2.2K IT professionals face an avalanche of unstructured data — driven by AI, IoT, and high-resolution media — creating the need for storage solutions that are scalable, flexible, and cost-effective to manage it all. By 2026, Gartner forecasts large enterprises will triple their unstructured data capacity compared to 2021, and IDC estimates that over 80% of enterprise data is already unstructured, with AI workloads as a major driver. So, what exactly is object storage, and why has it become the go-to architecture for managing unstructured data at scale? Let’s break it down. The challenges of unstructured data To understand the need for object storage, we first need to look at the challenge of unstructured data — information that doesn’t fit neatly into spreadsheets or traditional databases. Think emails, images, audio and video files, IoT streams, AI training sets, and documents.This list is now broadening dramatically to include all kinds of sensor and telemetry data from smart connected devices — for applications ranging from healthcare and agriculture to transportation and space exploration. Unstructured data is both a business goldmine and an operational headache. Unlike structured databases, it has been growing at breakneck speed for nearly two decades and spans an increasingly diverse variety of formats, which is now accelerating further as described above. This sheer volume and diversity of unstructured data create several inherent IT challenges: Explosive growth: Double-digit annual increases quickly pushed organizations from terabytes to petabytes over the last years and now the reality for many businesses is the exascale-era has arrived, with exabytes of data expected to overwhelm organizations and any remaining legacy storage systems. Poor organization: Without a predefined schema, unstructured data is hard to search, tag, and govern. Silos everywhere: Files scatter across clouds, devices, and data centers, complicating access and visibility. Security and compliance risks: Sensitive information hides inside PDFs, images, or emails, making it difficult to consistently enforce policies. Cost inefficiency: Storing everything “just in case” drives up expenses while slowing performance. Lifecycle complexity: Data moves from active use to archive, and managing these transitions without automation adds overhead. Left unchecked, these issues stall analytics initiatives, decrease the flow of timely data to AI pipelines, and expose organizations to unnecessary risk. That’s why scalable, API-driven object storage has become the preferred storage infrastructure for managing the abundance of today’s unstructured data. What is object storage? Put simply, object storage is a type of data storage that manages data as objects, consisting of the data itself plus the object’s descriptive attributes (metadata). This is different from other storage architectures that manage data as a file hierarchy (file storage systems), or as blocks within sectors and tracks (block storage). Object storage, also known as object-based storage, offers a really simple way to store data. It organizes information into distinct containers of flexible sizes and uses keys to retrieve the specific data you’re looking for. Three fundamental building blocks make up object storage: Key: A unique identifier for the object Value: The data itself, which may be a document, image, video, etc. Metadata: Descriptive properties (such as when it was created, file size, owner, access rules) that tell the system how to handle the object Think of it like your phone’s contact list: the value is the phone number, the key is the contact’s name, and the metadata is all the extra details like email, birthday, or a photo. With object storage, you don’t need to know where the data you want is located; you just provide the key. Then the system fetches your object for you. Unlike traditional file systems, object storage can also support custom metadata, letting organizations tag and organize data with business-specific attributes. In a legacy environment, this level of flexibility would require complex databases and extra management overhead. With object storage, it’s built in — keeping things both powerful and simple. How does object storage architecture work? In a typical object storage architecture, data is stored as objects inside logical containers called buckets. Each object carries rich metadata that can include hundreds of attributes — security tags, compliance rules, even AI dataset labels — making it ideal for diverse, large-scale datasets. Access is provided through APIs, such as Amazon S3-compatible, OpenStack Swift, or Azure Blob APIs. This makes object storage inherently cloud-native and easy to integrate with applications, data lakes, and AI/analytics pipelines. Modern object storage architectures are designed for resilience. They protect data by distributing it across nodes and sites using replication or erasure coding, and support immutability (via object lock/WORM/retention policies) to lock data against changes or deletion — making backups ransomware-resistant and compliant with industry regulations. Think of it like GPS coordinates: every location has a unique set of numbers that pinpoint it on a map. In the same way, each object in storage has a unique identifier that takes you directly to the right data, no matter how many objects exist. How is object storage different from file and block storage? File and block storage organize data in very different ways, but both