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Google Cloud Software / SaaS United States

Quantro Security

Security and Compliance

The team relied on a wide range of models from different providers to power these demanding workloads, but that resulted in several operational and financial bottlenecks. Because the company was tied to standard API pricing with limited options for cost control, compute expenses ballooned.

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Agentic security platform reduces compute cost by 90%, scales vulnerability testing
Quantro Security moves its AI to Google Cloud and saves 90% on compute costs to scale vulnerability and exploit research.
Reduced compute costs by 90% without disrupting development timelines
Scaling vulnerability research to test against up to 5x+ more potential exploits

Reported impact

Business outcome

Scaling vulnerability research to test against up to 5x+ more potential exploits

  • Cost Reduction
  • Risk Reduction
  • Scalability

Solution

Some of these providers were also inconsistent with unexplained timeouts and restrictive usage limits that disrupted their development pipelines. When an API limit was unexpectedly reached, processes would halt and waste tokens. This meant the team had to restart some complex experiments from scratch. "We felt so limited in our ability to control costs," says Mehul Revankar, Co-founder and CPO at Quantro Security. "We started to explore our options and realized we didn't have to move our entire workload to one platform. We could just shift some of it to test, and that opened up so many new possibilities." Through Model Garden on Gemini Enterprise, the team gained the flexibility to experiment with a wide variety of open-source models outside of their previous closed ecosystem.

Solution architecture

2 components · 2 layers
  1. Orchestration
    • Gemini Enterprise Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
  2. Governance
    • Model Garden Adds governance, security, and control to the production AI and data environment.

Architecture clues

  • Agentic security platform reduces compute cost by 90%, scales vulnerability testing
  • Artificial intelligence has rapidly and dramatically altered cyber offense and defense.
  • Instead of relying on uncontrollable API pricing, Quantro deployed these models on reserved compute instances.
  • is an AI-native cybersecurity company whose agentic workforce safely exploits new vulnerabilities within minutes, so customers can patch them before attackers do.

Evidence from the source

Artificial intelligence has rapidly and dramatically altered cyber offense and defense. In the middle of this revolution, Quantro Security develops novel, agentic AI-powered approaches to vulnerability detection and expo
Instead of relying on uncontrollable API pricing, Quantro deployed these models on reserved compute instances. This dedicated, high-performance infrastructure ensured it had the necessary compute capacity to run their co
Products: Gemini Gemini Enterprise
Scaling vulnerability research to test against up to 5x+ more potential exploits
Through Model Garden on Gemini Enterprise, the team gained the flexibility to experiment with a wide variety of open-source models outside of their previous closed ecosystem. After extensive testing to find the highest v
Transparency Evidence & methodology 81/100 reference value · 77% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value81/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    5 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Google Cloud

    Quantro Security customer story

    First-party
    https://cloud.google.com/customers/quantrosecurity
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Sep 25, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Sep 24, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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