Kubernetes Storage Hitting AI Workload Limits: Complete Infrastructure Guide

AI and machine learning workloads are different from the web applications Kubernetes was designed to orchestrate. When training models on 65,000-node clusters or running inference services that demand sub-millisecond latency, standard Container Storage Interface implementations hit hard limits. You need storage that can deliver 7 times higher IOPS whilst managing petabytes of checkpoints, embeddings, and versioned datasets.

This guide covers ten critical questions that help you evaluate cloud providers, plan VMware migrations, and implement disaster recovery strategies. Whether you’re running a 10-GPU cluster or planning a 100-node AI infrastructure, you’ll find strategic context and navigation to detailed technical implementations. Each section below answers a critical question and points you toward specialised articles for deeper exploration.

The ten core areas include: identifying storage bottlenecks, understanding GPU utilisation limits, comparing AI versus traditional workloads, performance requirements, cloud provider solutions, enterprise vendor evaluation, business continuity strategies, VMware migration, cost analysis, and implementation steps. Use this as your central navigation hub to understand the full problem space before diving into tactical solutions.

What Are the Main Storage Bottlenecks When Running AI Workloads on Kubernetes?

Storage bottlenecks in Kubernetes AI workloads manifest as GPU idle time whilst waiting for training data, checkpoint writes blocking model progress, and insufficient IOPS preventing parallel data loading across distributed training nodes. These bottlenecks typically stem from CSI drivers optimised for traditional stateless applications rather than the sustained high-throughput, low-latency access patterns required by GPU-accelerated training pipelines. The result is expensive compute resources sitting unused whilst storage struggles to keep pace.

The architectural mismatch between CSI design and AI requirements creates three primary bottleneck sources. First, insufficient IOPS for parallel data loading when dozens of training pods simultaneously read datasets. Second, high latency blocking checkpoint operations—saving 50GB model state can interrupt training for minutes with inadequate storage. Third, inadequate bandwidth for distributed training synchronisation where gradients must flow between nodes continuously.

Detection indicators help you identify when storage limits GPU utilisation. GPU utilisation metrics consistently below 70% suggest storage bottlenecks. Training iteration times dominated by data loading phases rather than computation indicate I/O constraints. Checkpoint operations taking minutes instead of seconds reveal write throughput problems. Google’s 65,000-node GKE cluster benchmarks demonstrate storage becoming the limiting factor for GPU utilisation beyond certain scales.

The Container Storage Interface was designed for general-purpose stateless applications, not for data-intensive AI patterns. Standard CSI implementations lack topology awareness for multi-zone deployments, introduce I/O bottlenecks through external storage dependencies, and can’t handle the parallel distributed access patterns that ML training requires across hundreds of compute nodes. Each vendor’s CSI driver implements storage operations differently, complicating workflow standardisation across backends.

Deep dive: Understanding Container Storage Interface Limitations for AI and Stateful Workloads explores why CSI wasn’t designed for AI workloads and what architectural gaps exist. AI Training and Inference Storage Performance Requirements Benchmarked provides concrete performance thresholds your storage must meet to avoid bottlenecks.

Why Is Storage Becoming the Limiting Factor for GPU/TPU Utilisation in AI Training?

Storage has become the AI training bottleneck because GPU compute performance improved 10x faster than storage I/O capabilities over the past decade. A single NVIDIA A100 GPU can process terabytes of data per hour, but traditional storage systems deliver only gigabytes per second. This mismatch means expensive GPUs costing thousands per day sit idle waiting for storage to feed training data, with utilisation rates dropping to 30-50% in poorly configured systems compared to the 80-95% achievable with optimised storage.

Historical divergence between compute and storage capabilities explains the growing gap. GPU FLOPS increased 100x whilst storage throughput improved only 10x between 2015-2025. This performance imbalance creates economic consequences—GPU idle time costs £50-200 per hour depending on instance type, making storage bottlenecks extremely expensive. When a training cluster with 100 GPUs operates at 50% utilisation due to storage constraints, you waste £5,000-20,000 daily in compute costs.

Modern workload characteristics exacerbate storage demands. Large language model training requires loading hundreds of gigabytes per epoch across dozens of nodes simultaneously. Training datasets measured in terabytes must stream continuously to maintain GPU saturation. The checkpoint problem compounds difficulties—saving 50GB model checkpoints every few hours becomes a major training interruption without NVMe-class storage delivering sustained gigabytes per second write throughput.

