Google Vertex AI
A unified ML platform covering data preparation, model training, deployment, and monitoring. Gemini foundation models are integrated directly.
ClearView
One platform from training to serving
Vertex AI is the managed machine learning platform on Google Cloud. It provides training jobs on provisioned GPU infrastructure, a registry for the models that result, endpoints to serve them, and pipelines that connect the stages, which is the infrastructure a team would otherwise have to assemble and operate itself.
Gemini models are served through the same platform, so foundation model calls run under a project's existing identity, audit logging and quota controls rather than through a separate provider account. Model Garden widens the catalogue to partner models, including Anthropic's Claude models, on the same terms.
The platform earns its place where the data already lives on Google Cloud, particularly in BigQuery, and where custom training is part of the work. It is the wrong choice when an application calls a hosted frontier model and no model of your own is being trained: calling the Anthropic or OpenAI APIs directly is simpler, and keeps the application portable across clouds.
- ML Pipelines
- Multimodal AI
- MLOps
- Data Analytics
Our Google Vertex AI capabilities
Gemini multimodal integration
Gemini integration work covers all four modalities, text, image, video and audio, with grounding through Vertex AI Search so answers draw on an organisation's own indexed content rather than the model's training data alone. Function calling connects the model to internal systems, and managed endpoints carry the traffic with quotas and logging in place from the first request.
- Gemini
- Vertex AI Search
- Grounding
ML pipelines & MLOps
Training, evaluation and promotion are defined as pipeline code rather than run by hand, so every model's provenance is reproducible on demand: the dataset version, the parameters and the evaluation that cleared it. Experiments records the runs that did not ship as well as the ones that did, and the Model Registry gates promotion, so a model reaches an endpoint through a recorded evaluation rather than a manual upload.
- Vertex Pipelines
- Model Registry
- Experiments
BigQuery ML & data integration
BigQuery ML trains models where the warehouse data already sits, which removes the export pipeline that usually precedes training. Feature Store serves the same feature values at inference that the model saw at training, closing the training-serving skew that quietly degrades deployed models, and Dataflow feeds both from streaming and batch sources.
- BigQuery ML
- Feature Store
- Dataflow
Why Adaca for Google Vertex AI?
GCP-native ML expertise
Vertex AI, BigQuery, Dataflow, and Pub/Sub for ML systems that use Google Cloud natively.
Gemini application development
Multimodal Gemini applications with grounding, function calling, and enterprise search.
MLOps & pipeline design
Reproducible training with versioned datasets, experiments, and automated model promotion.
Custom model training
Custom training jobs on managed GPU clusters with hyperparameter tuning.
Model monitoring
Feature drift, prediction quality, and performance degradation detected in production.
Cost-efficient ML
Spot instances, autoscaling endpoints, and model distillation to reduce serving costs.
How We Staff It
A Vertex AI engagement pairs machine learning work with data engineering more often than it stands alone: pipelines and serving are Python software engineering, and the inputs usually arrive through BigQuery and Dataflow. Engineers come from the same pool that carries Adaca's Python data and AI work, and they work to the customer's working day.
Model work and platform work are distinct skills, so briefs are scoped to name which the engagement needs: evaluation and prompt work on the model side, pipelines, serving and cost control on the platform side.
Common Questions
Should we serve models through Vertex AI endpoints or build our own serving stack?
Managed endpoints answer for most teams. Autoscaling, GPU allocation, traffic splitting and monitoring arrive as configuration rather than as systems to build and operate.
A self-built serving stack is worth the cost in two cases: sustained inference volume high enough that managed pricing overtakes running your own nodes, and a requirement that serving stay portable off Google Cloud. Short of those, the managed path costs less engineering time.
Do we need Vertex AI just to use Gemini?
No. The Gemini API can be called directly without the platform, and for a single application that is the shorter path. Vertex AI becomes worth adopting when model calls must fall under the same governance as the rest of a Google Cloud estate, when grounding on indexed content is needed, or when custom models run alongside.
Is a Vertex AI estate a good fit for AI-assisted development?
It depends on where the estate's logic lives. Pipeline definitions are Python code with a compile step, so an agent can read a training workflow end to end, modify it, and get a fast structural check before anything runs. An estate that lives in notebooks resists the same tooling: state depends on execution order, tests are absent, and outputs sit interleaved with code, so an agent has nothing reliable to verify a change against.
One platform-specific caution: the Vertex SDK surface has moved across namespaces in recent years, so generated code frequently targets superseded versions and needs review against the current SDK. The measured results of our AI-augmented delivery flow are on the Technology page.
How is a Vertex AI engagement priced and structured?
Pricing follows one of two models: a monthly rate for a dedicated team, or a fixed cost for an initial outcome. Engagements start at one engineer, terms run three, six, nine or twelve months chosen up front, and a shortlist usually reaches you within 48 hours. The People pages set out both models in detail.