Private on-premises AI offers untold benefits for enterprises, but there are some challenges to overcome first.
We think of enterprise AI as transformative, yet adoption remains limited beyond the largest corporations. Despite significant media coverage and clear potential, many mid-sized enterprises and smaller organizations hesitate to deploy AI on-premises.
The hesitation stems from traditional private AI’s reputation as complex, resource-intensive, and financially unpredictable. To go mainstream, it must address these challenges by simplifying infrastructure, eliminating unnecessary hardware requirements, and providing immediate, tangible business value.
Understanding the true advantages of private, on-premises AI
Organizations comparing AI deployment strategies weigh public cloud-based AI services against private, on-premises alternatives. While public cloud providers promise quick starts and convenience, they fail to deliver the critical data privacy, security, and cost predictability that enterprises need.
In contrast, a private, on-premises AI environment ensures complete control over sensitive data, processes, and intellectual property. Organizations can secure proprietary datasets within their infrastructure, which may be a regulatory or compliance necessity.
Another significant advantage of private AI is cost predictability. Public cloud AI platforms use a token- or query-based pricing structure, which may be initially manageable but becomes increasingly unpredictable and expensive as usage increases. Private AI avoids this scenario with a fixed-cost structure that doesn’t penalize organizations for broader adoption or heavier usage.
Finally, private on-premises AI can be more hardware-efficient. Enterprise AI use cases do not require extensive deployments of high-end GPUs or specialized accelerators. Many organizations can achieve their AI goals with a smaller number of mid-range GPUs or even CPU-only configurations, reducing infrastructure expenses without sacrificing performance.
Our webinar Enterprise AI Infrastructure will discuss the requirements for an on-premises AI that can add value to the organization, not just IT, on day one.
Roadblocks preventing broader enterprise AI adoption
Despite its compelling advantages, enterprises encounter significant roadblocks when attempting to deploy private AI. Understanding these roadblocks in detail helps identify effective ways to overcome them.
1. AI pipeline complexity
The traditional AI software stack often uses open-source tools, containers, Kubernetes, and other layered technologies. Each layer adds complexity, requiring specialized skills and significant internal expertise to manage well. Mid-sized enterprises lack the dedicated AI resources to handle these complex stacks, creating hesitation and preventing adoption.
2. Over-promising with AI bundled solutions
In response to concerns about complexity, many vendors offer bundled or “AI-in-a-box” solutions, promising simplified, turnkey experiences. These solutions trade one type of complexity for another,. They introduce expensive hardware requirements and proprietary configurations that lock organizations into vendor-specific ecosystems, increasing total ownership costs without genuinely simplifying deployment or management.
3. Security and compliance concerns
Enterprises remain cautious about the security of AI platforms, especially when sensitive data is involved. Internal AI deployments handle critical business information, making security a paramount concern. Yet many AI platforms don’t provide robust, integrated security measures, leaving organizations uncertain and unwilling to risk potential breaches or compliance violations.
4. Hardware lock-in and lack of flexibility
AI solutions require specific, high-end GPU hardware or proprietary infrastructure. This hardware lock-in presents significant long-term risks, as enterprises may find themselves bound to a single hardware vendor, with uncertain long-term support and compatibility. Additionally, as new, innovative GPU vendors emerge, organizations may struggle to adopt their technologies due to the rigidity of their chosen AI infrastructure.
5. Storage complexity and duplication
One often-overlooked barrier is the additional storage infrastructure AI workloads traditionally require. AI deployments need specialized storage infrastructure separate from and incompatible with existing storage systems.
Organizations may find themselves investing not only in one additional storage system, but in multiple tiers of storage. They’ll buy high-performance systems for training, medium-performance solutions for inference, and separate long-term archival storage solutions. This duplication increases operational complexity and cost.
6. Difficulty in building and curating AI models
Model training, fine-tuning, and ongoing curation require specialized skills. Without straightforward tools and automated processes, enterprises struggle to use AI, creating barriers to practical implementation.
