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.
Verizon, for instance, requires a highly tailored recommender system to personalize offerings across various customer touchpoints, integrating diverse data sources to meet their specific business goals. Niantic’s AR mapping and Visual Positioning System exemplify the need for bespoke solutions, processing user-submitted scans and building 3D maps at a scale and performance level unmatched by existing platforms. Samsara optimizes models for IoT edge devices like dashcams, addressing a niche requirement that generic platforms struggle to support. Spotify’s AI initiatives demand deep integration with their existing tools and infrastructure, necessitating a custom approach. DoorDash and Uber, operating in the competitive food delivery and ride-sharing markets, develop tailored AI solutions to optimize their unique operational challenges. Instacart similarly builds custom AI tools to enhance its grocery delivery service, addressing specific needs in inventory management and order fulfillment.
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.Â
Netflix processes and serves recommendations for millions of users and billions of events daily, necessitating a custom solution to handle this immense volume. Pinterest grapples with petabyte-scale data generated by hundreds of millions of users, requiring a platform that can efficiently iterate on large datasets and optimize resource utilization. Uber’s operations involve over 5,000 models in production, serving an astounding 10 million real-time predictions per second at peak times. To manage this complexity and volume, Uber has developed Michelangelo, a custom-built platform tailored to their specific needs. Two Sigma, Instacart, and IPRally also cite the need for handling massive scale and performance requirements as key drivers for building their own solutions. These companies find that off-the-shelf solutions often struggle to scale to such extreme levels, making custom platforms essential for optimizing every aspect of their systems—from data processing to model training to serving.
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.
Spotify’s AI platform integrates effortlessly with their existing tools like Flyte, Backstage, and internal data endpoints. DoorDash’s custom platform enables deep integration with their data lake, feature store, and monitoring tools. Netflix’s solution smoothly integrates with their existing infrastructure, deployment processes using Spinnaker, and data pipelines leveraging Amazon S3 and FSx for Lustre. Pinterest’s custom AI platform connects seamlessly with their Kubernetes infrastructure and internal systems. Verizon’s recommender system integrates tightly with existing customer data sources and touchpoints. Instacart’s AI solution also emphasizes integration with their existing data infrastructure. These custom AI solutions enable deeper integrations, optimized workflows, and a unified ecosystem tailored to specific business needs, enhancing efficiency and streamlining model deployment.
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.
Uber’s Michelangelo platform demonstrates this need, adapting to evolving ML requirements and supporting both batch and real-time use cases. Instacart’s platform accommodates growing data volumes and complex model architectures, while preparing for advanced ML techniques like LLM fine-tuning. Netflix’s custom solution allows them to quickly incorporate ideas from the latest academic research, maintaining their edge in content recommendation and understanding. Niantic’s platform is designed to adapt as AR technology evolves, ensuring they remain at the forefront of immersive experiences. DoorDash’s platform provides the flexibility to support various ML models and libraries, enabling rapid iteration and addressing specific pain points. Spotify’s custom solution allows for rapid prototyping and deployment of new ML applications, tailored to their specific needs and workflows. Stitch Fix has developed a flexible architecture that supports various use cases and allows for easy incorporation of new AI tools and frameworks, ensuring they can continuously leverage the latest AI advancements. Pinterest has built a modular architecture that can integrate emerging technologies like KubeRay, ensuring their system remains relevant and scalable.
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.
Instacart, for instance, significantly reduces AWS costs by optimizing their infrastructure through a tailored platform. IPRally demonstrates the power of custom solutions by achieving a remarkable 70% reduction in compute costs through efficient use of spot instances and optimized ML pipelines. Pinterest improves resource utilization efficiency with their bespoke platform, while Spotify and OneDigital also cite cost optimization as a key driver for building custom solutions. DoorDash now optimizes resource utilization and reduces infrastructure costs significantly by building a custom solution with Ray. Uber finds that developing their own platform is potentially more cost-effective compared to the licensing fees associated with third-party solutions. These examples show that custom AI platforms can offer superior long-term cost optimization and fine-grained control compared to off-the-shelf platforms, especially for companies operating at scale with specific workload requirements.
