Reported impact
Business outcome
Encord simplified operations, improved customer training throughput, and created a globally scalable base for fast-growing AI data workloads.
- Productivity Improvement
- Scalability
- Speed to Market
Google CloudSoftware / SaaSApplication RefactoringAI and Machine LearningCloud NativeProductivity ImprovementScalabilitySpeed to MarketUndated Google CloudGC#4285f4Software / SaaSGlobalEncord needed an infrastructure platform that could keep pace with rapid growth in data-intensive AI workloads while staying simple enough for a small startup team to operate efficiently across geographies.35% reduction in customer dataset size, with optimized storage on Google CloudArchived · Aug 12, 20265Application RefactoringCloud GPUs · Cloud SQL · Compute Engine0 2026-08-12 5
Encord
Application Refactoring
Encord needed an infrastructure platform that could keep pace with rapid growth in data-intensive AI workloads while staying simple enough for a small startup team to operate efficiently across geographies.
35% reduction in customer dataset size, with optimized storage on Google Cloud
350% increase in
Encord delivers 60% faster model training for customers, with the help of
Solution
Encord uses Google Cloud to address this challenge: Encord needed an infrastructure platform that could keep pace with rapid growth in data-intensive AI workloads while staying simple enough for a small startup team to operate efficiently across geographies. Cloud GPUs provide the training and inference horsepower customers need for faster model-development pipelines, serving as the compute layer. Compute Engine hosts the GPU-backed processing environment that can be placed near customer data for low-latency operation, serving as the compute layer. Cloud SQL stores the operational data that supports Encord’s data platform at scale, serving as the storage layer. Google Kubernetes Engine supports the broader scalable platform architecture as usage grows, serving as the orchestration layer. Gemini is being adopted to speed up Encord’s internal coding and development work, serving as the compute layer.
Data flow
Customer datasets are processed on regional GPU-backed Compute Engine infrastructure, Cloud SQL stores platform state, GKE supports the service runtime, and Encord can keep data close to where it is created so robotics and autonomy customers avoid excess latency and transfer cost.
Solution architecture
- Compute
- Cloud GPUs provide the training and inference horsepower customers need for faster model-development pipelines
- Compute Engine hosts the GPU-backed processing environment that can be placed near customer data for low-latency operation
- Gemini is being adopted to speed up Encord’s internal coding and development work
- Storage
- Cloud SQL stores the operational data that supports Encord’s data platform at scale
- Orchestration
- Google Kubernetes Engine supports the broader scalable platform architecture as usage grows
Architecture clues
- Global regional availability keeps data close to robotics and vehicle deployments
- Google Cloud usage grew 350% in one year to support expansion
- The team views Gemini as a next step for internal developer productivity
Evidence from the source
35% reduction in customer dataset size, with optimized storage on Google Cloud
350% increase in
Encord delivers 60% faster model training for customers, with the help of
Encord delivers 60% faster model training for customers, with the help of Cloud GPU pipelines.
Its powerful Cloud SQL databases and compute services provided the necessary infrastructure to process complex datasets.
This automation depends on the immense computing power of Cloud GPUs running on Compute Engine.
Transparency Evidence & methodology
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 value95/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth20/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/253 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/206 direct source quotes retained.
- Date provenance0/10The publication date has no documented provenance.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards 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.
- https://cloud.google.com/customers/encordGoogle CloudFirst-party
Encord 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 Aug 12, 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 Jul 16, 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.
Related
Similar use cases
Google Cloud Sample Archived · Aug 29, 2026 WeSpeak Building that product required infrastructure that would never go down. Early on, WeSpeak ran on a virtual private server that could fail without warning, taking the entire platform offline. For a hotel waiting on a… 70% of hotel guest inquiries automated end to end Cloud Run · Gemini · Gemini Flash Google Cloud Sample Archived · Jul 23, 2026 FlutterFlow FlutterFlow's products utilize Firestore's native real-time sync features to enable collaboration at production scale for teams building together Firestore became the foundation that let the company scale from zero to 3.4 million builders while serving more… Cloud Run · Cloud Run functions · Firebase AI Logic Google Cloud Sample Archived · Jun 11, 2026 InMobi InMobi needed to migrate a latency-sensitive advertising platform from another hyperscaler, preserve zero-downtime service at massive QPS, and create a more efficient real-time serving and analytics environment for both… 7 million peak qps Bigtable · Cloud Composer · Cloud SQL