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MongoDB Financial Services United States

Coinbase

IT Operations Automation

Coinbase needed database clusters that could absorb volatile crypto traffic spikes without long scaling delays, downtime, or degraded customer experience.

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3.25x reduction in cluster deployment time
60% reduction in scaling time
The model automatically triggers the clusters to scale up 60 minutes prior to anticipated traffic spikes.
“We’ve brought scaling times down from upward of 70 minutes to 25 minutes — less than half the time it used to take.”

Reported impact

Business outcome

Coinbase cut scaling time from upward of 70 minutes to 25 minutes, accelerated new cluster deployments, and improved resiliency so users no longer notice traffic-driven infrastructure changes.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Coinbase standardized its high-traffic operational data platform on MongoDB Atlas running on Amazon Web Services and used Atlas Data Federation for its warehousing pipeline while redesigning how large clusters scale. The team first identified the Atlas clusters most correlated with user traffic, then analyzed historical operational metrics and cryptocurrency price signals to predict how much capacity those clusters would need during surges. A machine learning model now forecasts volatility and triggers scale-ups 60 minutes before expected spikes. MongoDB and Coinbase also introduced parallel scaling so similar node types expand together instead of node by node, reducing time to peak performance while keeping application data available. The result is a more resilient, automatically scaled platform that supports fast-moving market demand without overprovisioning.

Data flow

Historical Atlas metrics and cryptocurrency price data feed Coinbase's machine learning model, which predicts demand and triggers Atlas cluster scale-ups 60 minutes before spikes; MongoDB then scales grouped node types in parallel so the application continues serving customers through volatility.

Solution architecture

2 components · 1 layer
  1. Storage
    • MongoDB Atlas Runs Coinbase's operational clusters and executes the predictive and parallel scaling workflow that keeps customer-facing services responsive during traffic spikes.
    • Atlas Data Federation Supports Coinbase's data warehousing pipeline alongside the operational Atlas estate.

Architecture clues

  • Coinbase is considering sharding to address future vertical scaling limits.
  • Coinbase operates roughly 700 database clusters.
  • Parallel scaling groups similar node types instead of scaling node by node.
  • The predictive model uses cryptocurrency price data to forecast volatility.

Evidence from the source

The model automatically triggers the clusters to scale up 60 minutes prior to anticipated traffic spikes.
This accelerated new cluster deployment times by up to 3.25 times.
“We’ve brought scaling times down from upward of 70 minutes to 25 minutes — less than half the time it used to take.”
“When we see big traffic spikes, we need to scale our clusters as quickly as possible.”
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.

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 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
    4 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
    4 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.
02

Traceability

Sources & provenance

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

  1. MongoDB

    Coinbase customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/coinbase
    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 Jan 17, 2025 · 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 22, 2025 · MongoDB · 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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