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.
Niantic’s proprietary AR mapping technology is a crucial differentiator in the AR gaming and application market, enabling them to create unique experiences that set them apart from competitors. Similarly, IPRally’s custom knowledge graph-based patent search capabilities distinguish them in the intellectual property domain, offering unparalleled search precision. In the financial sector, Two Sigma’s bespoke AI platform for financial modeling and trading strategies forms the cornerstone of their competitive advantage in the highly sophisticated quantitative trading industry. Uber’s development of their custom ML platform allows them to offer unique functionalities in the fiercely competitive ride-sharing and food delivery sectors. Samsara, too, has recognized the value of custom AI infrastructure in maintaining their market position.
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.
Verizon prioritizes transparency and understanding of their recommender system’s decision-making process to ensure compliance with legal standards. Similarly, Uber cites the need for robust security and compliance as a key factor in developing their custom platform, Michelangelo. Spotify’s custom solution gives them greater control over security aspects of their ML platform, allowing them to tailor safeguards to their specific needs. Two Sigma highlights the importance of implementing specific security protocols, access controls, and auditing mechanisms that are customized to their requirements and regulatory landscape. Pinterest also emphasizes the significance of security and compliance in their decision to build a custom AI solution. Custom AI platforms empower companies to maintain control over data governance, security, and compliance, ensuring responsible AI practices while adhering to internal policies and external regulations.
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.
Spotify’s platform empowers ML practitioners to rapidly test new ideas and algorithms, fostering innovation and agility. Pinterest’s custom solution enables swift experimentation with diverse dataset variations, particularly beneficial for their recommendation models. DoorDash’s infrastructure focuses on expediting the transition from model development to production, significantly reducing time-to-market for ML solutions. Samsara’s platform takes a holistic approach, enabling data scientists to become “full-stack” ML developers, managing projects from conception to deployment. This end-to-end ownership streamlines the entire ML lifecycle. OneDigital and IPRally also emphasize the importance of custom platforms in boosting developer productivity. These custom ML platforms offer integrated tools for the entire lifecycle, accelerating development from experimentation to production compared to using separate tools. For instance, Samsara’s single platform approach, handling the full ML workflow, improves both developer productivity and consistency by eliminating the need for separate tools at each stage.
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.
Samsara requires a unified platform to cover the full ML lifecycle, addressing gaps in existing solutions. Verizon handles complex workflows involving multiple data sources, model types, and deployment scenarios for their recommender system. Instacart’s custom platform streamlines the transition from research to production environments, addressing a key pain point in their previous workflow. Uber aims to standardize ML development practices across teams by providing a unified platform. Netflix requires a comprehensive solution to manage their complex recommendation systems and content delivery workflows. Spotify needs to integrate audio analysis, user behavior modeling, and personalized playlist generation. Pinterest, with its vast image-based dataset, benefits from a custom solution that seamlessly integrates visual search, content recommendation, and user engagement models. These examples demonstrate how diverse industries are leveraging AI platforms to streamline complex ML workflows from data processing to deployment.
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.
Two Sigma, for instance, requires the capability to handle massive financial datasets and perform complex simulations in real-time. Pinterest needs specialized processing for user engagement data and recommendation datasets to enhance its content discovery algorithms. Niantic faces the distinctive challenge of processing enormous volumes of user-generated scan data for their augmented reality (AR) mapping applications. Additionally, companies like Uber utilize custom platforms for real-time geospatial data processing, while Netflix employs specialized systems for content recommendation and streaming optimization. In the healthcare sector, organizations like Flatiron Health develop custom AI solutions to handle sensitive patient data and complex medical information. These diverse requirements across industries demonstrate the critical role of specialized data handling and processing in driving the development of custom AI platforms.
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.Â
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.
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