Real-world impact extends beyond training delays. Inference services require consistent low-latency reads to serve predictions within SLA windows. Feature stores and artifact registries can store terabyte to petabyte scale checkpoints, embeddings, and versioned datasets. Storage performance directly determines whether your AI infrastructure enables innovation or creates operational friction that blocks data science teams from productive work.

Deep dive: AI Training and Inference Storage Performance Requirements Benchmarked provides MLPerf data showing exactly what performance levels prevent GPU idle time and how different storage architectures perform under production AI workloads. FinOps and Cost Optimisation for AI Storage in Kubernetes Environments helps calculate the true cost of storage bottlenecks against premium storage investment.

How Does Kubernetes Storage Differ for AI/ML Workloads Versus Traditional Applications?

Traditional applications use storage primarily for state persistence with intermittent read/write operations, whilst AI/ML workloads demand sustained high-throughput sequential reads during training, large burst writes for checkpointing, and parallel access from dozens of nodes simultaneously. Standard Kubernetes storage optimised for database transactions—high IOPS with low capacity—fails when training workloads require high bandwidth delivering 1-10 GB/s sustained throughput alongside massive capacity supporting 10-100TB datasets with completely different access patterns.

Access pattern differences fundamentally distinguish AI from traditional workloads. Traditional applications perform random I/O with small block sizes—database queries reading 4KB-64KB blocks, web application sessions storing kilobytes of state. AI training requires sequential streaming of large files, reading multi-gigabyte dataset shards continuously to maintain GPU saturation. Checkpoint operations write entire model states—50GB to 500GB—in single operations demanding sustained high-bandwidth writes that overwhelm storage optimised for small random transactions.

Scale differences compound architectural mismatches. Web applications might deploy 10-100 pods sharing storage through standard ReadWriteOnce volumes. AI training distributes 100+ nodes accessing the same dataset simultaneously, requiring ReadWriteMany capabilities with parallel filesystem semantics. Standard CSI implementations struggle with this concurrency, creating contention that degrades performance as training scales beyond a few dozen nodes.

Persistence model differences reflect distinct workload assumptions. Stateless applications separate compute and state, allowing independent scaling and easy migration. AI training tightly couples GPU compute with data locality for performance—moving computation to data rather than data to computation. This creates scheduler constraints and reduces flexibility compared to stateless architectures. Performance priorities diverge completely: traditional workloads optimise for latency and IOPS supporting interactive users, whilst AI workloads require sustained bandwidth and parallel filesystem capabilities supporting batch processing at massive scale.

Deep dive: Understanding Container Storage Interface Limitations for AI and Stateful Workloads explains why CSI assumptions break for AI workloads and when to consider Kubernetes-native storage alternatives. AI Training and Inference Storage Performance Requirements Benchmarked details specific I/O patterns for different model types with concrete performance measurements.

What Storage Performance Do AI Training and Inference Actually Need?

AI training workloads require sustained sequential read throughput of 1-10 GB/s per node, write throughput of 500MB-2GB/s for checkpointing, and sub-millisecond latency for metadata operations. Inference serving has different requirements: lower throughput at 100-500 MB/s but stricter latency demands delivering single-digit milliseconds for real-time predictions. Azure Container Storage demonstrates these capabilities with 7x IOPS and 4x latency improvements specifically for AI workloads, whilst Google’s Managed Lustre offers tiered performance from 125 to 1,000 MB/s per terabyte.

Training performance profiles demand specific capabilities. High sustained read bandwidth streams dataset shards to GPUs continuously—a 100-node training cluster reading at 2 GB/s per node consumes 200 GB/s aggregate bandwidth. Periodic burst writes save checkpoints without blocking training progress—NVMe-class storage reduces checkpoint times from hours to minutes, enabling more frequent model saves. Parallel access across distributed nodes requires filesystem semantics beyond simple block storage, supporting concurrent reads from hundreds of pods without contention.

Inference performance profiles prioritise different characteristics. Lower bandwidth suffices since model loading happens infrequently, but strict latency requirements ensure predictions complete within SLA windows. High random read IOPS support batch serving where each inference request reads different model sections. Consistency under variable load prevents performance degradation when traffic spikes—inference services must maintain sub-10ms response times whether handling 100 or 10,000 requests per second.

