Why Digital-First Companies Are Building Their Own AI Platforms
10 Advantages of Custom AI Platforms
Despite the abundance of AI services & platforms in the market, many tech-forward companies are taking a different route: building their own custom AI platforms. This raises a crucial question: why craft a bespoke AI platform when so many options already exist? The classic "buy vs. build" debate takes on new layers in the AI era, pushing companies to consider factors like cost, scalability, and strategic value. To fully appreciate the decision-making process, I sought out expert viewpoints through a combination of presentations, blog posts, and in-depth discussions with technical leaders.
One compelling reason is talent acquisition and retention. Custom AI platforms are a magnet for top-tier talent, offering cutting-edge and engaging projects. Another significant factor is experimentation and research. Custom platforms provide a controlled and flexible environment for conducting advanced AI research and experimentation, crucial for companies at the forefront of AI innovation.
But these are just the tip of the iceberg. As alpha geeks often lead in tech adoption, understanding their motivations for custom builds can provide essential lessons for the broader market. Now, let's be clear: this isn't just about tech companies flexing their muscles or indulging in some misguided NIH (Not Invented Here) syndrome. The reasons behind this trend are complex and multifaceted. And while you might not share every concern on this list, there are undoubtedly lessons here that can help shape your approach to building generative AI systems & solutions. Let's explore the full list:
Unique Business Requirements and Use Cases
Many companies have highly specialized business needs, unique use cases, or operate in niche domains that generic off-the-shelf solutions cannot adequately address. Building custom AI/ML platforms allows them to tailor the solution precisely to their specific requirements, data types, and business objectives.
Scale and Performance Requirements
Companies dealing with massive datasets, computationally intensive workloads, or stringent performance requirements often find that off-the-shelf solutions lack the necessary scalability and optimization capabilities. Building custom solutions allows them to tailor the infrastructure and algorithms to their specific scale and performance needs, ensuring optimal efficiency and cost-effectiveness.
Integration with Existing Infrastructure and Tools
Companies have existing data infrastructure, tools, and workflows that need seamless integration with their AI/ML solutions. Custom solutions allow for tighter integration with existing data infrastructure, tools, and workflows, avoiding the compatibility nightmares that can arise with off-the-shelf options. This tailored approach ensures a cohesive and efficient technology ecosystem, maximizing the value of both existing infrastructure and new AI/ML capabilities.
Flexibility, Customization, and Future-Proofing
Building custom solutions offers greater flexibility to adapt and customize the platform to evolving needs, while also future-proofing AI infrastructure. This includes incorporating new technologies, integrating specific libraries or frameworks, and tailoring the user interface and workflows. Owning the full stack also provides more control over the roadmap and feature development, allowing companies to create flexible architectures that can more easily incorporate future AI advancements and emerging technologies.
Cost Optimization and Efficient Resource Utilization
Custom solutions allow companies to optimize costs by controlling infrastructure choices and resource allocation. This includes leveraging existing resources, implementing fine-grained cost control measures, and optimizing for specific workloads, often resulting in significant cost savings at scale compared to off-the-shelf solutions. This trend of cost optimization through custom solutions isn't unique to AI platforms; many companies find substantial savings by moving away from cloud services across various applications. Having full control over infrastructure choices and resource allocation enables companies to optimize costs with surgical precision.
Competitive Advantage and Proprietary AI Capabilities
For many digital-first companies, building custom AI infrastructure is a key competitive differentiator, enabling them to develop unique AI capabilities tailored to their specific domain and use cases. This can lead to superior products and services that are difficult for competitors to replicate, as well as faster innovation and the ability to quickly operationalize new AI advancements. This realization is driving a broader trend, positioning custom AI platforms not just as a competitive edge, but as a fundamental building block for future business success across sectors.
Security, Compliance, and Responsible AI Practices
For companies dealing with sensitive data, operating in regulated industries, or prioritizing responsible AI practices, custom solutions provide greater control over security, compliance measures, and ensuring transparency, fairness, and explainability of AI models.
Developer Productivity and Streamlined ML Workflows
Improved developer productivity and streamlined ML workflows are often cited as key reasons for building custom platforms. Custom solutions allow companies to create workflows and tools specifically optimized for their data scientists and ML engineers, often including features like simplified resource allocation, integrated experiment tracking, and streamlined deployment pipelines.
