Gemini Enterprise Agent Platform pricing guide details
Pricing Overview
You pay only for the compute, storage, and API calls you consume across Google Cloud's ML infrastructure. This makes it accessible for teams running occasional experiments, but costs can scale rapidly with production workloads.
The platform bundles several services under one umbrella: custom training, AutoML, prediction serving, pipelines, feature store, model registry, and managed notebooks. Some components like Model Registry and Feature Store are free, while compute-intensive services like training and AutoML carry per-hour charges. The pricing structure rewards teams that optimize their resource utilization, but the sheer number of billable components means you need careful monitoring to avoid budget surprises.
Plan Comparison
Instead, each service is priced independently based on usage. Here is a breakdown of the core components and their starting rates:
| Service | Starting Price | Unit | Notes |
|---|---|---|---|
| Custom Training | $0.2185/hour | n1-standard-4 | Scales with machine type |
| AutoML Training | $3.465/hour (image) | Per node | Higher cost for automated model search |
| Prediction (Online) | $0.0771/node-hour (e2-standard-2) | Per node | Scales with traffic volume |
| Vertex AI Pipelines | $0.03/pipeline run | Per run | Plus underlying compute costs |
| Workbench (Notebooks) | Compute + management fee | Basic tier | Managed Jupyter environment |
| Model Registry | Free | Unlimited | No charge for model versioning |
| Feature Store | Free | Core usage | Storage charges may apply separately |
The gap between custom training at $0.2185/hour (n1-standard-4) and AutoML image training at $3.465/hour is significant. Teams with ML engineering capacity will save substantially by running custom training jobs, while AutoML suits teams that need quick model iteration without deep infrastructure expertise. Prediction costs remain low at $0.0771/node-hour (e2-standard-2), but high-traffic endpoints serving millions of requests can accumulate meaningful charges.
Hidden Costs and Considerations
Gemini Enterprise Agent Platform (formerly Vertex AI) prices in several layers beyond the headline rates. Cloud Storage costs for training data and model artifacts add up quickly with large datasets. GPU and TPU accelerators multiply base node-hour rates significantly. Network egress charges apply when serving predictions outside Google Cloud regions. Persistent disk storage for notebooks and training jobs carries separate fees. We recommend setting up billing alerts and budget caps early, because a misconfigured AutoML job running overnight can generate an unexpectedly large bill.
Cost Estimates by Team Size
These estimates assume typical MLOps workloads with a mix of training, experimentation, and prediction serving:
| Team Size | Monthly Estimate | Assumptions |
|---|---|---|
| Solo / Startup (1-3) | — | Light training ($0.2185/hr x 20hrs), Workbench (40hrs), minimal prediction |
| Mid-size Team (5-15) | — | Regular custom training, moderate AutoML ($3.465/hr x 10hrs), several prediction endpoints ($0.0771/hr) |
| Enterprise (20+) | — | Heavy training with GPUs, multiple AutoML experiments, high-traffic prediction endpoints, pipelines ($0.03/run x 500+) |
These figures cover Gemini Enterprise Agent Platform compute only. Add 15-30% for associated Cloud Storage, networking, and monitoring costs.
How Vertex AI Pricing Compares
Here is how it stacks up:
| Platform | Pricing Model | Starting Price | Best For |
|---|---|---|---|
| Gemini Enterprise Agent Platform | Usage-Based | $0.2185/hour (training, n1-standard-4) | Full ML lifecycle on GCP |
| Azure Machine Learning | Usage-Based | VM rate, varies by size and region | Microsoft ecosystem teams |
| Weights & Biases | Freemium | Free / $60/mo (Pro) | Experiment tracking and visualization |
| ClearML | Freemium | Free (Open Source) / $15/mo | Self-hosted ML pipeline management |
Gemini Enterprise Agent Platform and Azure Machine Learning are the closest comparisons since both offer full-platform compute pricing. Azure bills compute at the underlying VM rate, which varies by VM size and region, while Google's custom training is $0.2185/hour on n1-standard-4; the two platforms use different machine types, making direct comparison tricky. Weights & Biases and ClearML focus on experiment tracking rather than full training infrastructure, so their lower price points reflect a narrower feature scope. For teams already invested in Google Cloud, Vertex AI eliminates data transfer costs and integrates natively with BigQuery, Cloud Storage, and other GCP services.