Overview
Vertex AI is Google Cloud's managed platform for building, tuning, and serving machine-learning and generative-AI applications. Its distinguishing characteristic is the combination of Google's own Gemini models with Model Garden, a catalogue that brings third-party and open models, including Anthropic's Claude, Meta's Llama, and Mistral, under a single Google Cloud billing relationship and governance layer. For an enterprise, that means one contract, one identity model, and one set of regional-residency and encryption controls across a wide range of frontier and open models, rather than separate vendor relationships for each.
The platform sits at the centre of Google's enterprise AI position and is strongest when paired with BigQuery: data that already lives in Google Cloud can be grounded, embedded, and served without leaving the boundary, and Vertex AI Studio gives a fast prompt-and-grounding iteration loop. Gemini 2.5 Pro and the Flash family give a range of price-performance points, with the cheapest Flash-Lite tier at roughly 0.10 US dollars per million input tokens and the Pro tier at 1.25 US dollars per million input tokens. Vertex is least productive for teams whose data and applications live primarily in AWS or Snowflake, where the grounding advantage disappears and the breadth of services adds complexity rather than removing it.
Key Features
- Gemini 2.5 Pro, Flash, and Flash-Lite models with multimodal input
- Model Garden access to Claude, Llama, Mistral, and open models
- Vertex AI Studio for prompt design, grounding, and evaluation
- Custom training on TensorFlow, PyTorch, and scikit-learn
- Vertex AI Pipelines for orchestrated ML workflows
- Feature Store and Vector Search for retrieval applications
- Agent Builder for grounded enterprise agents
- Native grounding against BigQuery and Google Search
- Model tuning, distillation, and managed endpoints
- VPC Service Controls, CMEK, and regional data residency
- Model Monitoring for drift and skew detection
- Generative AI evaluation service for output quality scoring
Pricing
| Model / Service | Unit | Indicative Price | Notes |
|---|---|---|---|
| Gemini 2.5 Flash-Lite | Per 1M input / output tokens | ~$0.10 / $0.40 | Lowest-cost tier |
| Gemini 2.5 Pro | Per 1M input / output tokens | ~$1.25 / $10.00 | Flagship reasoning |
| Model Garden (Claude, Llama) | Per token, per model | Varies by model | Third-party rates apply |
| Custom training / serving | Per compute hour | Pay per use | Plus storage and egress |
Pricing verified June 2026. Enterprise pricing requires a quote where committed-use discounts apply. Token prices are indicative and change frequently; storage, online prediction, and data egress are billed separately and are common sources of unbudgeted cost.
Strengths
- Gemini plus Model Garden gives access to Google, Anthropic, Meta, and Mistral models under one contract
- Strongest grounding and retrieval experience when data already lives in BigQuery
- Enterprise controls: VPC Service Controls, customer-managed encryption keys, regional residency
- Full lifecycle coverage from notebooks and custom training to managed serving and monitoring
- Multimodal and long-context strength across text, image, audio, and video
Limitations
- Cost is hard to forecast: token, compute, storage, and egress charges accumulate across many services
- Most productive only when data is in Google Cloud; weaker value for AWS- or Snowflake-centric estates
- Breadth of services creates a steep learning curve and fragmented documentation
- Deeper adoption increases Google Cloud ecosystem lock-in across identity, networking, and data
- Frequent model and API changes require teams to track and re-test against moving targets
Buyer Considerations
Vertex AI is the default enterprise AI platform for organisations already standardised on Google Cloud, and especially those whose analytical data sits in BigQuery, where the grounding and retrieval advantage is real and measurable. The harder question is cost governance: because charges span tokens, training compute, storage, online prediction, and egress, a finance team needs instrumentation and committed-use planning before production scale, or the bill becomes unpredictable. Teams whose data lives in AWS or Snowflake should weigh whether the Model Garden convenience outweighs the loss of native grounding, and should benchmark Vertex against calling the same models through their existing cloud. Standardising an abstraction layer over the model APIs early reduces the re-test burden as Google iterates on Gemini.