Handling Complex Workflows and End-to-End ML Lifecycle Management
The machine learning lifecycle is a complex beast, from data processing to model deployment and monitoring. Many companies require comprehensive platforms to manage the entire machine learning lifecycle, from data processing to model deployment and monitoring. Custom solutions allow companies to create tightly integrated workflows that seamlessly connect different stages of the ML process, reducing friction in moving from experimentation to production.
Specialized Data Handling and Processing
Some companies have unique data processing needs or complex data types that drive them towards custom solutions. Custom platforms allow for optimized data pipelines tailored to specific data formats, processing requirements, and domain-specific optimizations.
Key Takeaways
While this article draws heavily on examples from digital-first companies with robust engineering teams, the underlying reasons for building custom AI platforms offer valuable lessons for organizations of all sizes. Even if you don't have the resources of an Uber or a Netflix, understanding their motivations can help you make informed decisions about your own AI strategy.
Leveraging Open Source Tools. Many open-source components now make building a custom platform more accessible to AI teams. For example, Ray, an open-source distributed computing framework, serves as a foundation for many custom AI platforms, offering scalability, performance optimization, and the ability to manage the entire ML lifecycle.
Prioritizing Business Value. Carefully evaluate whether a custom platform truly aligns with your unique business needs, data requirements, and long-term strategic goals before deciding between custom and off-the-shelf solutions.
Considering the Total Cost of Ownership. Evaluate not just the upfront development costs but also the long-term expenses related to maintenance, infrastructure, and talent required to build and sustain a custom AI platform.
Embracing Flexibility and Future-Proofing. Design your custom platform with adaptability in mind, allowing for easy integration of new technologies and evolving business requirements to future-proof your investment.
Focusing on Developer Experience. Prioritize developer productivity by providing streamlined workflows, intuitive tools, and a unified platform that simplifies the ML lifecycle, enhancing efficiency and adoption within your organization.
Enhancing Tailored Governance. While many AI platform providers support enhanced control over AI governance, custom platforms allow for an even higher level of tailored control. This ensures the governance frameworks are precisely aligned with a company’s specific ethical guidelines and regulatory requirements.
Accelerating Innovation Cycles. Owning the entire AI stack enables rapid prototyping, testing, and iteration of new AI technologies and methodologies, accelerating innovation cycles and reducing time-to-market.
Aligning Culture and Internal Knowledge Growth. Building a custom AI platform fosters a culture of continuous learning and innovation, developing deep internal expertise and knowledge that serve as a competitive advantage.
Implementing Custom Security Measures. Custom AI platforms support the implementation of tailored security protocols and access controls, ensuring sensitive data is protected in ways that off-the-shelf solutions might not support.
Optimizing Specialized Data. While many AI platform providers support the utilization of proprietary data, custom platforms offer more specialized optimization and integration capabilities tailored to a company's unique data and operational needs, enabling unique insights and performance improvements.
The trend towards custom AI platforms among digital-first companies reveals compelling advantages, but it's crucial to approach this with a critical eye. While off-the-shelf solutions offer convenience, a custom approach can unlock unparalleled flexibility, scalability, and strategic advantage tailored to your unique needs - if you can justify the astronomical costs and overcome the technical hurdles. This doesn't have to mean abandoning existing tools and platforms entirely; rather, it's about exploring the full spectrum of possibilities—from hybrid models to strategically leveraging custom components—and understanding how those choices align with your broader AI strategy.
As you navigate the evolving AI landscape, ask yourself: what could you achieve with an AI platform built specifically for your vision, and is it worth the potentially large investment? The key is to align your AI infrastructure with your long-term goals, unique data needs, and specific use cases, while remaining acutely aware of the financial and technological risks involved. Just remember, the AI revolution promises fire, but it might deliver smoke if we're not careful with our resources and expectations.
View the complete slideshow of all the slides used in this article by clicking here.
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Ben Lorica edits the Gradient Flow newsletter. He helps organize the AI Conference, the NLP Summit, Ray Summit, and the Data+AI Summit. He is the host of the Data Exchange podcast. You can follow him on Linkedin, Twitter, Reddit, Mastodon, or TikTok. This newsletter is produced by Gradient Flow.
I thought this was a compelling deep dive into how digital-first organizations are shifting from off-the-shelf AI to owning their entire model stack. Great work. I was specifically interested in your point that unique operational data can become an accelerating moat. I did have a question for you, though: could companies co-develop distributed data-and-model ecosystems (rather than monopolizing them) so that emergent “community models” form the next wave of competitive advantage? In other words can openness become a strategic moat?