11 Organizations managing petabytes of data have different storage requirements from those operating at terabyte scale. Capacity still matters, but so do the architecture behind that capacity, the ability to expand without disruptive migrations, data durability, cyber resilience, performance, hardware flexibility and long-term cost. The best petabyte storage solutions today generally fall into three categories: scale-out object storage deployed in the data center, public cloud object storage and specialized high-performance platforms. The right choice depends on where the data needs to live and how it will be used. This guide compares the leading approaches and solutions for storing petabytes of enterprise data. What is petabyte storage? Petabyte storage refers to infrastructure designed to store and manage datasets measured in petabytes. One petabyte (PB) equals 1,000 terabytes (TB), or one million gigabytes (GB). At this scale, traditional storage architectures can become difficult or expensive to expand. Petabyte-scale environments therefore commonly rely on distributed storage, where data is spread across multiple servers or locations and managed as a unified system. Object storage is particularly well suited to these environments because it uses a flat namespace and metadata-rich objects rather than the hierarchical directories associated with conventional file systems. This architecture can support extremely large numbers of objects while allowing capacity to expand across additional storage nodes. Petabyte-scale storage is increasingly relevant for workloads including: AI and machine learning datasets Enterprise backup and ransomware recovery Data lakes and analytics Scientific and genomic research Media libraries Medical imaging Video surveillance Cloud service provider infrastructure Long-term archives Sovereign and regulated data As datasets grow, the question shifts from simply finding enough capacity to determining how that capacity can be operated efficiently over many years. What are the best petabyte storage solutions? The leading options include Scality RING and ADI, Amazon S3, Microsoft Azure Blob Storage, Google Cloud Storage, Dell ObjectScale, NetApp StorageGRID, Cloudian HyperStore and high-performance platforms such as Pure Storage FlashBlade. They solve different problems. Some prioritize on-premises control and hardware flexibility, while others provide fully managed cloud capacity or specialized performance. 1. Scality RING Best for: Enterprise on-premises and hybrid petabyte-scale storage Scality RING is a software-defined file and object storage platform designed for large-scale enterprise and service-provider environments. It can scale from petabytes to substantially larger deployments while allowing organizations to use industry-standard hardware. RING supports both S3 object and file workloads, making it useful when organizations need to consolidate different types of unstructured data rather than build a separate storage silo for every application. Its scale-out architecture also allows capacity to be expanded without replacing the existing storage environment. Scality describes RING as supporting environments ranging from 1 PB to more than 100 PB, with capacity, performance, applications and other dimensions able to scale independently. Cyber resilience is another major consideration at this scale. RING includes S3 Object Lock, encryption, IAM-based access controls, replication, erasure coding and self-healing capabilities as part of its data protection architecture. Best suited for: Large private clouds Enterprise data lakes AI data repositories Backup and recovery Long-term archives Media and healthcare data Service providers Hybrid-cloud architectures For organizations expecting storage to grow from a few petabytes to tens or hundreds of petabytes, the software-defined architecture can also provide flexibility as server and drive technologies change. 2. Scality ADI Best for: Multi-petabyte AI and data infrastructure Scality ADI is designed for organizations operating at multi-petabyte to exabyte scale, particularly where AI infrastructure, cyber resilience, sustainability and operational automation are major requirements. The platform combines distributed storage with autonomous operations and support for different storage media across the data lifecycle. This is relevant to AI environments because data does not have a single performance requirement throughout its lifetime. Training data might require high-throughput flash storage during active processing, for example, while older datasets may eventually move to capacity-oriented storage. Maintaining those datasets within a broader data infrastructure strategy can reduce the number of independent storage silos that infrastructure teams need to operate. Best suited for: Large-scale AI infrastructure AI training and inference pipelines Sovereign data platforms Multi-petabyte data repositories Environments combining flash, HDD and archival storage Organizations seeking greater storage operational automation Scality positions ADI for multi-petabyte through exabyte environments, while RING remains applicable to a broad range of enterprise object and file storage deployments. 