7. Unclear immediate business value
Many organizations hesitate to deploy enterprise AI because the immediate, tangible value of initial AI investments can be unclear. Without quick and recognizable returns, businesses will delay adoption, fearing lengthy implementation periods with uncertain outcomes.
Strategies to overcome enterprise AI roadblocks
To overcome these challenges, organizations need enterprise AI solutions with several features:
1. Integrated, streamlined AI infrastructure
Enterprises should prioritize integrated infrastructure solutions over complex, multi-layered AI stacks. Directly integrating AI into core infrastructure software reduces complexity. This makes deployment and ongoing management far more manageable, even without dedicated AI experts.
2. Built-in generative AI capabilities
Infrastructure software should come with integrated generative AI capabilities, eliminating the need for separate, complicated installations. Organizations should be able to immediately deploy and use popular large language models (LLMs) like LLaMa or Mistral integrated into their core infrastructure. This change simplifies the rollout of AI, quickly demonstrating business value, while also enabling IT administrators to leverage the integrated AI for monitoring and managing infrastructure.
3. Simplified storage infrastructure
Organizations must select AI solutions that eliminate the need for separate AI-specific storage hardware. A unified storage system should handle high-performance, mid-tier, and archival storage to reduce operational complexity and cost. This single-storage-instance model eliminates redundancy, enabling organizations to utilize existing hardware and accommodate new data requirements.
4. Vendor-neutral hardware approach
Private AI solutions should provide complete vendor neutrality, ensuring compatibility with GPUs from various vendors or even functioning without GPUs altogether. This GPU-neutral approach safeguards organizations against hardware lock-in, allowing them to embrace new GPU innovations as they emerge. While NVIDIA, the current GPU leader, may look untouchable, the same was once said about Intel 25 years ago.
5. CPU-based AI capabilities for flexibility
Enterprise AI solutions must offer robust CPU-based processing capabilities. CPU-based AI capabilities provide operational flexibility, ensuring sustained performance and operational consistency, even if GPUs become unavailable or temporarily inaccessible.
6. Security integrated by design
Security must be an integral part of AI solutions from the outset. Infrastructure software should include built-in security features, such as a firmware-style operating environment, network segmentation, data encryption, secure authentication, comprehensive audit logging, and role-based access controls, ensuring the protection of sensitive enterprise data without adding complexity.
7. Immediate and practical value
Enterprise AI solutions must demonstrate immediate practical value. Solutions should empower users to quickly perform essential business tasks, such as securely analyzing internal documents, auditing proprietary source code, automating infrastructure management scripts, and generating tailored enterprise content, delivering a rapid return on investment.
The ideal enterprise AI solution should promote experimentation without token-based charges that penalize users. It should enable the quick setup of secure, isolated virtual labs that allow teams to test, refine, and validate new AI workloads without requiring GPU resources, thereby accelerating innovation and reducing operational risk.
8. Broader IT problem-solving
AI deployments should also tackle broader IT infrastructure challenges. Instead of being standalone solutions, AI implementations must be integrated into a wider infrastructure strategy, addressing IT issues such as VMware exit strategies, infrastructure modernization, and SAN refreshes simultaneously. Using AI infrastructure software that can resolve multiple critical problems ensures maximum organizational value.
VergeOS and VergeIQ as examples
Platforms like VergeOS, which integrates VergeIQ, exemplify these principles. By embedding generative AI capabilities as a core infrastructure resource, VergeOS offers a streamlined, vendor-neutral AI deployment model. Organizations using VergeOS benefit from a unified storage architecture, flexibility in CPU and GPU options, comprehensive built-in security, and practical, immediate business value.
The path forward for enterprise AI adoption
For broader enterprise AI adoption to become a reality, organizations must re-evaluate traditional AI infrastructure approaches. Complex, multi-layered stacks, bundled solutions, and inflexible hardware dependencies must give way to streamlined, integrated, and vendor-neutral alternatives.
Organizations can choose AI infrastructure software that eliminates complexity, reduces unnecessary hardware, provides immediate practical value, and addresses multiple critical IT challenges simultaneously. Then they can finally realize the transformative potential of private, on-premises enterprise AI.
Contributed by VergeIO.
