Bookings in 2026 are already up nearly 14x over full-year 2025 as customers choose Hammerspace to turn existing infrastructure into an AI-ready advantage across data centers, cloud, and neocloud
The first phase of AI was about building models. The next phase is about putting AI into production — across enterprises, governments, sovereign AI initiatives, neoclouds, and global cloud deployments.
That shift changes everything.
In the model-building phase, organizations could tolerate curated datasets, isolated infrastructure, and specialized environments designed for experimentation. But production AI is different. Inference, agentic AI, and physical AI need access to real enterprise data, which is distributed, dynamic, governed, constantly changing, and often spread across data centers, clouds, edge environments, and specialized GPU infrastructure.
The winners in this next phase will not simply be the organizations with the biggest GPU clusters or the most ambitious AI roadmaps. They will be the organizations that can make their data AI-ready first.
That is why Hammerspace’s momentum is accelerating.
Hammerspace bookings in 2026 are already up nearly 14x over full-year 2025 as customers move to make existing infrastructure AI-ready now, rather than waiting for ideal future buildouts, new capacity, or another expensive AI silo.
Hammerspace was built for this moment.
It enables organizations to use the infrastructure they already own, access data where it already lives, and deliver that data to GPUs with the performance, governance, and flexibility required for operational AI.
The Market Has Shifted From Model Building to Operational AI
The AI market is entering a structural reset.
For the past several years, much of the industry focused on training foundation models and proving what generative AI could do. That work mattered. But it was only the beginning.
Now the pressure has shifted to execution.
Enterprises and governments need to operationalize AI against their own data. They need faster time to first token. They need lower cost per token. They need the ability to run inference across hybrid environments. They need to support agentic workflows that continuously interact with live data. And they need to prepare for physical AI, where machines, robotics, sensors, and real-world systems will depend on constant access to distributed data.
This cannot be solved by GPUs alone. And it cannot be solved by copying data into another isolated AI storage stack every time a new initiative begins.That model is too slow, too expensive, and too rigid for the speed of this market.
The new AI bottleneck is not just compute; it is data readiness.
- Can the right data be found?
- Can it be accessed where it lives?
- Can it be governed?
- Can it be delivered to GPUs fast enough?
- Can it be used without waiting days, weeks, or months for migration, duplication, staging, or infrastructure rebuilds?
For many organizations, the answer is still no. For Hammerspace customers, that answer changes.
The Real AI Advantage Is Time to First Token
Every AI infrastructure vendor claims they can feed GPUs faster. But feeding GPUs only matters after the data is actually ready.
If an organization has to copy, migrate, cleanse, stage, and rebuild data pipelines before inference can begin, the GPU may be fast, but the business is still waiting. That is why time to first token is becoming one of the most important measures of real AI readiness.
Hammerspace delivers a massive time-to-first-token advantage because its Data Platform makes distributed data usable in place on the infrastructure customers already own, where the data already lives.
That means organizations don’t need to wait for data to be copied into a new AI silo before they can begin. They do not need to rebuild their entire storage architecture before they can act. They do not need to choose between performance and access to enterprise data. They can move from data fragmentation to AI execution much faster – seconds to first token, not days, weeks, or months.
That is the difference between having AI infrastructure and being AI-ready.
Existing Infrastructure Has Become the New Strategic Advantage
AI demand is rising faster than infrastructure supply can keep up. Enterprises and governments are being forced to act amid tight SSD and memory supply, rising storage costs, power constraints, GPU availability challenges, and increasing pressure to operationalize inference now.
Waiting is not a strategy.
The organizations moving fastest aren’t waiting for perfect infrastructure to arrive. They are activating the infrastructure they already have across storage, GPU servers, data centers, cloud, and neoclouds.
Hammerspace turns existing infrastructure into a high-performance data platform for AI. It gives organizations a unified way to access, orchestrate, and deliver data across heterogeneous environments without disruptive migrations or copy-based architectures.
This is why the platform is gaining traction across global enterprises, hyperscalers, government environments, sovereign AI initiatives, high-frequency trading organizations, neocloud providers, and large-scale cloud deployments.
The pattern is clear: customers want to move now. Hammerspace gives them a way to do it.
Neocloud Adoption Is Accelerating
Neocloud providers are becoming a critical part of the AI infrastructure landscape, especially for GPU-intensive AI workloads. But compute capacity alone is not enough.
To win, neoclouds need to offer more than access to GPUs. They need to help customers connect those GPUs to the enterprise data that actually powers production AI. That is where Hammerspace is becoming a competitive divider.
