Beyond Products: Why Cloud and Data Architects Must Think Like Platform Builders in the AI Era

In today's rapidly evolving tech landscape, understanding the difference between building a platform and building a product is no longer just important for product managers (PMs) and product marketing managers (PMMs). It's equally crucial for cloud architects and data architects. The challenge becomes not just about delivering software but about creating an ecosystem where external contributors can plug in, extend functionality, and scale solutions to meet complex business needs.

Let’s break this down from a cloud and data architecture viewpoint, with a forward-thinking focus on the integration of AI models.

A Quick Primer

• Product: A single, standalone solution designed to solve a specific problem for users. It’s typically built around a well-defined theme. For instance, Slack started as a communication tool, solving the pain of fragmented team conversations.
• Platform: A flexible, scalable foundation that solves problems for users while enabling external developers to build upon it. Platforms aren’t limited by their own capabilities; instead, they extend through third-party integrations, APIs, and custom applications. For example, Miro allows users to integrate various plugins and apps, expanding its core capabilities to suit different use cases.

Now, let's dive into how cloud and data architects approach building platforms vs. products.

Cloud Architect's Perspective: The Backbone of Platforms

For a cloud architect, building a platform involves crafting the infrastructure to support external integrations, scalability, and extensibility. The focus shifts from solving direct user needs (as with a product) to creating a robust, adaptable architecture that supports a wide array of uses.

1. Scalability & Elasticity

When building a product, cloud resources are often optimized for specific workloads. But a platform? It needs to be elastic. The architecture must anticipate not only growing user bases but also external developers who will leverage your platform’s APIs and SDKs.

Take AI models, for example. A product might use AI to enhance specific features (like Slack’s message filtering). But a platform needs to support AI extensibility, enabling third-party developers to train and deploy models on the platform. Think of AWS Sagemaker or Azure ML Studio—these platforms allow developers to use AI capabilities at scale, training models directly within the platform’s environment.

Key questions cloud architects face:

• How do we ensure dynamic scalability for AI workloads?
• What is the best way to manage and optimize multi-tenant architectures while maintaining high performance?

2. APIs and Extensibility

Cloud architects building a platform must design developer-friendly APIs. These APIs are not just for internal use—they must be clear, secure, and flexible enough for third-party developers to extend the platform. Platforms like Google Cloud offer APIs that integrate with TensorFlow and AI models, allowing users to deploy, train, and fine-tune models without leaving the ecosystem.

This extensibility also involves creating sandbox environments for developers to test their integrations safely. The cloud architect has to think not just about functionality but about ease of use, because a thriving developer ecosystem is key to platform success.

Key questions cloud architects face:

• How do we optimize APIs for high-load applications, especially AI-driven integrations?
• Should we create specific AI-focused SDKs for third-party developers to integrate machine learning (ML) capabilities?

3. Security & Compliance

As platforms open up to third parties, security risks multiply. It’s not enough to secure the platform's core features; cloud architects need to secure external integrations and data flows. For instance, AI-driven applications handling sensitive data must comply with stringent security protocols like GDPR or HIPAA.

Key questions cloud architects face:

• How do we secure sensitive AI data processed by third-party integrations?
• What compliance frameworks should be built into the platform’s foundation?

Data Architect’s Perspective: Harnessing Data for Growth

In a platform world, data is the fuel that powers both internal and external applications. From a data architect’s perspective, the challenge shifts from merely designing systems that store and manage data to creating a platform that allows seamless data integration and governance across many applications.

1. Data Integration & Interoperability

When building a product, a data architect focuses on organizing data pipelines specific to the product’s needs. However, with a platform, data architects must ensure interoperability across multiple data sources and integrations, especially when dealing with AI models.

For example, platforms like Databricks provide the infrastructure for external users to run AI models by seamlessly integrating with external data lakes. A platform-oriented data architecture must offer easy integration with different types of data stores (structured, unstructured, streaming data, etc.), allowing external developers to plug into the platform without friction.

Key questions data architects face:

• How do we enable real-time AI data streaming across applications?
• How can we ensure smooth data integration while maintaining data integrity for AI processing?

2. Data Governance & Compliance

As third-party applications proliferate on a platform, ensuring data integrity, security, and compliance becomes more complex. A data governance framework must be in place to manage both platform-generated and third-party data. Moreover, for AI-based models that might require massive datasets, governance is critical to avoid biases or misuse of sensitive data.

Key questions data architects face:

• How can we build data governance frameworks that support AI-driven third-party applications?
• How do we ensure that AI models built on the platform comply with data privacy regulations?

3. Data as a Service (DaaS)

With platforms, data isn’t just an internal asset—it becomes part of the platform’s value proposition. Platforms like Snowflake offer data-sharing capabilities as a service, allowing external developers to tap into datasets for AI model training or analytics.

In this case, the data architect plays a pivotal role in designing systems that allow secure data sharing, monetization strategies, and management of data subscriptions—paving the way for external applications to thrive on the platform.

Key questions data architects face:

• How do we create value from the platform’s data to power third-party AI models?
• What data monetization strategies can we build into the platform to enable developers to access high-quality datasets?

AI Models: The Future of Platform Strategy

As AI becomes an integral part of modern platforms, cloud and data architects must design systems that not only support the use of AI but also enable developers to build, train, and deploy models on the platform. From API optimization for AI inference to handling massive training datasets, the ability to support AI-driven applications will increasingly differentiate platforms from products.

Closing Thoughts:

Building a platform isn’t just about solving a single problem—it’s about creating an ecosystem where developers can expand, enhance, and innovate. As a cloud or data architect, you’re no longer just a builder; you’re a visionary architect, laying the foundation for future growth.

So, what are you building?

• Are your APIs optimized for AI integrations?
• Is your platform designed to scale with third-party apps?
• How are you securing AI-driven data in a multi-tenant environment?

Platform or Product: Which path are you on?

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