Real-world benchmarks validate these requirements. MLPerf storage results show JuiceFS achieving 72-86% bandwidth utilisation versus traditional filesystems at 40-50%. Google demonstrated Kubernetes scalability by benchmarking a 65,000-node GKE cluster achieving 500 pod bindings per second whilst running 50,000 training pods alongside 15,000 inference pods. These numbers establish ceilings for what’s possible whilst highlighting scheduler throughput and storage orchestration required to operate at this scale.

Deep dive: AI Training and Inference Storage Performance Requirements Benchmarked provides complete performance analysis with MLPerf data, workload-specific I/O patterns, and validation methodology. Implementing High Performance Storage and Changed Block Tracking in Kubernetes covers how to provision NVMe and configure storage tiers to meet these requirements.

Which Cloud Providers Offer Specialised AI Storage Solutions for Kubernetes?

Azure Container Storage 2.0, Google Cloud’s Managed Lustre CSI driver, and AWS’s range of EBS volume types each address AI storage needs differently. Azure provides 7x IOPS improvements and local NVMe integration optimised for checkpointing. Google offers managed parallel filesystem tiers from 125-1,000 MB/s per terabyte ideal for distributed training. AWS relies on high-performance EBS volumes and integration with S3 for data lakes. Your choice depends on whether you prioritise checkpoint performance, distributed training throughput, or object storage integration.

Azure Container Storage emphasises local NVMe performance with transparent failover, making it well-suited for latency-sensitive inference workloads. The v2.0 release achieves built-in support for local NVMe drives and 4 times less latency compared to previous versions. Hierarchical namespace capabilities enhance checkpoint efficiency, whilst Azure-specific CSI drivers integrate tightly with Azure Blob Storage for multi-tier data management.

Google Cloud strengths centre on massive scale-out scenarios. Managed Lustre eliminates operational overhead whilst delivering proven performance at scale—the same infrastructure supporting 65,000-node benchmarks. GKE optimises scheduler throughput to handle 500 pod bindings per second, addressing the orchestration challenges that emerge when storage and compute both scale massively. Tight integration with Google Cloud Storage provides seamless data lake connectivity.

AWS considerations reflect their broader storage ecosystem maturity. Multiple EBS volume types—gp3, io2, io2 Block Express—provide performance tiers matching different workload requirements. Extensive S3 integration supports data lakes and checkpoint archival. FSx for Lustre offers managed parallel filesystem capabilities. However, this flexibility increases operational complexity—you need understanding of which storage service fits which workload pattern, requiring more manual configuration for optimal AI performance than Azure or Google’s more opinionated approaches.

Multi-cloud portability creates another complexity dimension. Containerised storage within Kubernetes enables consistent deployment across AWS, GCP, Azure, and on-premises infrastructure, but each provider’s CSI implementation has quirks. Cross-availability zone data transfer fees and load-balancer charges can easily escape notice until the invoice arrives, particularly with distributed training operations generating significant east-west traffic. Cloud provider lock-in happens gradually through storage dependencies—once you’ve stored multi-petabyte scale training data in provider-specific services, migration costs become prohibitive.

Deep dive: Comparing Cloud Provider Kubernetes Storage Solutions for Machine Learning evaluates Azure, AWS, and GCP storage offerings with configuration examples, detailed performance comparisons, and cost analysis. FinOps and Cost Optimisation for AI Storage in Kubernetes Environments addresses cloud storage pricing and total cost of ownership comparison.

What Enterprise Storage Vendors Support Kubernetes AI Workloads?

Enterprise storage vendors including Portworx, Pure Storage, Nutanix, JuiceFS, and Red Hat OpenShift Data Foundation all provide Kubernetes CSI drivers with varying AI optimisations. Portworx offers VM operations and disaster recovery capabilities attractive for VMware migrations. Pure Storage partners with NVIDIA for AI-specific integrations. JuiceFS demonstrates superior MLPerf benchmark results achieving 72-86% bandwidth utilisation. Nutanix provides software-defined consolidation. Your evaluation should compare MLPerf benchmarks, migration risks, and total cost of ownership beyond initial licensing.