3. Amazon S3 Best for: Cloud-native petabyte storage on AWS Amazon S3 is one of the most established choices for petabyte-scale cloud object storage. AWS operates S3 at enormous scale and supports data lakes, analytics, AI, backup and application storage. S3 eliminates the need to purchase and operate the underlying storage hardware. Organizations can instead consume storage as a cloud service and use multiple S3 storage classes to balance availability, access frequency and cost. AWS also offers capabilities such as lifecycle management, replication, Object Lock and integrations throughout the AWS ecosystem. Amazon describes S3 as a foundation for petabyte-scale analytics and AI workloads. The primary consideration at petabyte scale is economics. Storage capacity is only one component of public cloud cost. Retrieval, API requests, data movement, replication and network egress can materially affect the long-term cost of large datasets. Best suited for: AWS-centric organizations Cloud-native applications Cloud data lakes Analytics AI workloads Organizations that prefer managed infrastructure 4. Microsoft Azure Blob Storage Best for: Microsoft and Azure-centric environments Azure Blob Storage is Microsoft’s cloud object storage service for large amounts of unstructured data. It is a natural option for organizations already using Azure services, particularly when storage needs to integrate with Microsoft’s analytics, AI, application and data-management ecosystem. Multiple access tiers allow organizations to align storage costs with how frequently data is accessed. Azure can therefore support everything from frequently accessed application data to large archival datasets. As with other public cloud platforms, organizations evaluating Azure for multiple petabytes should model total costs rather than comparing capacity pricing alone. Best suited for: Microsoft-centric enterprises Azure applications Cloud analytics Data lakes Backup and archive Hybrid Microsoft environments 5. Google Cloud Storage Best for: Google Cloud analytics and AI environments Google Cloud Storage provides managed object storage within Google Cloud and is frequently used alongside Google’s analytics and AI services. It supports multiple storage classes and can accommodate very large data repositories without requiring customers to manage the underlying storage infrastructure. For organizations building around BigQuery, Vertex AI or the broader Google Cloud ecosystem, keeping petabyte-scale datasets within Google Cloud can simplify integration and data access. Best suited for: Google Cloud environments AI and machine learning Analytics Data lakes Cloud-native applications As with AWS and Azure, organizations should evaluate data access and movement patterns carefully when calculating the long-term cost of storing several petabytes. 6. Dell ObjectScale Best for: Dell-centric enterprise object storage Dell ObjectScale is an S3-compatible object storage platform aimed at enterprise and cloud-scale environments. It is particularly relevant to organizations already standardized on Dell infrastructure or looking for an integrated hardware and software approach to large-scale object storage. ObjectScale can support use cases including analytics, generative AI, backup, archive and other unstructured-data workloads. Best suited for: Existing Dell environments Enterprise object storage Data lakes Backup Large unstructured datasets The tradeoff compared with software-defined alternatives is that infrastructure choices can be more closely tied to a particular vendor ecosystem. 7. NetApp StorageGRID Best for: Enterprises with established NetApp infrastructure NetApp StorageGRID is an S3-compatible object storage platform designed for large unstructured datasets across distributed environments. It provides lifecycle management, data protection and multi-site capabilities, making it applicable to petabyte-scale archives, data lakes and hybrid-cloud environments. Organizations already operating NetApp infrastructure may find StorageGRID particularly attractive because of the broader NetApp ecosystem. Best suited for: NetApp customers Distributed object storage Archives Hybrid cloud Data lakes Large media repositories 8. Cloudian HyperStore Best for: S3-compatible private cloud storage Cloudian HyperStore is an S3-compatible object storage platform designed for private and hybrid-cloud deployments. It is commonly evaluated for backup, archive, data lakes and other workloads where organizations want cloud-style object storage while retaining physical control over infrastructure and data. Its S3 compatibility can also make it useful for organizations seeking to deploy applications designed around the S3 API without putting all associated data in the public cloud. Best suited for: Private clouds Backup repositories Archives S3-compatible applications Hybrid cloud 9. Pure Storage FlashBlade Best for: Performance-intensive petabyte-scale workloads Capacity is not always the primary requirement for petabyte storage. AI, analytics and other data-intensive applications may place substantially greater emphasis on throughput and latency. Pure Storage FlashBlade targets this portion of the market with an all-flash scale-out architecture supporting file and object workloads. Flash-based infrastructure generally costs more per unit of raw capacity than high-density HDD-based object storage, so the economics are most compelling when application performance justifies the additional investment. Best suited for: AI and machine learning High-performance analytics Rapid data processing Performance-intensive file and object workloads Petabyte storage solutions compared SolutionDeploymentPrimary strengthGood fit forScality RINGOn-premises / hybridFlexible scale-out file + object storageEnterprise storage, AI, backup, private cloudScality ADIEnterprise data infrastructureMulti-petabyte AI infrastructure and autonomous operationsAI, sovereign data, large-scale infrastructureAmazon S3Public cloudMassive managed cloud ecosystemAWS applications, data lakes, AIAzure Blob StoragePublic cloudMicrosoft ecosystem integrationAzure workloads and analyticsGoogle Cloud StoragePublic cloudGoogle analytics and AI integrationAI, analytics and cloud applicationsDell ObjectScaleOn-premises / hybridIntegrated enterprise object storageDell-centric infrastructureNetApp StorageGRIDOn-premises / hybridDistributed object storageNetApp environments and archivesCloudian HyperStoreOn-premises / hybridPrivate-cloud S3 storageBackup, archive and private cloudPure Storage FlashBladeOn-premisesAll-flash performancePerformance-intensive AI and analytics There is no single architecture that is optimal for every petabyte-scale workload. Public cloud storage can reduce infrastructure management, while on-premises object storage can provide greater control over data placement, infrastructure and