The Challenge: Trapped Between Cloud Power and Cloud Lock-In

Organizations face an impossible choice. Hyperscaler clouds offer powerful data services, advanced catalogs, vector search, and high-performance analytics, but only within their walled gardens. The result? Data silos, vendor lock-in, and costs that spiral as data grows.

The numbers tell the story: Industry analysts project that the vast majority of future data growth will be unstructured data for AI and analytics: documents, images, videos, and sensor streams. This isn't just a storage problem. It's an intelligence problem. Basic object storage can scale to exabytes, but it can't answer questions, find patterns, or power AI models. Organizations need storage that can both hold massive unstructured datasets AND make them intelligently searchable and analytically ready.

Yet most "modern data platforms" force users into a trap: Lakehouse fragmentation. Organizations run separate stacks: cheap data lakes for storage, expensive proprietary warehouses for analytics, standalone vector databases for AI, disconnected catalogs for governance. Each system has its own security model, APIs, and operational overhead. Data is copied, synchronized, and transformed across systems, creating sprawl, latency, and governance headaches.

Gartner calls this "technology integration debt." In fact, recent research shows that organizations have, on average, deployed more than a dozen data management solutions with overlapping functionality, creating complexity that distracts teams from their primary goal: delivering AI-ready data quickly and reliably.

This architectural complexity, combined with hyperscaler lock-in, has triggered a massive wave of data repatriation; organizations bringing workloads back on-premises to regain control and reduce costs.

But there's a problem: they fear losing the very "cloud-native" data services they've come to depend on.

What they need is clear:

• Cloud-native power, without cloud lock-in. Hyperscaler-grade data services on their own infrastructure

• A unified, converged data management platform consistently deployed from on-prem data centers to the edge

• Built on open standards, the antidote to vendor lock-in

• Intelligent data services, not just storage. Unstructured data that's intelligently indexed, secured, and ready for analytics and AI

• No architectural fragmentation or data movement tax. One platform, no copying data around between systems

As Gartner notes: "The promise of a comprehensive data management architecture has pushed D&A leaders into evaluating end-to-end converged data management platforms." But the key is ensuring these platforms don't simply replace one lock-in with another.

The Solution: IBM Storage Ceph as Your Lakehouse Data Engine

IBM Storage Ceph is evolving from a passive storage repository into an active, intelligent data service platform.

What does "data services" mean? Instead of just storing files, IBM Storage Ceph now embeds security, intelligence, and high-performance compute directly into the data fabric itself, providing native catalogs, caching, vector search, and query acceleration that bring processing to your data, not the other way around. This multimodal approach handles structured analytics, unstructured AI data, and embedded vectors within a single, unified platform.

Built on a battle-tested foundation of exabyte-scale storage, self-healing resilience, and enterprise-grade security, IBM Storage Ceph transforms into your Lakehouse Co-Pilot: an intelligent partner that securely governs your data, dramatically accelerates your analytics, and unlocks breakthrough performance for AI workloads; all while keeping you in control.

The result: All the power of hyperscaler data services. None of the lock-in. On your infrastructure. On your terms.

Three Pillars: How IBM Storage Fusion Powered By Ceph Delivers Cloud-Native Data Services

IBM Storage Ceph's strategy centers on three Data Service pillars, all delivered through the unified "single pane of glass" Fusion control plane to redefine object storage as a high-performance platform for the Lakehouse and AI era:

1. Unified Governance (Open Catalogs / Open Formats)

Addressing security fragmentation and data lock-in for the hybrid lakehouse

2. High-Efficiency Analytics (Caching & Query-Native Storage)

Removing the data movement bottleneck for large-scale analytics

3. High-Velocity AI Acceleration (Vectors & KV Cache)

Solving the most expensive bottlenecks in RAG and LLM inference

Let's explore each pillar.

Pillar 1: Unified Governance with Iceberg REST Catalog

The Lakehouse Foundation

The modern Data Lakehouse relies on open-source table formats such as Apache Iceberg to provide ACID (Atomicity, Consistency, Isolation, Durability) transactions, schema evolution, and time travel on S3 data files. The integrity of these tables depends on a Catalog, the single source of truth for metadata.

IBM Storage Ceph's Approach

Delivered via IBM Storage Fusion, IBM Storage Ceph now provides a native, multimodal metadata catalog that operates in lockstep with the S3 Object Storage platform (RGW). Fusion acts as the unified control plane, providing a "single pane of glass" that abstracts complexity and delivers a seamless "it just works" experience.