The vendor landscape spans pure-play storage platforms, traditional storage vendors with Kubernetes CSI drivers, and cloud-native solutions built specifically for containerised environments. Each category brings different trade-offs around maturity, feature completeness, and operational complexity. Storage-native vendors like Pure and Nutanix leverage decades of enterprise storage expertise but sometimes struggle with Kubernetes-native patterns. Kubernetes-native solutions like Portworx and JuiceFS eliminate CSI’s limitations but may lack some traditional enterprise features around compliance and audit trails.

Architecture approaches distinguish vendor solutions. Shared-nothing designs where each node provides local storage scale linearly but create data locality constraints. Shared-array designs centralise storage management but introduce network bottlenecks. InfiniBand versus Ethernet networking affects latency and throughput characteristics. These architectural choices have direct implications for performance, cost, and operational complexity in production environments.

Differentiation factors extend beyond raw performance. Some vendors emphasise enterprise features—business continuity and disaster recovery, compliance frameworks, multi-tenancy isolation. Others optimise for raw performance, pursuing MLPerf leadership through aggressive caching and I/O optimisation. Lock-in considerations matter when building critical infrastructure. CSI standardisation provides theoretical portability, but vendor-specific features create switching costs. Advanced capabilities around snapshot management, replication, and encryption often use proprietary APIs beyond CSI’s specification.

Your evaluation framework needs to extend beyond feature checklists to operational realities. Can the vendor’s solution handle disruptive upgrades without downtime? Does it provide consistent security policies across diverse storage arrays? Traditional CSI implementations often complicate data protection and compliance because they lack topology awareness and create upgrade complexity in multi-cloud environments. Ask vendors for proof points from customers running similar AI workloads at your scale, and insist on proof-of-concept testing under production load patterns before committing.

Deep dive: Enterprise Kubernetes Storage Vendor Ecosystem Evaluation Framework provides structured assessment criteria, vendor comparison matrices, objective comparison methodology, and due diligence questions for procurement decisions. VMware to Kubernetes Migration Playbook Using KubeVirt and Storage Rebalancing explores Portworx and Pure Storage solutions specifically for VM migration scenarios.

How Do You Ensure Business Continuity and Disaster Recovery for AI Workloads?

Business continuity for AI workloads requires balancing checkpoint frequency, replication strategy, and recovery time objectives. Changed Block Tracking in Kubernetes reduces backup windows from hours to minutes by tracking only modified blocks. Synchronous replication provides zero data loss for production inference serving but requires sub-10ms latency between sites. Asynchronous replication suits training workloads where losing hours of progress is acceptable if infrastructure fails. Geographic compliance adds complexity, requiring data residency controls for regulated industries.

Modern Kubernetes disaster recovery frameworks implement tiered protection based on workload criticality. Mission-critical workloads demand synchronous replication delivering zero RPO with instantaneous failover across clusters or regions. Business-critical applications use asynchronous replication with configurable RPOs—typically 15 minutes to one hour—balancing performance against protection. Less critical workloads rely on periodic snapshots with longer recovery windows and accepted data loss.

Technical implementation requires coordination across multiple layers. Control plane backup captures cluster state and configuration stored in etcd, whilst persistent volume backup protects application data. Backup solutions must handle both to enable complete recovery. Automated failover recipes reduce manual intervention during unplanned outages, but they require careful testing—simulated failures often expose gaps in recovery procedures that documentation misses.

RPO and RTO targets drive architectural decisions with direct cost implications. A one-hour RPO with synchronous replication to a secondary region costs substantially more than daily snapshots with 24-hour RPO. For AI workloads, the calculation includes the cost of rerunning training jobs if checkpoints are lost. A training run consuming thousands of GPU hours makes aggressive checkpoint protection economically rational even when storage costs increase. Compliance considerations add another dimension—EMEA data residency, healthcare data sovereignty, and financial services audit requirements constrain replication topology and storage location choices.

Changed Block Tracking transforms backup economics for large AI datasets. CBT identifies only the storage blocks modified since the last snapshot, eliminating the need to scan entire volumes. For a 10TB training dataset where daily changes affect 500GB, CBT-based backup transfers only those 500GB rather than scanning all 10TB. This reduces backup windows from hours to minutes and decreases storage bandwidth consumption, enabling more frequent checkpoint saves without overwhelming storage infrastructure.

Deep dive: Business Continuity and Disaster Recovery Strategies for Kubernetes Storage explores tiered protection frameworks, RPO/RTO planning, automated failover, risk assessment, and recovery testing methodologies. Implementing High Performance Storage and Changed Block Tracking in Kubernetes provides step-by-step CBT configuration with alpha feature gate enablement for Kubernetes 1.32+.

