Normalized claim
Quantified impact: 45% decrease
Reduced infrastructure overhead by 45%.
ChatAndBuild case study | Vertex AI + Gemini for natural-language app/agent generation with low latency
Use case typeAI platformUpdated Jun 13, 2026
ChatAndBuild is an AI-native app building platform that turns natural language into real-time applications, enabling builders worldwide to generate software, games, and agents from prompts. The company migrated to Google Cloud to support real-time app generation, multi-region scaling, and long-term agent memory. Vertex AI routes tasks to specialized agents for coding and research, while Gemini models on Vertex AI handle multimodal generation such as video motion analysis. GKE isolates agent graph namespaces; Cloud Run and Cloud Functions handle burst traffic; Cloud Load Balancing routes requests to the nearest region; BigQuery tracks billions of tokens to help prevent context rot. Google Cloud security controls such as VPC Service Controls, Google Cloud Armor, and IAM support compliance for regulated customers.
- Organization
- ChatAndBuild
- Industry
- Tech & Comms
- Location
- United States
- Published
- June 2026
Reported outcomes
Cost: −45%
Cost savings
Time: −30–70%
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Quantified impact: 30-70% decrease
Reduced latency by 30–70%.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- ChatAndBuild
- Provider
- GCP
- Maturity
- Scaled Production
- Linked source
- Google Cloud Customer Stories
Scaled to more than 13 billion tokens with zero downtime during global events
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 4
- 1Generative AI app building
- 2Agent orchestration
- 3Multimodal generation
- Migrated the platform to Google Cloud.
- Used Vertex AI to route tasks to specialized coding and research agents.
- Used Gemini models on Vertex AI for multimodal generation.
- Used Google Kubernetes Engine to isolate agent graph namespaces.
- Used Cloud Run and Cloud Functions for burst handling.
- Used Cloud Load Balancing for nearest-region routing and BigQuery as a telemetry layer to track billions of tokens and reduce context rot.
- Reduced infrastructure overhead by 45%.
- Reduced latency by 30–70%.
- Enabled more than 140,000 users to generate apps.
Architecture
AI-native app building platform on Google Cloud using Vertex AI and Gemini for specialized agent routing and multimodal generation, GKE for isolated agent namespaces, Cloud Run and Cloud Functions for burst scaling, Cloud Load Balancing for nearest-region routing, and BigQuery telemetry to track billions of tokens and mitigate context rot.
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 3, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
Explore related AI use cases
Customer evidence
Provider evidence
AI patterns
Browse the catalog
Was this useful?
Community
Comments
No published comments yet.
Comments could not be loaded.
Similar cases
No similar cases found.