rely on structured paths or fixed blocks. Object storage takes another approach — it stores the data, metadata, and a unique identifier together in a flat namespace, eliminating the need for complex hierarchies. Block storage is raw capacity, divided into fixed-size chunks. To be useful, you add a file system on top. On its own, it’s unstructured — like an empty parking lot. File storage adds structure by creating directories and paths on top of block storage. It’s like painting marked parking spaces in the lot — organized and familiar, but harder to scale as the lot grows. Object storage goes further. Each object has a unique identifier and rich metadata, so the system can retrieve it directly — like a valet service that brings your car when you hand over your ticket. It operates the same whether there are hundreds of cars or thousands. Comparison of block, file, and object storage This difference in architecture is also what makes object storage uniquely capable of scaling to billions of objects across petabytes and exabytes of data. What are the top use cases for object storage? When it comes to data-intensive workloads and industries, object storage is the ideal foundation to support: Backup & archiving: Long-term, cost-effective data retention with instant retrieval. AI & machine learning: Massive unstructured datasets (images, video, sensor data) for training and inference. Media & entertainment: Management and delivery of 4K/8K video archives and streaming assets. IoT: Ingest and management of high-volume sensor data from distributed devices — powering everything from smart factories to connected vehicles. Agriculture: Collection and analysis of data from smart devices to optimize crop yields, monitor soil conditions, and prevent pest-related diseases. Transportation: Capture and processing of unstructured data from airlines, railways, and autonomous vehicles, with long-term archiving for analysis and compliance. Beyond these industry and cross-functional use cases, AI has become one of the most transformative drivers of object storage adoption — and deserves a closer look. How does object storage support AI in the modern era? Industry experts, including TechTarget, recommend object storage as the go-to architecture for unstructured data, AI pipelines, and petabyte-scale datasets — see Why object storage for AI makes sense . It is already the case today that the major AI players use cloud object storage as the basis of all of their AI model data preparation and training, as we see OpenAI and Microsoft leverage Azure Blob Storage, Amazon and others using AWS S3. AI and machine learning thrive on massive amounts of unstructured data — from self-driving vehicles generating petabytes of sensor data to AI models analyzing medical images — and object storage has become the backbone of these workloads by: Scaling effortlessly to handle petabytes of training data without slowing performance. Leveraging rich metadata to tag, organize, and quickly retrieve datasets for both training and inference. Integrating seamlessly through S3‑compatible, OpenStack Swift, or Azure Blob APIs — making it easy to connect with AI frameworks and pipelines. Delivering durability and reliability, ensuring your AI workflows consistently yield reproducible results. AI-specific use cases and examples powered by object storage: Retrieval-Augmented Generation (RAG): Object storage serves as the scalable, metadata-rich knowledge base that supplies AI models with accurate business data.Example: A bank uses object storage to connect internal documents with chatbots for customer support. Vector databases & embeddings: Modern AI apps integrate object storage with vector DBs (e.g., Milvus, Pinecone, Weaviate) to power smarter search and recommendation tools.Example: An e-commerce company pairs product images with vector embeddings in object storage to improve visual search. Synthetic data generation: AI teams use object storage to cost-effectively store and manage the massive artificially generated training data needed when real-world data is limited or sensitive.Example: An automotive manufacturer generates synthetic road scenarios for training self-driving algorithms. Digital twins & simulation: Industries like manufacturing, energy, and smart cities rely on object storage to store the huge amounts of test and sensor data needed for digital twins.Example: A utility provider builds digital twins of power grids to predict outages and optimize energy flow. Edge AI: From retail video analytics to autonomous vehicles, object storage at the edge enables the processing of data quickly near the source, before sending it to the cloud or datacenter for deeper analysis.Example: A retailer uses edge object storage to analyze in-store camera feeds in real time while archiving video centrally. What are the benefits of object storage? Modern object storage solutions deliver a combination of scale, flexibility, and resilience that traditional file and block systems can’t match. Key characteristics include: Durability & availability: Geo-distribution with built-in redundancy ensures data survives hardware failures and keeps information continuously accessible (often quoted at 11+ nines). Massive scale-out: Store billions of objects without performance degradation, and handle exponential growth — especially for AI training data and unstructured workloads. Immutability: Objects can be locked against changes or deletion, making backups ransomware-resistant and compliant with industry