How Do You Migrate from VMware to Kubernetes for AI Infrastructure?

VMware-to-Kubernetes migration for AI workloads uses KubeVirt to run virtual machines directly within Kubernetes clusters, preserving operational investments whilst transitioning infrastructure. Storage migration requires mapping VMware constructs to Kubernetes equivalents: vMotion becomes Enhanced Storage Migration, Storage DRS becomes Portworx Autopilot, and vSAN translates to container-native storage platforms. The migration follows phased approaches—assessment, proof-of-concept, pilot workloads, production rollout—with timelines ranging from 6-18 months depending on infrastructure complexity and team skills.

Migration drivers span economic and strategic factors. Broadcom’s VMware licensing changes force cost reassessments—some organisations face 300-500% price increases. Desire for Kubernetes standardisation eliminates technology fragmentation across development, staging, and production environments. Cloud-native tooling advantages include declarative infrastructure as code, built-in horizontal scaling, and integration with modern CI/CD pipelines that VMware’s ecosystem struggles to match.

Operational parity mapping ensures you don’t sacrifice proven capabilities during migration. KubeVirt enables running VMs on Kubernetes without modifying applications, gaining Kubernetes orchestration benefits whilst maintaining workload compatibility. Advanced VM operations for Kubernetes include live storage migration—the Kubernetes equivalent to Storage vMotion—automated rebalancing when adding nodes, and maintaining VM uptime during host failures and maintenance. Storage rebalancing becomes critical as you move hundreds or thousands of VMs onto Kubernetes nodes.

Real-world migration experiences reveal challenges beyond vendor promises. Michelin achieved 44% cost reduction migrating from Tanzu to open-source Kubernetes across 42 locations with an 11-person team, completing the transition in six months. Their success came from deep technology knowledge and realistic timelines—not assumptions about seamless migration. Storage integration testing under production load patterns proved essential before committing to cutover.

Risk mitigation requires phased approaches with explicit rollback plans. Lift-and-shift moves existing VMs to KubeVirt with minimal modification, validating performance and compliance requirements before full commitment. Parallel infrastructure during transition enables gradual workload migration, maintaining production stability whilst teams build operational confidence. Application modernisation opportunities emerge post-migration—refactoring workloads to leverage cloud-native services whilst retaining operational familiarity. Staff training investments determine whether migration succeeds or creates operational chaos.

Deep dive: VMware to Kubernetes Migration Playbook Using KubeVirt and Storage Rebalancing provides complete migration strategy with step-by-step planning, timelines, staffing requirements, storage configuration patterns, operational parity mapping, and troubleshooting guidance.

How Much Does High-Performance Kubernetes Storage Cost for AI Workloads?

High-performance Kubernetes storage costs vary dramatically by architecture and scale. Cloud NVMe storage costs £0.30-0.80 per GB-month versus £0.10-0.20 for standard SSD. Enterprise solutions range from £500-5,000 per terabyte annually depending on vendor and features. However, storage bottlenecks that reduce GPU utilisation from 90% to 50% waste £1,000-4,000 daily in GPU costs, making performance storage economically justified. Hidden costs include orphaned PersistentVolumeClaims accumulating after experiments, over-provisioned volumes consuming unnecessary capacity, and inappropriate tier selection.

Direct storage costs represent only one dimension of economic analysis. Cloud pricing structures vary by region and service tier. Enterprise licensing often includes base capacity with additional per-terabyte charges. Operational overhead—staff time managing storage, backup infrastructure, disaster recovery testing—accumulates invisibly. These direct costs are measurable and controllable through vendor negotiations and capacity planning.

Opportunity costs dwarf direct storage expenses when infrastructure limits productivity. GPU idle time from storage bottlenecks costs £50-200 per hour per GPU. A 50-node training cluster with each node running 8 GPUs, operating at 50% utilisation due to storage constraints, wastes £20,000-80,000 daily. This context makes premium storage delivering 90% GPU utilisation economically obvious—spending an additional £10,000 monthly on storage to save £400,000 monthly in wasted compute represents dramatic ROI.

