Building GenAI Applications with Vertex AI Step by Step

Introduction

Building GenAI Applications with Vertex AI lets teams train, fine-tune, and deploy powerful generative models like Gemini 2.5 while integrating data from BigQuery and exposing APIs through API Gateway. This guide walks through a practical end to end workflow, highlighting data preparation, fine-tuning best practices, deployment patterns, and operational tips for production readiness.

Architecture overview and core components

Start by defining the architecture: BigQuery for data storage and analytics, Cloud Storage for artifacts, Vertex AI for model training, fine-tuning, and hosting, and API Gateway in front of a lightweight Cloud Run or Cloud Function that forwards requests to Vertex AI endpoints. This pattern decouples public API concerns from model serving and lets you control authentication, rate limits, and monitoring at the gateway layer.

Key components and interactions:

• BigQuery: store labeled training examples, metadata, and feature tables. Use SQL to sample or aggregate training data.
• Cloud Storage: staging for datasets and exported artifacts used by Vertex AI.
• Vertex AI: dataset management, training jobs, fine-tuning of foundation models like Gemini 2.5, model registry, and endpoints.
• Cloud Run + API Gateway: a secure, scalable HTTP façade that validates requests, applies quotas, and proxies predictions to Vertex AI.

Preparing data and training with BigQuery and Vertex AI

Data quality and format matter. For generative tasks, prepare prompt-response pairs or instruction/response pairs as newline delimited JSON or CSV. A typical pipeline:

• Use BigQuery to build training sets: SELECT prompt, response FROM project.dataset.table WHERE quality_score > 0.8;
• Export results to Cloud Storage with bq extract or a scheduled query: bq extract –destination_format NEWLINE_DELIMITED_JSON ‘project:dataset.table’ gs://your-bucket/train.jsonl
• Create a Vertex AI Dataset referencing the Cloud Storage files. In Python you can use the google.cloud.aiplatform SDK to register datasets and inspect examples before training.

For initial training of a custom generative model, you may run a managed training job on Vertex AI using container-based training or use Vertex AI’s Fine-tune APIs for foundation models. Ensure you set up IAM roles: Vertex AI Service Agent, Storage Admin for GCS access, and BigQuery Data Viewer for dataset reads.

Fine-tuning Gemini 2.5 on Vertex AI

Gemini 2.5 is available via Vertex AI model garden and supports fine-tuning for specialized tasks. Recommended steps:

• Choose the correct model variant based on cost and latency needs. Gemini 2.5 is performant for complex reasoning but consider smaller variants for high-throughput low-cost use cases.
• Curate 5k–50k high-quality examples for instruction tuning where possible. Avoid noisy entries; small but high-quality sets often outperform large noisy datasets.
• Invoke the Vertex AI fine-tune API pointing to your Dataset or Cloud Storage JSONL. Example high-level flow: create a FineTuneJob with model_name set to the Gemini 2.5 resource, training_files pointing to gs://your-bucket/train.jsonl, and hyperparameters like learning_rate and num_epochs configured conservatively.
• Monitor the job in the Vertex AI console and capture evaluation metrics on a holdout validation set. Use early stopping if loss plateaus to control costs.

Practical tips: use instruction templates to standardize prompts, augment with negative examples to reduce hallucinations, and regularize with constrained decoding (max tokens, temperature, top_p) during evaluation.

Deploying generative models and integrating with API Gateway

After fine-tuning, register the model in the Vertex AI Model Registry and deploy to an endpoint. Steps:

• Create an Endpoint: gcloud ai endpoints create –region=YOUR_REGION –display-name=genai-endpoint
• Deploy the model to the endpoint with traffic and machine type settings. Pick an accelerator type if using GPU-backed replicas to hit latency targets.
• Build a small Cloud Run service that accepts client requests, performs authentication using Identity Tokens, applies input sanitization, and calls the Vertex AI prediction endpoint using the aiplatform client or REST prediction API.
• Front Cloud Run with API Gateway: configure routes, API keys, quota limits, and OAuth verification. API Gateway handles public exposure while Cloud Run handles model proxied calls.

Example request flow: Client -> API Gateway (auth, throttling) -> Cloud Run (validate, enrich with BigQuery lookup) -> Vertex AI Endpoint (predict using Gemini 2.5) -> Cloud Run (post-process, log) -> Client.

Monitoring, cost controls, and best practices

Operational considerations for production GenAI:

• Monitoring: capture latency, error rates, token usage, and cost per request. Use Cloud Monitoring dashboards and set alerts for quota and error thresholds.
• Cost controls: use replica autoscaling and traffic splitting, set lower-cost model fallbacks for non-critical requests, and cache common responses with Redis or Memorystore.
• Security: apply VPC-SC or private service connectors for BigQuery and Vertex AI, use signed tokens for Cloud Run-to-Vertex calls, and audit logs for data access.
• Evaluation: periodically refresh evaluation sets stored in BigQuery and run scheduled A/B tests between model versions. Track qualitative metrics like hallucination rate and response usefulness.

Real example: a customer reduced inference cost by 40% by routing high-frequency simple prompts to a smaller tuned model and reserving Gemini 2.5 for complex queries requiring deeper reasoning.

Conclusion

Building GenAI Applications with Vertex AI combines the power of Gemini 2.5, scalable data in BigQuery, and secure API exposure via API Gateway to deliver production-ready generative services. Follow a disciplined pipeline: prepare high-quality data in BigQuery, fine-tune thoughtfully, deploy behind a gateway, and monitor both performance and costs. With these steps you can move from prototype to reliable production deployments while maintaining control over latency, cost, and quality.