Government and public neocloud providers are choosing Hammerspace because it gives them both the performance required to feed GPUs at scale, and the ability to connect GPU infrastructure directly to distributed enterprise data without forcing another copy, another migration, or another silo.
That matters because enterprise AI does not run on static, perfectly staged datasets. It runs on large, active, governed data estates that are constantly changing across hybrid environments. The neocloud providers that pull ahead will be the ones that help customers use that data directly, securely, and at speed.
Hammerspace makes that possible.
OCI Growth Shows the Need for a Global AI Data Layer
Hammerspace is also seeing growing demand on Oracle Cloud Infrastructure (OCI) to support large-scale enterprise AI and globally distributed workloads.
As OCI continues to expand its AI infrastructure footprint, including GPU-rich environments and sovereign cloud deployments, customers need more than raw compute. They need a data layer that can span hybrid and cloud environments, eliminate delays associated with copying and moving data, and deliver the performance required for operational AI. Hammerspace provides that layer.
It allows customers to use OCI as part of a broader AI infrastructure strategy without forcing data into a single location or creating another disconnected environment.
That flexibility is becoming essential as enterprises and governments deploy AI across multiple operating models: on-premises, cloud, sovereign cloud, neocloud, and hybrid combinations of all of the above.
The future of AI infrastructure is distributed. The data platform has to be distributed too.
Partnerships Are Expanding the Hammerspace Advantage
Hammerspace’s momentum is also being strengthened by its expanding ecosystem partnerships.
The Hitachi Vantara partnership is extending Hammerspace’s reach through integrated hybrid cloud and AI infrastructure solutions.
The Supermicro partnership is helping accelerate adoption through pre-qualified infrastructure designed to simplify deployment and shorten time-to-value.
These partnerships matter because the market is no longer asking whether AI infrastructure will scale. It is asking how quickly organizations can make it operational. Customers want validated, deployable, high-performance architectures that work with the infrastructure they already have and the cloud environments they prefer.
Hammerspace is increasingly becoming the data platform that makes those architectures work.
Customer Loyalty Confirms the Strategic Role of Hammerspace
Hammerspace’s growth is not just about new demand. It is also about customer confidence.
Gross retention is above 95%, and NPS has reached 71; these strong indicators that customers increasingly view Hammerspace as strategic infrastructure as they move from AI pilots to operational AI.
That is important because AI infrastructure decisions are no longer experimental. They are becoming foundational.
The data layer customers choose now will shape how fast they can deploy inference, how efficiently they can use GPUs, how well they can govern distributed data, and how prepared they will be for physical AI. Hammerspace is becoming a core part of that foundation.
AI Readiness Will Not Wait
The market is moving too fast for copy-based architectures, infrastructure silos, and build-first strategies that depend on ideal future conditions.
AI readiness won’t wait for shortages to ease. It won’t wait for new storage capacity to arrive. It won’t wait for perfect infrastructure plans to become reality.
The next generation of AI leaders will be the organizations that can activate what they already have — their storage, their data centers, their GPU servers, their cloud investments, their neocloud partnerships, and most importantly, their data.
That is the story behind Hammerspace’s momentum.
Hammerspace is not growing because the market needs another storage product. Hammerspace is growing because the market needs a new data platform for operational AI:
- One that makes existing infrastructure AI-ready
- One that makes distributed data usable in place
- One that reduces time to first token
- One that feeds GPUs at scale
- One that supports training, inference, agentic AI, sovereign AI, and physical AI across the environments customers already use
The first phase of AI was about building models. The next phase is about operationalizing AI.
Hammerspace is the platform built to move your AI initiatives forward, now.
Learn More
Press Release: Hammerspace Posts Nearly 14x YTD Bookings Over Full-Year 2025 as AI Shifts to Inference and Physical AI
Press Release: Hammerspace Launches AI Data Platform Based on NVIDIA Reference Design – Available Now
Solution Brief: Supercharge Your AI Data Workflows with Hitachi and Hammerspace
Press Release: Giving Customers More Choice and Flexibility to Deploy Hammerspace – Now with Supermicro as a Server Hardware Partner
Blog Post:Accelerating AI/ML Workloads on OCI: Integrating Hammerspace with the OCI HPC/GPU Stack
On-Demand Webinar: Looking Beyond the GPU: Solve Your AI Data Pipeline for Faster Outcomes w/AIDP
Omdia Research Brief: The 2026 SSD Crisis: Implications for Enterprise AI Infrastructure
