Google Cloud Software / SaaS United States

Elasticsearch

Enterprise Knowledge Search

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Cut compute-related carbon footprint by estimated 25%

Deployment frequency: Improved over 10x, from biweekly releases to 25 code releases per day on average.

Lead time for changes: Reduced from seven days to under one day for 70% of services.

MTTR: Lowered by 40–60% through enhanced observability, automated rollback, and canary deployments.

Reported impact

Business outcome

~40% YoY growth in Elastic Cloud on Google Cloud business; Reduced operational expenses by ~20% through automation; Cut compute-related carbon footprint by estimated 25%

Solution

Elastic fundamentally re-architected its core offering to Elastic Cloud Serverless on Google Cloud, transforming its ability to provide lightning-fast search and AI capabilities at scale while eliminating operational complexity for customers and significantly improving its own software delivery performance through… Elastic Cloud Serverless is built on the industry-first Search AI Lake architecture, combining decoupled storage and compute with low-latency search and efficient, scalable storage, along with advanced AI capabilities to deliver uncompromising speed and… Through a close partnership with Google Cloud, Elastic became the first ISV directly integrated into Gemini platform's UI and SDK, enabling seamless grounding of Gemini prompts using Elastic's vector search. Key components include Compute Engine runs serverless or virtualized application services with elastic scale for production AI workloads; Gemini models provides the reasoning or generation layer used for.

Data flow

Elastic Cloud Serverless is built on the industry-first Search AI Lake architecture, combining decoupled storage and compute with low-latency search and efficient, scalable storage, along with advanced AI capabilities to deliver uncompromising speed and scale. The solution leverages Google Compute Engine, Google Kubernetes Engine (GKE), and Cloud Storage to empower developers, analysts, and SREs to search and analyze massive volumes of data in real time, all without managing infrastructure.

Solution architecture

5 components · 3 layers

Compute
- Compute Engine Runs serverless or virtualized application services with elastic scale for production AI workloads.
- Gemini models Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
Storage
- Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.
Orchestration
- Google Kubernetes Engine (GKE) Orchestrates the platform's containerized services and high-concurrency workloads.
- Gemini platform Coordinates multi-agent workflows, tool calling, and governed automation across the application.

Architecture clues

As data volumes grew and workloads became more demanding, Elastic saw an opportunity to reimagine the experience for a broader set of users—delivering a simpler user experience that continues to offer lightning-fast…
Elastic Cloud Serverless is built on the industry-first Search AI Lake architecture, combining decoupled storage and compute with low-latency search and efficient, scalable storage, along with advanced AI capabilities…
Elastic Cloud Serverless: Built on the Search AI Lake
Elastic fundamentally re-architected its core offering to Elastic Cloud Serverless on Google Cloud, transforming its ability to provide lightning-fast search and AI capabilities at scale while eliminating operational…
This shared vision led to Elastic Cloud Serverless; a strategic re-architecture of Elasticsearch that eliminates operational complexity, enables automatic scaling, and consistently delivers high performance at any scale.

Evidence from the source

Cut compute-related carbon footprint by estimated 25%

Elastic Cloud Serverless: Built on the Search AI Lake

Elastic realized they could better serve these needs with a modern, cloud-based model.

Reduced operational expenses by ~20% through automation

We realized we could better serve our and our customers' needs with a modern, cloud-based model, eliminating operational complexity and consistently delivering high performance at scale.

~40% YoY growth in Elastic Cloud on Google Cloud business

Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

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

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 value100/100

Problem clarity20/20
Measures whether the customer's operational problem is specific enough to compare.
Implementation depth25/25
Rewards named components, data flow, integrations, and architecture detail.
Outcome specificity20/20
Measures whether the reported benefit is concrete rather than a generic claim.
Quantified impact25/25
6 metrics describe measurable impact.
Deployment scale10/10
Rewards evidence of a live or scaled deployment and meaningful integration scope.

Evidence confidence79%

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
6 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 specificity10/10
Rewards source-backed components, data flow, quantified impact, and a specific outcome.

Traceability

Sources & provenance

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

Google Cloud
Elasticsearch customer story

First-party
https://cloud.google.com/customers/elasticsearch

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 May 28, 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 Oct 8, 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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