This governance layer builds on IBM Storage Ceph Object's proven, production-grade security foundation, which includes self-service IAM, STS, MFA delete, Object Lock, per-bucket auditing, and resilient multisite replication, all of which are available today.

Why This Matters: Unified Security & Open Ecosystems

• Unified Security: A single IAM system at the storage level consistently controls access and governance for both metadata operations AND data file access; down to per-table Role-Based Access Control (RBAC). No separate security stacks. No gaps.

• Open Ecosystems, No Lock-In: Built on the Iceberg REST Catalog Specification, this architecture enables true freedom. Engines such as Snowflake, Dremio, and Spark can all connect to Iceberg tables in Ceph S3 as full participants in the open data ecosystem. Snowflake can consistently and securely read data ingested by Spark, without requiring secondary copies or changing file formats.

• Business Impact: Unified governance dramatically reduces security complexity, eliminates catalog fragmentation, and enables hybrid cloud flexibility without vendor lock-in. Multisite replication embedded in Ceph ensures business continuity and disaster recovery with RPO measured in seconds, not hours.

Pillar 2: High-Efficiency Analytics – Moving Compute to the Data

This pillar is focused on dramatically accelerating analytics by building on a foundation of raw, all-flash performance. IBM Storage Ceph delivers this high-throughput, low-latency workload capability today.

The roadmap includes cutting-edge features that bring processing power directly to your data.

A. All-Flash Object Storage Performance Foundation

Before exploring the future, it's critical to establish a baseline performance for IBM Storage Ceph Object on modern hardware. Ceph is not just for capacity; it is a proper high-performance solution. In recent tests on an all-flash 4-node cluster (Supermicro X14 GrandTwin with Gen4 NVMe), Ceph delivered nearly 60 GB/s of throughput to a single AI client (like the Intel Gaudi3 platform). This 15 GB/s per-Node throughput demonstrates that for workloads requiring "lightning fast" raw speed, Ceph's all-flash foundation delivers.

B. Distributed Caching (D4N): Attacking the Recursive Query Bottleneck

To eliminate network congestion and accelerate repeated queries, IBM Storage Ceph is developing D4N (Directory-based Datacenter-Delivery Network). D4N evolves beyond traditional caching by adding:

• Write-back caching for accelerated data transformation and staging operations

• Directory-based cache coordination across compute nodes to eliminate redundant fetches from storage

• Improved Iceberg Table performance through Metadata Caching

Business Impact: Dramatically accelerates repeated queries across all modern lakehouse engines, from IBM's WatsonX Data to open-source Trino, Presto, and Spark, by caching hot data paths close to compute. Reduces network congestion and bandwidth costs for recursive query patterns.

C. High-Speed AI/ML Data Paths (RDMA)

For AI/ML workloads, the future roadmap for IBM Storage Ceph Object includes support for NFS over RDMA for the Object Gateway (RGW). This new data path will deliver the following benefits:

• Reduced network latency for client-to-storage access on high-speed fabrics

• Higher throughput on high-speed networks (100/200 Gb+)

• Direct GPU integration via technologies like GPUDirect Storage (GDS) for model staging and inference

• Reduced CPU overhead by offloading data transfers directly to RDMA-capable NICs, maximizing compute available for AI workloads

Business Impact: Accelerates model loading and data staging for AI/ML workflows, enabling faster training iterations and improved GPU utilization on high-performance network infrastructures.

D. Query-Native Analytics (Arrow Flight & Query Pushdown)

This represents the ultimate evolution of the analytics pillar: embedding Apache Arrow Flight directly into RGW to transform Ceph into a query-native platform.

Instead of analytics engines pulling data through the S3 API, Arrow Flight will enable:

• High-speed, parallel, columnar data access

• Query Pushdown by integrating compute kernels (like Velox) and standards (like Substrait)

• Storage-layer execution of filters, projections, and aggregates

What this means: The storage itself will execute parts of your query, reducing data movement, slashing query times, and freeing up compute resources.

Business Impact: Query pushdown can dramatically accelerate analytical workloads by executing filters and aggregations at the storage layer, reducing network data movement and freeing compute resources for higher-level processing.

Pillar 3: High-Velocity AI Acceleration – Vectors and KV Cache

IBM Storage Fusion Powered by Ceph is positioning object storage at the heart of the AI revolution, with native services for multimodal AI workloads, including vector search APIs for embeddings, document storage for RAG, and KV cache offloading for LLM inference, all on a single unified platform.

The sections below explain these AI-specific capabilities and how Ceph enables them.