Hidden cost sources accumulate through operational patterns. Feature stores and artifact registries store multi-petabyte environments of checkpoints, embeddings, and versioned datasets. Without lifecycle policies, snapshot accumulation continues indefinitely—three months of daily checkpoints at 500GB each consumes 45TB. Orphaned volumes persist after pods terminate, continuing to accrue charges. Cross-availability zone data transfer fees for distributed training and load-balancer charges for inference services accumulate invisibly until invoices arrive.

Optimisation strategies balance cost against capability requirements. Rightsizing eliminates over-provisioned volumes—75% of organisations provision 2-3x more storage than workloads actually consume. Tier selection based on access patterns moves cold data to archive storage whilst keeping hot training datasets on NVMe. Lifecycle policies automatically clean up ephemeral artifacts and old checkpoints. Governance policies prevent runaway storage provisioning whilst maintaining developer velocity through namespace quotas and resource limits.

Deep dive: FinOps and Cost Optimisation for AI Storage in Kubernetes Environments covers complete TCO calculators, cost visibility tools, budget planning templates, multi-tier storage architectures, governance frameworks, chargeback models, and continuous optimisation strategies. AI Training and Inference Storage Performance Requirements Benchmarked provides cost-performance trade-off analysis to justify premium storage investment.

What Implementation Steps Are Required for High-Performance AI Storage?

Implementing high-performance AI storage in Kubernetes requires configuring storage classes for different workload types, provisioning appropriate CSI drivers—Azure Container Storage, Managed Lustre, or enterprise vendors—setting up Changed Block Tracking for efficient backups, and establishing monitoring for performance validation. Implementation follows: prerequisites assessment, storage class configuration, CSI driver deployment, volume provisioning, incremental backup setup, and validation testing. Teams should expect 2-4 weeks for initial deployment and 4-8 weeks for production hardening including disaster recovery testing.

Prerequisites assessment establishes foundation requirements. Kubernetes version requirements vary by storage solution—Azure Container Storage supports 1.29+, Changed Block Tracking requires 1.32+ with alpha feature gates enabled. CSI driver compatibility matrices determine which storage backend versions work with your Kubernetes distribution. Storage backend availability—whether cloud provider services or enterprise storage arrays—must be verified before beginning implementation. Network topology impacts performance, particularly for distributed storage requiring low-latency node-to-node communication.

Storage class configuration determines performance characteristics and workload mapping. Training jobs typically use high-throughput storage classes optimised for sequential writes, whilst inference services need low-latency classes tuned for random reads. The separation allows matching storage tiers to workload economics—you don’t need to pay for premium storage for ephemeral training artifacts that can be regenerated. Reclaim policies prevent data loss during PVC deletion whilst enabling cleanup of temporary volumes. Volume binding modes control whether provisioning happens immediately or waits for pod scheduling.

CSI driver deployment integrates storage backends with Kubernetes. Cloud provider drivers—Azure Container Storage CSI, Google Filestore CSI, AWS EBS CSI—install through Helm charts or Kubernetes operators. Enterprise vendor drivers require vendor-specific installation procedures, typically involving privileged DaemonSets running on all nodes. Driver configuration specifies backend connectivity, credentials management through Kubernetes Secrets, and integration with storage controllers. Testing validates driver operation before proceeding to production workload deployment.

Volume provisioning patterns vary by workload type. Dynamic provisioning simplifies initial setup but requires governance policies for quota management and cost control. Static provisioning pre-creates PersistentVolumes for predictable capacity allocation. Volume snapshots and cloning enable workflow patterns essential to AI development: capturing model state at specific training epochs, creating reproducible datasets for experimentation, implementing efficient staging environments. The CSI snapshot controller coordinates between Kubernetes and storage backends, but different vendors implement snapshot semantics differently.

Changed Block Tracking enablement requires Kubernetes 1.32+ with alpha feature gates. Feature gate configuration activates CBT capabilities in the API server and kubelet. VolumeSnapshot CRD installation provides snapshot management APIs. Storage provider compatibility verification ensures your chosen storage backend supports CBT—not all CSI drivers implement the full specification. Backup integration connects snapshot capabilities with backup tools like Velero or vendor-specific solutions.

Validation testing confirms implementation meets performance requirements. Synthetic benchmarks using fio or similar tools establish baseline throughput and latency under controlled conditions. Production workload testing validates performance under actual AI training loads—MLPerf benchmarks provide standardised tests. Failover testing confirms disaster recovery procedures work as designed. Cost tracking implementation enables ongoing financial management and optimisation.

