expand the inference split gradually.

Original data

Try it yourself

Open the Google Vertex AI cost calculator →

Vertex AI is a platform, not just a model endpoint

Vertex AI covers training, tuning, pipelines, model registry and deployment alongside Gemini and partner models. That breadth is why enterprises choose it, and it is also why the bill and the operational surface grow: every capability has to be learned, governed and paid for.

If a team only needs a reliable chat, tools and embeddings endpoint, most of that platform sits idle. Separating the inference layer from the training platform is a common architectural simplification, not a rejection of Google Cloud.

Splitting platform from inference

Plugsky speaks the OpenAI chat completions schema, which means portable inference workloads move with a base URL and model-name change. Anything genuinely tied to Google — BigQuery, Pub/Sub, Vertex Pipelines, custom training — stays where it is.

• One key for chat, embeddings, RAG and agents.
• Flat monthly self-serve pricing instead of per-token accounting.
• Free plan with plugsky-micro and plugsky-lite, no card.
• 14-day full-access trial for frontier models.

Governance, residency and gaps

Plugsky supports region selection and deployment in your VPC, on-prem or air-gapped environments, which helps when data must stay within a jurisdiction or a private network. It does not replace the Google Cloud control plane: IAM roles, audit logging and org policy remain Google's, and Plugsky is not a Google service.

Endpoint coverage is also narrower: audio, images, moderation, files, batch, fine-tuning, assistants and responses are coming soon. Chat, streaming, tools, embeddings and agents are live today. Keep platform and inference decisions independent so each can change without forcing the other. Start free, validate on real prompts, and see the live pricing page for current plans.

Honest comparison

Capability	Plugsky	Google Vertex AI	Self-managed on GCP
Scope	Inference API	Full ML platform	Whatever you build
API style	OpenAI-compatible	Google SDKs and REST	Runtime-specific
Pricing shape	Flat monthly self-serve	Usage-based	VM and GPU cost plus ops
Deployment	Cloud, VPC, on-prem, air-gapped	Google Cloud regions	GKE or custom VMs
Platform features	Not a training platform	Training, pipelines, registry	You assemble tools