What challenges did Galileo face before AI implementation?

LLM outputs are non-deterministic, making it difficult to de-risk and evaluate AI applications at scale. Enterprises need to measure model behavior against application-specific benchmarks and governance requirements. The platform needed to scale globally with low latency and multi-region data placement.

What was the impact of the AI solution?

Galileo reports more than 1,000 AI applications de-risked. The platform handles over 20 million requests per day at 300-millisecond latencies. It scaled to more than a petabyte of data and 5,000+ concurrent end users. Customers across North America, Europe, and Asia use the platform.

Source

GCP

Galileo case study | Google Cloud

Use case typeAI platformUpdated Jun 13, 2026

Galileo built an LLM reliability platform on Google Cloud to help customers de-risk, evaluate, and monitor generative and agentic AI applications. The company uses Gemini, Vertex AI, Google Kubernetes Engine, Cloud SQL, Cloud Storage, Vector Search, BigQuery, and NVIDIA GPUs to run evaluation agents, multi-region deployments, and retrieval-augmented workflows. The platform supports large-scale experimentation, model behavior measurement, and mitigation of hallucinations and other reliability issues for customer AI applications.

Organization: Galileo
Industry: Tech & Comms
Location: United States
Published: May 2026

Reported outcomes

Strategic outcomes

Risk & complianceDe-risked AI applications at scaleScale & capacitySupports high-volume global operationsScale & capacityScaled to petabyte-level data and usersMarket & geographic expansionUsed across multiple global regions

Primary read

Use case focus

Showing 3 of 3

1AI Platform
2Model Monitoring and Evaluation
3Retrieval-Augmented Generation

LLM outputs are non-deterministic, making it difficult to de-risk and evaluate AI applications at scale.
Enterprises need to measure model behavior against application-specific benchmarks and governance requirements.
The platform needed to scale globally with low latency and multi-region data placement.

Built a trust layer and evaluation platform on Google Cloud.
Used Gemini and Vertex AI to power evaluation agents that automate experimentation and measurement.
Deployed on Google Kubernetes Engine with Cloud SQL and Cloud Storage for multi-region operations, Vector Search for RAG, and BigQuery for customer data lineage and analytics.

Technologies

Gemini Vertex AI Google Kubernetes Engine Cloud SQL Cloud Storage Vector Search BigQuery Cloud GPUs

Galileo reports more than 1,000 AI applications de-risked.
The platform handles over 20 million requests per day at 300-millisecond latencies.
It scaled to more than a petabyte of data and 5,000+ concurrent end users.
Customers across North America, Europe, and Asia use the platform.

Architecture

Galileo runs on Google Cloud with NVIDIA GPUs, Gemini and Vertex AI-powered evaluation agents, Google Kubernetes Engine for scalable deployment, Cloud SQL and Cloud Storage for regional data placement, Vector Search for retrieval-augmented generation workflows, and BigQuery integration for lineage and analytics.

Implementation partners1

NVIDIA

Sources & evidence1

Groundedness: 5/5Type: Customer StoryPublished: May 25, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High

Primary source

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Customer evidence