Deep dive: Implementing High Performance Storage and Changed Block Tracking in Kubernetes covers complete implementation with YAML examples, detailed configuration, performance tuning, and operational patterns. Before implementation, choose between cloud provider solutions and enterprise vendor options based on your infrastructure strategy.

📚 Kubernetes AI Storage Resource Library

This guide covers the complete Kubernetes storage landscape for AI workloads. Each article below provides focused, actionable guidance on specific aspects of storage strategy and implementation.

🔧 Technical Foundation

Understanding Container Storage Interface Limitations for AI and Stateful Workloads CSI architectural constraints, vendor-specific extensions, I/O pattern challenges, and when to consider Kubernetes-native storage alternatives. Read this if you’re experiencing GPU idle time and suspect storage bottlenecks.

Implementing High Performance Storage and Changed Block Tracking in Kubernetes Storage class configuration, persistent volume claims, snapshot management, CBT implementation, and performance tuning for production environments. Read this if you need step-by-step implementation guidance.

📊 Performance and Providers

AI Training and Inference Storage Performance Requirements Benchmarked MLPerf analysis, throughput efficiency metrics, latency consistency measurements, and real-world performance validation across storage architectures. Read this if you need to justify storage investments with concrete performance data.

Comparing Cloud Provider Kubernetes Storage Solutions for Machine Learning Azure, AWS, and GCP storage offerings evaluated on performance, cost, integration capabilities, and multi-cloud portability considerations. Read this if you’re choosing between cloud provider storage services.

🏢 Enterprise Strategy

Enterprise Kubernetes Storage Vendor Ecosystem Evaluation Framework Vendor assessment criteria, comparison matrices, feature completeness analysis, and due diligence questions for procurement decisions. Read this if you’re evaluating enterprise storage vendors.

Business Continuity and Disaster Recovery Strategies for Kubernetes Storage Tiered protection frameworks, synchronous and asynchronous replication, automated failover, RPO/RTO planning, and recovery testing. Read this if you need to design BCDR for AI workloads.

💰 Migration and Economics

VMware to Kubernetes Migration Playbook Using KubeVirt and Storage Rebalancing Migration planning, KubeVirt setup, storage migration patterns, performance validation, and operational maturity preservation during transition. Read this if you’re planning a VMware-to-Kubernetes migration.

FinOps and Cost Optimisation for AI Storage in Kubernetes Environments Cost visibility tools, multi-tier storage architectures, governance frameworks, chargeback models, and automated cleanup policies. Read this if storage costs are growing faster than expected.

FAQ

What is Changed Block Tracking and why does it matter for AI workloads?

Changed Block Tracking is a Kubernetes alpha feature that monitors which storage blocks have been modified since the last snapshot, enabling incremental backups that transfer only changed data. For AI workloads with multi-day training runs and 50-100GB checkpoints, CBT reduces backup windows from hours to minutes and enables more frequent checkpoint saves without overwhelming storage bandwidth. This becomes critical when you need to save model state every few hours during expensive GPU training runs.

Learn more in our guides to Business Continuity and Disaster Recovery and Implementing Changed Block Tracking.

Can I run AI workloads on standard Kubernetes storage classes?

You can run AI workloads on standard storage classes for development and small-scale experimentation, but you’ll encounter severe performance bottlenecks at production scale. Standard CSI implementations typically deliver 100-500 MB/s throughput versus the 1-10 GB/s sustained bandwidth required for distributed training. GPU utilisation will drop to 30-50% as expensive compute resources wait for storage, making premium storage economically justified despite higher per-GB costs.

See our performance requirements benchmark for specific thresholds.

Should I use object storage or block storage for model training?

Object storage works well for initial dataset storage and model checkpointing due to scalability and cost-effectiveness, but most training frameworks require block or file storage for active training data due to POSIX filesystem expectations. The optimal approach uses hierarchical tiering: object storage (S3, Azure Blob, GCS) for cold data and checkpoints, NVMe or high-performance SSD for active training datasets. Azure’s hierarchical namespace and Google’s Cloud Storage integration specifically address this multi-tier requirement.

Explore storage architecture options in our cloud provider comparison.

How do I know if storage is bottlenecking my GPU training?

