Scality entrusts the contextual memory storage of AI agents, without performance guarantees. Avery Calloway - 10/09/2026 - 6 minutes of reading Scality Unveils AI Inference Factory at Scality Day in Paris Scality has announced its latest innovation, the AI Inference Factory, during Scality Day in Paris - a software stack designed for on-premises inference that offloads the contextual memory of AI models to storage. This move aims to address the limitations of GPU memory for AI agents as the complexity of context expands during operations. Although Scality has launched this product with a fixed budget model in mind, it has not yet guaranteed performance outcomes. AI models and reasoning agents require context retention throughout a session, which can grow increasingly cumbersome. Scality's announcement, made on October 8, aims to tackle these demands, promoting the use of AI Inference Factory on enterprise infrastructure instead of relying on cloud providers. This software stack integrates validated open-weight models, an inference service, a control layer, and Scality's own Scality AI Data Infrastructure (ADI). The target market includes businesses, government entities, and new cloud service providers focused on AI. Scality offers this stack through both licensed and fully managed service options, delivering a complete solution that it validates, deploys, and maintains. Shared KV cache for agents' context. To produce each token, the AI model utilizes a Key-Value (KV) cache, storing previous computations related to the context. This technical memory mechanism enables AI agents and large language models to respond swiftly and cost-effectively during complex tasks or conversations. Instead of revisiting and recalculating entire conversation histories for every word generated, the system retains prior attention states directly within the graphics card memory (VRAM). According to Scality, reasoning models and agent loads, particularly during long sessions, are the most impacted. The company has opted to move the KV cache from GPU servers to ADI, marketed as a shared cache with multi-petabyte capacity. Furthermore, the architecture separates the two phases of inference: a group of GPUs first reads the request and updates the cache (prefill), while a second group then accesses this cache to generate tokens (decode). "With ADI as a shared cache, any decode GPU can retrieve any context without size limitations," explains Giorgio Regni, Scality's Chief Technology Officer. It's important to note that this KV cache does not overlap with an agent's memory from one session to another. Instead, it maintains the context of the ongoing session, including instructions, read documents, tool results, and reasoning already produced. Retention of historical data, preferences, and organizational knowledge requires different mechanisms, such as retrieval-augmented generation or long-term memory, which are not part of this announcement. This distinction is crucial for agent functionality, allowing an agent that waits for tool feedback or human validation to resume tasks without the GPU recalculating all accumulated context. Hence, this development addresses the costs associated with extended and interrupted sessions, not the learning capabilities of agents. ADI operates on a scale of multiple petabytes, using NVMe or HDD disks connected to GPUs via RDMA. Data is distributed across flash memory, disks, and optionally tape. Laboratory performance insights from Scality. The performance numbers shared stem from Scality's internal tests. The Gemma-3 27B model loads in 1.9 seconds - nearly ten times faster than loading from a local NVMe drive. Restoring a context of 14,000 tokens from ADI takes 166 milliseconds before producing the first token, which is only 83 milliseconds longer than loading from HBM. Accessing cached data is 14 times faster than recalculating it for the same context, going up to 72 times faster for 439,000 tokens. The cache exceeds GPU memory capabilities by over 80 times and can restore 1,000 sessions without recalculation. According to the company, latency between generating tokens remains steady, with context restored before the first token is emitted. However, these tests are based on a modest setup: a server with eight GPUs, five storage servers, and four RDMA links operating at 100 Gbit/s. Scality has not released any service-level commitments. Its control layer effectively distributes requests according to service-level agreement (SLA) targets, yet these are ultimately determined by the client. The company cites two external studies. DistServe, presented at OSDI 2024, reported a 7.4 times increase in requests served at similar latencies thanks to the separation of phases, while Moonshot AI noted a 75% increase in requests processed with its Mooncake shared cache. Fixed budget per token amidst unpublished pricing. Scality's second key argument revolves around costs. In cloud environments, providers charge per token generated, with bills escalating alongside usage. Additionally, a provider may alter a model's version, risking disruption to dependent applications. By deploying on-premise, enterprises can maintain control over model versions and pay for infrastructure rather than consumption. The stack supports various models, including Mistral, Gemma, gpt-oss, Qwen, Kimi, GLM, and DeepSeek, compatible with Dell, HPE, Lenovo, and Supermicro servers. This variety addresses common needs within the market. Yet, Scality has