Pricing last verified: April 2026. Plans and pricing may change — check the vendor site for current details.
Pricing Overview
Google Cloud AI Platform (Vertex AI) uses a pay-as-you-go pricing model where you pay only for the compute, storage, and API calls your ML workflows consume. There is no flat monthly fee or per-seat charge for the platform itself — costs scale directly with usage across training jobs, prediction serving, and managed pipeline execution. New Google Cloud accounts receive free credits that can be applied to Vertex AI services, giving teams a genuine way to evaluate the platform before committing budget. The pricing structure is granular and service-specific, meaning your total cost depends heavily on which Vertex AI capabilities you use and at what scale.
Plan Comparison
| Service | Pricing Model | What It Covers |
|---|---|---|
| Training (Custom) | Per compute-hour by machine type | Model training on managed infrastructure |
| Training (AutoML) | Per node-hour | Automated model training without code |
| Prediction (Online) | Per node-hour + per prediction | Real-time model serving endpoints |
| Prediction (Batch) | Per node-hour | Large-scale batch inference jobs |
| Vertex AI Pipelines | Per pipeline run | Managed ML workflow orchestration |
| Feature Store | Per GB stored + per read | Centralized feature management |
| Vertex AI Studio | Per API call by model | Access to foundation models (Gemini, PaLM) |
The pay-as-you-go model means you pay nothing when your models are not training or serving predictions. This is advantageous for teams with intermittent workloads but can become expensive for always-on serving endpoints that handle continuous traffic.
Hidden Costs and Considerations
The biggest cost surprise is online prediction endpoints. A single always-on prediction endpoint running a modest machine type can cost hundreds of dollars per month even with low traffic, because you pay for the node-hour whether it processes one request or one thousand. Vertex AI Pipelines charges per pipeline run, and complex pipelines with many steps accumulate costs quickly during iterative development. Data storage in Google Cloud Storage (used for training data, model artifacts, and Feature Store) incurs separate GCS charges. Network egress for serving predictions to clients outside Google Cloud adds transfer fees. The lack of a simple per-seat pricing tier makes cost forecasting difficult — teams typically need 2-3 months of usage data to build accurate budget projections.
Cost Estimates by Team Size
A solo ML engineer experimenting with custom training jobs and occasional batch predictions might spend between $50 and $200 per month, mostly on compute hours during training runs. A 5-person ML team running regular training pipelines with one or two online prediction endpoints should budget $500 to $2,000 per month depending on model complexity and serving traffic. A 20-person AI organization with multiple production models, continuous training pipelines, and high-throughput serving endpoints can expect $5,000 to $20,000 per month — at this scale, committed use discounts and negotiated enterprise agreements become essential for cost management.
How Google Cloud AI Platform Pricing Compares
The pay-as-you-go model makes direct comparison difficult since competitors use different pricing structures. For the MLOps workflow layer, managed alternatives are often cheaper: Dagster Pro starts at $29 per month for pipeline orchestration, and dbt Cloud Pro starts at $25 per month. However, these tools cover orchestration only — they do not include model training infrastructure or prediction serving. For the full ML platform comparison, AWS SageMaker uses a similar pay-as-you-go compute model with comparable per-hour rates. Open-source alternatives like MLflow (free) and Kubeflow (free) eliminate platform fees entirely but require significant self-hosting infrastructure and operational expertise, similar to the Apache NiFi trade-off between licensing savings and operational costs.