Monitor GPU utilisation metrics—if GPUs consistently run below 70% utilisation during training, storage is likely the bottleneck. Specific indicators include: training iterations taking 2-3x longer than expected, checkpoint operations blocking training progress for minutes, and storage I/O wait time dominating system metrics. Use Kubernetes monitoring tools to track PersistentVolume throughput and compare against your workload’s theoretical data loading requirements.

Learn to identify and resolve bottlenecks in our CSI limitations article.

What’s the difference between synchronous and asynchronous replication for Kubernetes?

Synchronous replication writes data to both primary and replica storage before acknowledging the write operation, guaranteeing zero data loss but requiring sub-10ms latency between sites—typically within the same metro area. Asynchronous replication acknowledges writes immediately and replicates in the background, enabling geographic separation but accepting potential data loss if the primary site fails. For AI workloads, use synchronous replication for production inference serving and asynchronous for training where losing hours of progress is acceptable.

Review complete BCDR strategies in our disaster recovery guide.

Do I need specialised storage if I’m only running inference, not training?

Inference workloads have different storage requirements than training—lower throughput at 100-500 MB/s but stricter latency demands delivering single-digit milliseconds for real-time predictions. Standard high-IOPS storage often suffices for inference, whereas training demands sustained high-bandwidth storage. However, if you’re serving models to hundreds or thousands of concurrent requests, you’ll benefit from NVMe-class storage to reduce model loading latency and improve serving throughput.

Compare storage requirements for training versus inference in our performance benchmarks article.

How much does it cost to migrate from VMware to Kubernetes storage?

Migration costs include licensing for KubeVirt and storage solutions—Portworx typically costs £500-2,000 per terabyte annually—staff training and consulting ranging from £50,000-200,000 depending on scale, temporary parallel infrastructure during migration creating 6-12 months of dual costs, and opportunity cost of delayed AI projects during transition. However, eliminating VMware licensing at £200-500 per VM annually and gaining cloud-native tooling often produces 3-year ROI of 150-300% for organisations running 100+ VMs.

Explore complete migration planning in our VMware to Kubernetes playbook and FinOps cost analysis.

What’s the difference between storage for web applications versus AI workloads on Kubernetes?

Web applications typically require modest storage for configuration, session data, and small databases—usually measured in gigabytes. AI workloads store datasets, model checkpoints, and artifacts measured in terabytes or petabytes. Web apps predominantly read data with occasional writes, whilst training jobs write checkpoint data every few minutes at sustained high throughput. Inference services require consistent low-latency reads under variable request loads that cause performance degradation with traditional storage.

When should you use synchronous versus asynchronous replication for Kubernetes storage?

Synchronous replication provides zero RPO by writing to primary and replica storage simultaneously before acknowledging completion. Use it for mission-critical workloads where data loss is unacceptable and you can tolerate write latency increases. Asynchronous replication writes to primary storage first, replicating to secondary storage afterwards. Choose it for business-critical workloads where 15-60 minute RPO is acceptable and write performance matters more than perfect data protection.

How do you prevent storage costs from spiralling as AI workloads scale?

Implement multi-tier storage policies that automatically migrate data from premium to standard to archive tiers based on access patterns. Use namespace quotas and resource limits to prevent uncontrolled provisioning. Deploy automated cleanup policies for ephemeral artifacts and old checkpoints. Enable cost visibility dashboards that show storage consumption by team, project, or workload. Make storage costs visible through chargeback or showback to create organisational awareness and accountability.

Conclusion

Kubernetes storage for AI workloads requires rethinking assumptions built around stateless web applications. The Container Storage Interface provides a foundation, but production AI infrastructure demands capabilities beyond CSI’s original design: massive scalability, consistent performance under mixed workloads, efficient snapshot management for model versioning, and disaster recovery that protects both cluster state and petabytes of training data.

Your path forward depends on current infrastructure and strategic goals. Teams migrating from VMware need different guidance than greenfield Kubernetes deployments. Cloud-native organisations optimise for different constraints than those maintaining on-premises infrastructure. The articles in this guide provide focused, actionable direction for each scenario—from evaluating cloud providers to implementing Changed Block Tracking to optimising storage costs.

The storage layer ultimately enables or constrains your AI capabilities. Understanding these trade-offs upfront, before accumulating technical debt through expedient decisions, determines whether Kubernetes becomes a platform for innovation or a source of operational friction. Navigate to the specialised articles above to develop expertise in the areas most relevant to your infrastructure challenges.