long-term economics. High-performance flash systems address another part of the market where throughput and latency are more important than minimizing capacity cost. How to choose a petabyte storage solution Storage comparisons at smaller capacities often focus heavily on price per terabyte. At petabyte scale, several additional factors become important. Scalability A platform should accommodate expected data growth without requiring periodic migrations to entirely new systems. Look beyond the vendor’s maximum supported capacity. Consider how expansion actually works: Can nodes be added without downtime? Can hardware generations be mixed? Does performance scale with capacity? Is there a practical limit on object counts? Can the system expand across multiple locations? The operational characteristics of scaling can matter as much as the theoretical maximum capacity. Total cost of ownership Small differences in storage economics become substantial when multiplied across several petabytes. A useful TCO model should include: Hardware Software Cloud capacity charges Data retrieval Egress Replication Networking Power and cooling Data center space Administration Hardware refreshes Support Migration costs Public cloud and on-premises storage use fundamentally different economic models, so comparing headline capacity prices alone can produce misleading results. Cyber resilience Petabyte-scale repositories frequently contain backup data, intellectual property, AI datasets or records that may be impossible or extremely expensive to recreate. Storage platforms should therefore be evaluated for capabilities including: Data immutability S3 Object Lock Encryption Identity and access management Multi-factor authentication Least-privilege administration Replication Erasure coding Audit capabilities Geographic protection Cyber resilience should be treated as an architectural requirement rather than an additional feature. Performance A petabyte archive and a petabyte AI repository can have completely different performance requirements. Important metrics may include: Sequential throughput Random access performance Object operations per second Latency Small-object performance Concurrent client performance Restore speed Organizations should test platforms against realistic workloads instead of assuming that capacity automatically translates into performance. S3 compatibility S3 has become a widely adopted API for object storage applications. Strong S3 compatibility can make it easier to connect backup applications, analytics platforms, AI tools and cloud-native software to storage deployed outside AWS. Compatibility can vary between platforms, however. Organizations should validate the specific S3 APIs and application integrations required by their environment. Hardware flexibility At several petabytes, infrastructure has a long operational life. Software-defined storage can allow organizations to introduce newer server and drive generations without replacing the entire platform at once. This can help infrastructure teams take advantage of increasing drive density and hardware efficiency over time. Appliance-based platforms can offer simpler procurement and support but may provide less freedom over hardware selection. Data sovereignty Where data physically resides is increasingly important for regulated industries and organizations operating across multiple jurisdictions. On-premises and private-cloud object storage can provide direct control over data location. Public cloud providers also offer regional controls, but organizations need to understand the provider’s architecture, contractual terms and replication policies. Object storage vs. traditional storage at petabyte scale Object storage has become a common foundation for petabyte-scale unstructured data because the architecture is designed for distributed scale. Traditional NAS remains appropriate for many file-based applications, but very large environments can encounter challenges related to namespace management, scaling and infrastructure complexity. Object storage takes a different approach. Data is stored as objects with unique identifiers and metadata, allowing large distributed systems to manage enormous datasets without depending on a conventional file hierarchy. Some platforms, including Scality RING, provide both object and file access. This can help organizations retain file-based application compatibility while building a scalable object storage foundation. On-premises vs. cloud petabyte storage One of the most important decisions is whether petabytes of data should reside in the organization’s infrastructure, the public cloud or both. Public cloud petabyte storage Cloud services such as Amazon S3, Azure Blob Storage and Google Cloud Storage remove responsibility for operating the physical storage infrastructure. This can be attractive when: Applications already run in the same cloud Capacity requirements fluctuate significantly Rapid provisioning is important Infrastructure management needs to be minimized The tradeoff is that recurring capacity, API, retrieval and data-transfer charges can become significant for large or frequently accessed datasets. On-premises petabyte storage On-premises object storage requires organizations to operate infrastructure but provides greater control over hardware, data location and cost structure. It can be attractive when: Several petabytes will be retained for many years Data is accessed frequently Applications run primarily on-premises Data sovereignty is important Predictable infrastructure economics are required Large datasets would otherwise generate substantial cloud data-transfer costs Hybrid petabyte storage Many enterprises ultimately use both. Frequently accessed or regulated datasets may remain on-premises, while cloud resources support specific applications, secondary copies, collaboration or temporary compute requirements. The important architectural consideration is avoiding isolated storage silos that make data increasingly difficult to move or manage. What is the best