A. Vector Search and Event-Driven RAG

Modern AI applications convert data into vectors, numerical representations that capture semantic meaning. This enables AI to find similar content based on meaning, not just keywords, and powers RAG (Retrieval-Augmented Generation), a technique in which AI models retrieve relevant information from your data before generating responses, ensuring answers are enhanced with your own triaged and up-to-date content.

IBM Storage Ceph is implementing an S3 Vector REST API via the Object Gateway to support vector buckets and indexing for AI inferencing.

The Architecture:

• Event-Driven Processing: When raw data lands in S3, bucket notifications trigger serverless pipelines (like Knative in K8s)

• Automated Embedding: These pipelines chunk, embed, and index the data

• Stored in RGW: Vectors are stored natively in RGW using the S3 Vectors API

• RAG Query Path: Applications query for relevant content, and RGW returns pointers to the matching data chunks stored in an S3 bucket

Business Impact: Native vector search eliminates the need for separate vector databases. This dramatically simplifies the infrastructure for AI platforms like IBM's watsonx.ai, reducing complexity and enabling RAG applications to scale seamlessly on existing storage.

B. KV Cache Offload for LLM Inference

When Large Language Models process text, they generate intermediate data called the KV (Key-Value) cache, which must be kept in fast GPU memory. This cache can grow to 3× the model size, quickly consuming all available GPU memory and limiting the number of requests you can process simultaneously. The larger the context (longer conversations, larger documents), the more severe this bottleneck becomes.

Running Large Language Models (LLMs) at scale hits a critical bottleneck: the KV (Key-Value) Cache.

The Problem:

• The KV cache stores vectors generated during the Prefill phase of inference.

• It consumes massive GPU memory (VRAM); often 3x the model size.

• As context length grows, cache size explodes linearly.

• Result: Capped concurrency, GPU inefficiency, and expensive cores sitting idle.

Ceph's Solution: Proven Collaboration with Intel on vLLM/LMCache enhancements

IBM Storage Ceph, in partnership with Intel, has demonstrated highly efficient KV Cache Offload using shared object storage. This capability is production-ready today.

How it works:

• Turn static GPU memory pressure into a streaming problem.

• Move cache blocks from local VRAM to a tiered cache structure backed by Ceph RGW.

• Use "Space for Time": If cache blocks load from Ceph faster than they can be recomputed, Time-to-First-Token (TTFT) drops dramatically.

• Cache blocks stored as standard S3 objects inherit Ceph's enterprise security; encryption at rest and in transit, access control policies, and audit logging, ensuring AI workloads meet compliance requirements.

Performance Results:

• Over 10x reduction in TTFT at 131k prompt length compared to computed prefill.

• Unlocks unused GPU cycles for higher concurrency and monetization.

• Turns static GPU memory pressure into a streaming problem, preventing the "KV cache wall" that caps concurrency.

• Enables long-context LLM applications (100K+ tokens) that can analyze entire documents, maintain extended conversations, and solve complex multi-step problems previously impossible due to memory constraints

Business Impact: By breaking through the memory bottleneck that caps GPU concurrency, organizations can serve dramatically more requests on existing hardware. The 10x TTFT improvement for long-context inference enables new use cases (document analysis, extended conversations) without proportional GPU scaling, unlocking revenue from capabilities that were previously computationally prohibitive.

Conclusion: The Power of Cloud-Native. The Freedom of Open.

IBM Storage Fusion powered by Ceph is fundamentally redefining its role as the strategic hybrid-cloud data platform for open data, analytics, and enterprise AI. It provides the high-performance foundation for IBM's WatsonX platforms and, through its open design, for the entire open-source ecosystem.

By investing in open standards (Iceberg, Arrow) as the foundation and implementing cutting-edge performance features (D4N, Arrow Flight, S3 Vectors, KV Cache Offload), all while maintaining the enterprise-grade reliability, security, and operational maturity that organizations demand, IBM Storage Fusion with IBM Storage Ceph provides a secure, resilient, high-performance data service platform.

What this means for you:

• Build a governed, efficient Lakehouse where your data lives; data center or edge

• Repatriate workloads from hyperscalers and cut costs by 40-60% without losing advanced capabilities

• Unlock breakthrough AI performance with native vector search and GPU-efficient LLM inference

• Avoid vendor lock-in with open standards that ensure interoperability and freedom

IBM Storage Ceph isn't just storage. It's your Lakehouse Data Engine for the AI Era; an intelligent partner that governs your data, accelerates your analytics, and powers your AI future.

All the capabilities of a cloud-native platform. All the control of on-premises infrastructure. Built on the open standards that keep you free.

Welcome to the future of data services. On your own terms.