not provided pricing details or client references. On-premise deployment incurs costs for GPU purchase, energy, and operational maintenance. In the cloud, token prices are competitive. For example, as of October 8, Anthropic charges $0.10 per million tokens for its Claude Haiku 5.5, and Google Cloud has set spending caps for computational loads. On-premise solutions excel in scenarios of massive, consistent usage, while cloud services are advantageous when usage is low or variable. Thus, cost savings will likely vary by case until Scality reveals its pricing. IDC Vice President Nataliya Yezhkova points out the market dynamics, stating that on-site inference is now a viable option "as long as the infrastructure can efficiently preserve and serve model states at scale." Scality isn't pioneering this field alone; Red Hat has also entered the arena with its AI Inference Server since May 2025, supporting a distribution of the vLLM engine, alongside the distributed inference project llm-d. A response to architectural challenges yet to be proven. The distinction between prefill and decode, as measured by DistServe, and the reuse of the KV cache are established practices that Scality hasn't invented. Fundamentally, the approach makes sense. The differentiating factor lies in how it handles inference state storage, as opposed to the engine work emphasized by Red Hat. In this paradigm shift, agent context transitions from a computational burden to a stored data format, allowing GPU memory constraints to transform into storage capacity inquiries that IT departments are capable of managing. Still, Scality's evaluations are based on a single test bed using just two models, Gemma-3 27B and GLM-5.2. It remains to be seen whether this framework can hold up under production loads. An IT department considering this stack will require testing with its own agents, specific session volumes, and documented performance guarantees. Ultimately, the fulfillment of the promise of a fixed budget will depend on how well these requirements are met.

Scality launches AI Inference Factory to bring enterprise AI on-premises. Scality's new open-code software stack enables enterprises to run validated open-weight AI models on their own infrastructure, with predictable costs and control over models and data sovereignty. SAN FRANCISCO, October 8, 2026 - Scality, a global leader in data infrastructure software for AI storage at scale, today announced the availability of Scality AI Inference Factory, an open-code software stack for deploying and operating AI inference on enterprise-owned infrastructure. The solution gives enterprises, government agencies and neo-cloud providers a supported alternative to cloud-based AI services, without having to assemble, integrate and maintain the entire software stack themselves. As AI moves from experimentation into production, organizations are looking for more predictable costs, greater control over model lifecycles and increased data sovereignty. For most organizations, the answer will be hybrid, with the most critical AI processes running on-premises. Running inference locally can address these requirements while keeping sensitive data within infrastructure that organizations own and operate. Scality AI Inference Factory brings together validated open-weight models, a disaggregated inference-serving layer, a control plane for authentication, metering, routing and scheduling, and Scality AI Data Infrastructure (ADI), which uses policy-governed autonomous operations to manage massive datasets across the AI lifecycle. Scality validates, ships and maintains the integrated stack. "With AI moving into mission-critical production environments, organizations need greater control over where inference runs, how their models are managed and what happens to their data," said Jérôme Lecat, CEO at Scality. "For 15 years, Scality has built data infrastructure that thousands of customers around the globe rely on to operate 24/7. AI Inference Factory brings that experience to on-premises AI, giving organizations the reliability and sovereignty they need to run critical AI workloads on their own terms." Cloud AI challenges drive interest in on-premises inference Enterprises scaling AI in production are confronting challenges with the cloud-based inference model. With per-token pricing, the more useful an AI workflow becomes, the more it costs, producing bills that are difficult to forecast and impossible to cap. Model versions and quantization can change at the provider's discretion, affecting the workflows built on them. Organizations with sensitive or regulated data face sovereignty concerns over where their prompts, documents and proprietary code are processed, under which jurisdiction, and who can restrict access to them. Scality AI Inference Factory is designed for organizations that want to run critical AI workloads on infrastructure they control while retaining flexibility across models, hardware and software. The stack supports specialized chatbots, AI-assisted software development, agentic applications and other inference-intensive workloads. Scality AI Inference Factory combines: * Validated open-weight models maintained as part of the supported stack; * Disaggregated inference serving that separates prefill from decode so each can scale independently; * A control plane that authenticates and meters requests, routes them to the appropriate GPUs holding the relevant context and schedules workloads against SLA targets; and * Scality ADI, which provides high-performance object storage for models, enterprise data and inference state. Scality validates and maintains the complete stack as a supported solution, helping organizations keep pace with rapidly changing models, serving technologies and security requirements without having to integrate and maintain the individual components themselves. "As AI adoption expands and AI agents become more prevalent, organizations are evaluating which IT environments are best suited to run inference. On-premises deployments are coming into focus for a variety of reasons, including cost optimization, data security and privacy, and regulatory requirements. IDC research shows that running inference on-premises is a credible option for a growing set of enterprise and public sector workloads, provided the infrastructure can hold and serve model state efficiently at scale. Scality is among the vendors addressing that requirement across the inference and storage layers." - Nataliya Yezhkova, Vice President, Storage and Data Management, Enterprise Infrastructure, IDC Scality ADI extends AI inference beyond GPU memory constraints Scality ADI serves as the shared storage layer for AI Inference Factory, extending key-value (KV) cache beyond limited GPU high-bandwidth memory (HBM). This allows inference infrastructure to preserve and retrieve model context instead of requiring GPUs to recompute it, improving GPU utilization and reducing inference costs. This is particularly important for reasoning models and agentic workloads, where KV cache can quickly exceed available GPU memory. Scality ADI provides a shared, multi-petabyte cache that GPUs read at latency the same order of magnitude as GPU memory, allowing them to retrieve existing context and remain focused on inference. Scality testing demonstrated that ADI can reduce GPU consumption while maintaining the same level of performance: * 1.9-second load time for Gemma-3 27B, almost 10x faster than local NVMe, with the model streamed in parallel across the cluster over RDMA; * 166 ms warm time-to-first-token on a 14K-token context restore from ADI, only 83 ms behind HBM; * 14x faster KV cache retrieval than recomputation on a 14K-token context and up to 72x faster on a 439K-token context; * A KV cache more than 80x larger than a single GPU's memory, keeping up to 1,000 concurrent sessions resumable without recomputation; * No measurable impact on token generation, as context is restored before the first token; and * 97% of network line rate for data transfer between GPUs and storage. The architecture separates prefill from decode, with ADI acting as the shared cache between GPU pools. This allows available decode GPUs to access existing context without tying it to a specific GPU server. Separating prefill from decode has documented gains: DistServe (OSDI 2024) measured up to 7.4 times more requests served within the same latency targets. Adding a shared KV cache pool on top, Moonshot AI's Mooncake reported 75 percent more requests on Kimi's production traffic. As Giorgio Regni, Scality CTO, puts it: "The KV cache on Scality ADI is fast enough to sit in the serving path. Restoring a context from ADI is the same order of magnitude as GPU memory, and 14 times faster than recomputing it, with the GPUs staying busy the whole time. Storage is no longer the reason to keep the KV cache inside the GPU server." Regni continues: "That opens the door to disaggregated serving. One pool of GPUs handles prefill, processing the prompt and writing the KV cache to ADI. A second pool handles decode, reading the cache back and generating tokens. With Scality ADI as the shared cache, any decode GPU can pick up any context, and there is essentially no size limit. Prefill no longer interrupts decode, and both pools run at full load." For a technical deep dive into the AI Inference Factory architecture and test results, visit Scality's companion blog. Open architecture provides flexibility across models, frameworks and hardware Scality AI Inference Factory is designed to preserve customer choice across the AI infrastructure stack. It is compatible with open-source harnesses and agentic frameworks such as OpenCode, Hermes, Goose, LangGraph and Pydantic AI, and runs validated open-weight models such as Mistral, Gemma, gpt-oss, Qwen, Kimi, GLM, and DeepSeek. The software deploys on standard servers from Dell, HPE, Lenovo and Supermicro. Every software component Scality ships is delivered as open code, enabling customers to inspect how inference state is stored and moved, and to submit contributions for Scality review. For organizations running sensitive, regulated or sovereign AI workloads, this provides transparency into the software handling their models and data rather than requiring them to rely on a closed external service. Scality ADI integrates with standard AI stacks and deploys at multi-petabyte scale on NVMe, with RDMA access to HDD for virtually unlimited KV cache capacity. A single namespace automatically tiers data across TLC, HDD and, optionally, tape, allowing organizations to align storage performance and economics with different stages of the AI data lifecycle. Availability Scality AI Inference Factory is available now as a software license or as a fully managed service. Scality is demonstrating the solution and its reference architecture at Scality Day on October 8 in Paris. To learn more, visit the Scality AI Inference Factory web page and detailed companion blog.