petabyte storage solution for AI? AI complicates the petabyte storage decision because a single AI pipeline can have multiple storage requirements. Training and inference may require extremely high throughput, while source datasets, checkpoints, model artifacts and historical data can require enormous amounts of economical capacity. Scality RING is designed to provide high-capacity storage for the capacity-intensive portions of AI data pipelines and can scale from hundreds of terabytes to hundreds of petabytes. For organizations operating broader AI infrastructure at multi-petabyte to exabyte scale, Scality ADI extends this approach across different performance and capacity requirements. All-flash platforms can make sense where maximum storage performance is required, while cloud object storage can be appropriate when the AI compute environment is already located within the same public cloud. The best architecture will often combine performance-oriented and capacity-oriented storage rather than attempting to place every AI dataset on the same storage tier. What is the best petabyte storage solution for backup? Backup storage has a different priority order. Capacity and cost remain important, but immutability, ransomware resistance, restore performance and integration with backup applications become critical. S3-compatible object storage has become particularly useful for this workload because S3 Object Lock can prevent protected backup objects from being modified or deleted during a defined retention period. Scality offers two approaches depending on scale. ARTESCA is designed specifically around cyber-resilient S3 storage for backup and can scale to 8.5 PB, while RING addresses larger and more diverse petabyte-scale environments. Organizations should evaluate backup storage based on the complete recovery architecture rather than capacity alone. How much does petabyte storage cost? There is no universal cost per petabyte. The answer depends on: HDD versus flash Usable versus raw capacity Erasure coding or replication overhead Required performance Number of locations Cloud storage class Retrieval frequency Data-transfer requirements Hardware refresh cycles Support requirements For this reason, cost per usable petabyte over three to five years is usually a more meaningful comparison than the initial purchase price. A public cloud service can have almost no upfront infrastructure cost but significant recurring operating expenses. An on-premises platform requires infrastructure investment but can provide more predictable economics as capacity and data access increase. The best petabyte storage architecture depends on the workload There is no universal winner among today’s petabyte storage solutions. For organizations deeply invested in a public cloud, Amazon S3, Azure Blob Storage and Google Cloud Storage provide effectively elastic managed capacity and close integration with their respective cloud ecosystems. For organizations requiring high-performance flash infrastructure, specialized platforms such as Pure Storage FlashBlade address workloads where storage speed has greater priority. For enterprises that want to maintain control of multi-petabyte datasets while gaining S3 compatibility, cyber resilience and the ability to scale on industry-standard infrastructure, distributed object storage provides another path. Scality RING is designed for this category, supporting scale-out file and object storage from petabytes to very large deployments. Scality ADI extends the architecture toward multi-petabyte and exabyte-scale AI and data infrastructure, while ARTESCA provides a more focused option for immutable backup storage. The most useful comparison therefore starts with the workload: how much data needs to be stored, how quickly it will grow, where applications run, how frequently data is accessed, what performance is required and how the organization expects those requirements to change over the next several years. Frequently asked questions about petabyte storage solutions What is the best storage for petabytes of data? Distributed object storage is generally well suited to petabyte-scale unstructured datasets because capacity can be expanded across multiple storage nodes. Scality RING, Amazon S3, Azure Blob Storage, Google Cloud Storage, Dell ObjectScale, NetApp StorageGRID and Cloudian HyperStore are among the options organizations can evaluate. Is object storage suitable for petabyte-scale data? Yes. Object storage is designed to distribute data across large numbers of storage devices and servers. Modern object storage platforms can scale from terabytes through petabytes and, in some architectures, into exabytes. How many terabytes are in a petabyte? One decimal petabyte equals 1,000 terabytes. Storage vendors typically use decimal units when describing drive and system capacity. Can S3 storage handle petabytes of data? Yes. Amazon S3 operates at substantially greater scale, while multiple on-premises object storage platforms implement the S3 API for private and hybrid-cloud environments. Is cloud storage cheaper for petabyte-scale data? Not necessarily. The answer depends on how long the data is retained and how frequently it is accessed or transferred. Cloud storage can reduce infrastructure management and upfront costs, while on-premises storage can provide more predictable economics for large, persistent and frequently accessed datasets. What should enterprises look for in a petabyte storage solution? Key considerations include scalability, usable capacity, data durability, cyber resilience, performance, S3 compatibility, hardware flexibility, data sovereignty, operational complexity and total cost of ownership. Can petabyte storage scale to exabytes? Some distributed storage architectures are designed to scale beyond petabytes into exabyte-scale environments. Organizations expecting significant long-term growth should evaluate whether expansion can occur non-disruptively and whether capacity, performance and operational management continue to scale with the dataset.