regulations. Just as scalability and performance make object storage ideal for AI workloads, its intrinsic immutability makes it equally powerful for backup and ransomware protection — one of today’s most critical IT priorities. Security & compliance: Encryption, WORM (write once, read many), access control, certifications, and retention policies to protect sensitive data and support regulated industries. Cloud-native compatibility: Broad S3, or other standard API, support for seamless integration with your existing tools and applications — including data lakes, AI pipelines, and analytics tools. Performance: High throughput and low latency required by demanding workloads like AI and analytics can run in parallel across huge datasets. Rich metadata for AI: Tagging, categorizing, and searching training datasets efficiently — and enabling semantic search when paired with vector databases. Gartner’s 2025 Hype Cycle for Storage Technologies notes that enterprises using AI-optimized object storage cut model training times by up to 25% compared to legacy NAS. Multidimensional scale : Storage and compute resources are decoupled/disaggregated, allowing them to scale independently across diverse workloads. How do I choose the right object storage solution? When evaluating object storage, it’s important to look beyond capacity and think about how the system will support your workloads and IT resources over time. Key considerations: Where will it live? On-premises, cloud, or hybrid/multi-cloud — your deployment model will shape cost, control, and performance. Who will manage it? Self-managed gives in-house IT maximum control. Fully managed reduces IT overhead, but comes at a higher subscription cost. What features matter most? Look for durability, scalability, security, API compatibility, and the right performance tiers. Compliance & regulatory readiness: Ensure support for GDPR, HIPAA, DORA, or the upcoming EU AI Act, as required in your region, so your AI pipelines remain auditable and trustworthy. How much will it cost? Balance CapEx vs. OpEx. Don’t forget to account for “hidden fees” like cloud egress charges, as well as the economics of scaling over time. Can it scale with you? From terabytes to petabytes and beyond, ensure it supports future workloads like AI, IoT, and streaming. How simple is it to operate? Evaluate usability: unified dashboards, automation, monitoring, and multi-tenant controls all reduce complexity. Ultimately, the right object storage solution depends on how well it aligns with your business model and priorities. A pharma company may zero in on high-throughput performance to accelerate research, while a managed service provider (MSP) may place more value on automation, multi-tenant isolation, and ease of management. The key is to map these considerations to what matters most for your workloads and long-term strategy. FAQs about object storage Q: What are examples of object storage?A: Object storage has multiple deployment models to choose from: Hyperscale, public cloud subscription services: Pay-as-you-go on 3rd-party object storage infrastructure that’s managed for you, and instantly scalable, but often with egress fees and less control over where data resides. Examples: Amazon S3, Google Cloud Storage , Microsoft Azure Blob Storage Private cloud / on-premises solutions: Storage deployed inside your own datacenter (or colocation facility) that behaves like a cloud service (API-driven, elastic, multi-tenant), and runs on infrastructure you own or lease, fully under your control. Examples: Scality RING, Scality ARTESCA Hybrid cloud / enterprise offerings: A mix of on-premises + public cloud storage, often managed as one. Keeps sensitive or high-performance workloads local while offloading backups or archives to the cloud.Examples: HPE GreenLake for Object Storage Q: Why is S3 called object storage?A: The term “S3” comes from “Amazon S3” branding when the service launched in 2006. S3 = simple storage service. Over time, Amazon S3 became synonymous with object storage itself because it was the first hyperscale, widely adopted public cloud object store. Q: Is Google Drive object storage?A: To end users, Google Drive behaves as file storage — presenting a folder-based interface for storing and sharing documents. Behind the scenes, Google relies on object storage technology to deliver scale and resilience, but that layer is not visible to users. Q: Is object storage more expensive than file or block storage?A: For transactional workloads (e.g. databases, email servers, ERP systems, and online transaction processing), block or file storage may be more cost-efficient. But for massive, long-term datasets, object storage reduces TCO by scaling linearly and avoiding costly upgrades. Cloud egress fees and retrieval costs should be considered, especially for frequent-access workloads. Scale out seamlessly with modern object storage That’s object storage in a nutshell — a scalable architecture built for the age of unstructured data and AI. Whether you’re modernizing backups, enabling analytics, or training models at petabyte scale and beyond, object storage gives IT teams the resilience and flexibility they need for today’s challenges and tomorrow’s opportunities. Related articles to consider How does object storage help overcome the growing security risks posed by unstructured data? Read more on that here. If object storage sounds like it could be a good fit for your needs, you can learn about Scality RING and ARTESCA here.
