In today’s rapidly evolving technology landscape, adopting and rolling out AI to enhance your enterprise is critical to improving your organization’s productivity and ensuring that you are delivering a world-class product and service experience to your customers. AI is without question, the single most important technological revolution of our time—representing a new technology super-cycle that your enterprise cannot be left behind on.
Today, enterprises are trying to adopt AI at an unprecedented pace — in fact, the latest research offered by Bain & Company suggests:
87% of companies surveyed by Bain said that they were already developing, piloting, or have deployed generative AI in some capacity, with most of these early deployments in software code development, customer service, marketing and sales, and product differentiation.
While the benefits of AI are increasingly clear, a notable trend is also starting to emerge: enterprise teams are now assessing how to own and control their AI systems rather than relying on third-party providers like OpenAI.
As the race to deploy AI into enterprise exploded rapidly, many enterprises tried to adopt AI as fast as possible without considering the broader impacts to their organizations and the impact on their employees, products, and customers. Now, as enterprises see their AI efforts maturing, and with more proof of concepts (POCs) moving to production, they are increasingly asking what they need to do to scale AI inside their organizations.
We regularly talk to enterprises about their AI needs, and here is a list of what enterprise customers tell us is important to them:
Product and engineering teams need the ability to customize their AI deployments — ensuring insight into data processing methods, model formats, AI pipelines, training methods, model serving, production monitoring and more. This level of control and customization is unattainable with off-the-shelf, or with third-party AI solutions. Product and engineering teams must innovate without constraints, deploying AI and customizing the implementation for your organizational needs.
You wouldn’t trust your most critical IP to a third party, and AI will increasingly become some of your most important IP. Over time, enterprise teams will create an increasing number of proprietary algorithms and approaches. By controlling enterprise AI, you can own and scale this important intellectual property, maintaining or growing your enterprise's competitive edge.
The ability to experiment, iterate, and adapt quickly is crucial for determining where AI can help your organization. Owning your AI systems ensures that you can foster an environment of continuous innovation, where organizational teams can explore new applications and rapidly respond to market changes. Third-party solutions can be short-lived, as they constrain innovation to third-party update cycles and schedules. Empowering your organization to own its AI enables your enterprise to drive innovation forward quickly.
While establishing in-house AI capabilities requires upfront investment, rapidly deploying AI across an enterprise is expensive. Third-party services often come with recurring fees and scaling costs that can escalate unpredictably — particularly on a token or context basis. Owning your AI future means that you can manage cost and scale AI within resource allocation and budgetary control standards. Escalating costs from a lack of control of your AI infrastructure systems often means months or years of challenges as you race to reduce your third-party AI dependency.
Safeguarding data privacy and security is paramount. AI systems thrive on vast datasets, often containing sensitive and proprietary information. By maintaining control over your AI infrastructure, your teams can implement robust security measures tailored to their unique needs. This control mitigates the risk of data breaches and unauthorized access — issues that can be magnified when outsourcing AI solutions. Ensuring that you control AI in-house allows your product and engineering teams to build a fortress around their data, ensuring its integrity and confidentiality.
For enterprises in finance, healthcare, and telecommunications - there are real and stringent regulations governing data handling and processing. In these sectors, enterprise teams face the difficult challenge of ensuring AI systems not only scale correctly, but also comply with strict state and federal legal requirements. By owning your AI, you can directly implement and monitor compliance measures - reducing the risk of regulatory breaches and associated penalties.
AI systems are deeply reliant on data and its quality. Enterprises with control over their AI infrastructure have greater oversight of data preprocessing and cleaning procedures, ensuring high data integrity standards which improves AI model quality. Further, this enables active identification and mitigation of biases in AI models, delivering fairer and more accurate outcomes. The lack of transparency into third-party services, indicates that you do not have deep insight into what is actually being done with your data.
Many enterprises operate within complex IT ecosystems that blend legacy systems, databases, and applications with modern code bases. A lack of control over AI workloads means its can be hard to scale AI into these environments. Controlling your AI deployment infrastructure enables you minimize integration challenges, building a more cohesive technology stack.
Without question, growing and scaling your AI talent ensures you can constantly scale with the latest AI breakthroughs. Building an AI-first enterprise requires deep investment in product and engineering teams and a commitment to developing expertise to scale AI systems. This will only make your organization more self-sufficient and resilient. All your teams must be AI teams, and fostering a strong internal culture of innovation and talent development ensures you can own your AI future.
At Modular, we have been at the forefront of building infrastructure that hands back ownership and control to enterprises seeking to own their AI future. As leaders in AI infrastructure who helped build the infrastructure that shaped today’s AI industry, we have taken a new approach to help developers and enterprises answer the critical question: What if deploying AI workloads into production was so simple you could do it yourself?
That’s why we built MAX - the Modular Accelerated Xecution Platform.
MAX gives you everything you need to deploy low-latency, high-throughput AI applications into production with minimal effort. More specifically, it provides the following:
MAX works with the industry-standard APIs and protocols that have become ubiquitous with the mass adoption of Generative AI and LLMs, including the OpenAI’s completion and chat APIs. Importantly, this significantly reduces the cost of migrating your application code to your AI systems, giving you the flexibility to choose the best engine for your needs.
MAX provides a state-of-the-art AI compiler and runtime that optimizes the latency and throughput of popular open source AI models like Llama3-8B and Gemma2 across a wide range of AI hardware, from local laptops to common cloud instances. MAX enables you to seamlessly move the same model, without code changes, across a wide range of CPU architectures—Intel, AMD, ARM—and GPUs, allowing you to take advantage of the breadth and depth of different cloud instances at the best price, and always get the best inference cost-performance ratio across cloud environments.
For most organizations experimenting with GenAI and LLMs, this isn’t their first foray into AI. Many of them already have traditional AI models scaled in production. These organizations have been forced to split their infrastructure efforts, despite already having well established standard infrastructure. MAX standardizes this existing infrastructure and extends it to GenAI and LLMs, replacing the parts of the stack that matter most. MAX is compatible with all PyTorch and ONNX models, including object detection, recommenders, and much more. It integrates with industry standard technologies such as Triton Inference Server, Docker, Prometheus, and Grafana.
For Enterprises looking to get their hands dirty and adapt GenAI and LLMs to their needs, MAX provides clean, composable abstractions, including the Serve, Engine, Drive, Graph, and Extensibility APIs. These abstractions enable users to go beyond stock LLMs and build a moat for their business in areas of AI that have yet to become mainstream.
Finally, MAX is deployable into any cloud or on-prem environment. Enterprises maintain data sovereignty, privacy, and control over how their data is used and how their AI services are scaled. When they’re ready, users can adopt the Enterprise Edition and get world-class support from the team that scaled Google’s AI.
The decision for an enterprise to control its own AI is more than a strategic one — it's a call to action for the entire organization to become AI first. It signifies a commitment to data security, customization, and innovation. By taking charge of AI, enterprises empower their entire organization to craft sophisticated solutions that drive long-term success. This approach protects valuable data and intellectual property. It positions their team at the forefront of technological advancement—ready and able to drive innovation and seize the AI opportunities that lie ahead.
By adopting MAX in your enterprise, you can drive this innovation across your entire organization, ensuring that you are in a position to control for your AI future. Learn more here, and contact us if you need help.
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