CTERA Networks bridging the gap between cloud storage and local storage. Liran Eshel, CEO, Co-Founder The explosive growth in unstructured data, the increased need to access files anywhere and from any device, and the desire to collaborate frequently across the distributed enterprise are all driving companies to use scalable, costefficient cloud storage technology to address these needs. Palo Alto based CTERA Networks caters to this need of both enterprises and service providers and in the process has created a buzz amongst the tech circles. The company enables its customers to use the cloud storage infrastructure of their choice whether private, public or hybrid, while ensuring end-to-end security and overcoming performance and scalability challenges. 'Single Pane Of Glass' Management Founded in 2008 by Liran Eshel, CEO, and Zohar Kaufman, VP R&D, the company provides a cloud storage platform that enables enterprises and service providers to create, manage and deliver cloud storage services, whether internal or external. The company's unique platform includes built-in functionality for file sync and share, hybrid on-premises/cloud backup,managed branch storage and mobile collaboration which is unified under 'single pane of glass' management. "Our platform extends storage infrastructure to make it "cloud enabled", and integrates with the majority of object-based and other scalable storage systems, as well as cloud service providers. CTERA is a technology vendor - we do not provide cloud services, but rather empower service providers and enterprises to do so," adds Eshel. It is the only company to provide a platform that addresses multiple cloud storage "killer apps" and supports both hardware and software-based deployment scenarios to optimize performance across remote office and branches,as well as mobile workers. Providing Game Changing Services CTERA's core development team originated from Check Point Software, so security is very much in the company's DNA and is embedded into all aspects of the company's solutions, which feature built-in source-based encryption and other security measures. The company's cloud storage platform is comprised of cloudside management software, cloud storage gateway appliances and ndpoint software agents for PCs and mobile. Customers can choose any mix of software/hardware to serve their storage, backup and file sharing needs in an optimal way. Active Directory integration, including support for the most complex AD forests, makes it easy for enterprises to deploy CTERA's solution, while global source-based de-duplication ensures optimal erformance over the WAN. CTERA integrates with storage infrastructure from EMC, IBM, HDS, OpenStack, Caringo, DDN, Scality and leversafe, as well as with public cloud providers such as AWS and Rackspace. Winning the Trust of Clients Due to its game changing technology, in a very short period of time, the company has won the hearts of many firms which include some of the Fortune 100 enterprises across multiple industry sectors including financial services,life sciences and energy. Major service providers also use CTERA's platform to drive their businessgrade cloud storage services.They include, among others, Telefonica, CenturyLink, Orange (France Telecom), Telecom Italia and Swisscom. With customers in production with 10,000s of appliances deployed and 100,000s of active users, CTERA's technology is proven to scale to very large deployments. CTERA plans to continue its good run and expand its platform both to support additional usecases as well as to extend further the value of cloud storage for security, compliance and data mining purposes in the future. Management Liran Eshel, CEO, Co-Founder and Zohar Kaufman, VP R&D, Co-Founder Description A provider of a cloud storage platform for enterprises and service providers, enabling the delivery of a suite of storage applications over the infrastructure of their choice