Not every AI startup needs to build its own LLM. This guide breaks down the infrastructure, API, and application layers of the generative AI stack, helping founders choose where to play—and how to win. With case studies of Jasper, Cohere, and CoreWeave.

The generative AI landscape is buzzing with opportunity, but not every startup needs to build a large language model (LLM) from scratch to make an impact. For aspiring founders, product managers, and investors, the key to success lies in strategically selecting the right layer of the generative AI stack—whether it’s the infrastructure layer, the API layer, or the application layer. Each layer demands distinct resources, expertise, and positioning, and understanding these differences is critical to carving out a competitive edge. This article explores the requirements for succeeding in each layer, highlights real-world examples like Jasper and Cohere, and explains how startups can leverage open-source models and cloud infrastructure to integrate across layers, all while delivering high-value, enterprise-ready solutions.

Understanding the Generative AI Stack

The generative AI ecosystem is structured in three primary layers: infrastructure, APIs, and applications. The infrastructure layer provides the computational backbone—GPUs, TPUs, and cloud platforms—that powers model training and deployment. The API layer offers pre-trained models or fine-tuning services, enabling developers to integrate AI capabilities without building models themselves. The application layer delivers end-user solutions, such as chatbots or content creation tools, that directly address customer needs. Choosing the right layer depends on a startup’s resources, technical expertise, and market positioning, as each comes with unique challenges and opportunities.

The Infrastructure Layer: Powering the AI Revolution

What It Takes to Succeed

The infrastructure layer is the foundation of generative AI, providing the computational resources needed to train and deploy models. Success here requires significant capital investment, as building AI data centers can cost billions—Morgan Stanley estimates that major cloud providers spent $175 billion on AI infrastructure in 2024 alone. Startups in this layer need access to high-performance GPUs or TPUs, robust storage solutions, and scalable cloud or on-premises systems. Talent is another critical factor: hiring experts in distributed computing, hardware optimization, and AI model training is essential but expensive, with top engineers commanding premium salaries.

Beyond technical requirements, infrastructure startups must navigate regulatory compliance, particularly for data security and privacy, especially in industries like finance and healthcare. Partnerships with cloud providers like AWS, Google Cloud, or Oracle Cloud Infrastructure (OCI) can reduce costs and provide access to enterprise-grade security and scalability. However, the high barrier to entry means only well-funded startups or those with strategic partnerships can compete effectively.

Example: CoreWeave

CoreWeave, a cloud provider specializing in GPU-powered infrastructure, has emerged as a key player in the AI infrastructure space. By offering scalable, high-performance computing tailored for AI workloads, CoreWeave supports companies building generative AI models without the need for in-house data centers. Its focus on flexibility and cost-efficiency has made it a go-to choice for AI startups, demonstrating the potential for infrastructure-layer companies to thrive by addressing niche needs.

Strategic Considerations

For most startups, entering the infrastructure layer is daunting due to its capital intensity. However, those with access to funding or partnerships can differentiate by offering specialized solutions, such as edge AI for low-latency applications or secure, private cloud environments for regulated industries. Leveraging existing cloud platforms can also lower costs, allowing startups to focus on optimizing compute efficiency rather than building hardware from scratch.

The API Layer: Enabling Developer Access

What It Takes to Succeed

The API layer focuses on providing pre-trained models or fine-tuning services that developers can integrate into their applications. This layer requires less capital than infrastructure but still demands significant investment in model development and optimization. Costs include compute resources for training and inference, as well as ongoing expenses for model retraining and maintenance. Talent needs are high, with a focus on machine learning engineers and data scientists skilled in natural language processing (NLP) and model fine-tuning.

Startups in this layer must prioritize ease of integration, offering intuitive APIs and robust documentation to attract developers. Security and privacy are also critical, as enterprises demand compliance with regulations like HIPAA or GDPR. Partnering with cloud providers or leveraging open-source frameworks like Hugging Face’s Transformers can reduce development costs and enhance scalability.

Example: Cohere

Cohere, a Canadian AI startup valued at $5.5 billion, exemplifies success in the API layer. Founded by former Google Brain researchers, Cohere offers large language models optimized for enterprise use cases, such as text generation, semantic search, and summarization. Its cloud-agnostic approach allows deployment on platforms like AWS, Google Cloud, or OCI, ensuring flexibility and privacy for clients. Partnerships with Oracle, SAP, and RBC have expanded Cohere’s reach, embedding its models into enterprise workflows for finance, healthcare, and more. By focusing on tailored, smaller models rather than massive LLMs, Cohere achieves capital efficiency and meets specific customer needs.

Strategic Considerations

API-layer startups can differentiate by offering specialized models for verticals like healthcare or finance, where customization and compliance are paramount. Fine-tuning on proprietary data or integrating retrieval-augmented generation (RAG) can enhance accuracy and reduce costs. Open-source models, such as Meta’s Llama or Mistral AI, provide a cost-effective starting point, allowing startups to focus on customization rather than building models from scratch.

The Application Layer: Solving Real-World Problems

What It Takes to Succeed

The application layer is where AI meets end users, delivering solutions like chatbots, content creation tools, or automated workflows. This layer is less resource-intensive than infrastructure or APIs, as startups can leverage existing models via APIs or open-source frameworks. Costs include software development, user interface design, and ongoing maintenance, with cloud hosting expenses varying based on scale. Talent needs focus on product managers, UX designers, and software engineers, though AI expertise is still valuable for fine-tuning or prompt engineering.

Success in this layer hinges on understanding customer pain points and delivering intuitive, value-driven products. Startups must also ensure compliance with data privacy regulations and integrate explainability to build user trust. Partnerships with API providers like Cohere or cloud platforms like Google Cloud can accelerate development and reduce costs.

Example: Jasper

Jasper, a generative AI startup, focuses on the application layer by offering a content creation platform for marketing teams. Its user-friendly interface allows businesses to generate blog posts, social media content, and ad copy using pre-trained models. Jasper’s success stems from its focus on specific use cases and seamless integration with existing workflows, making it accessible to non-technical users. By leveraging APIs from providers like OpenAI or Cohere, Jasper avoids the cost of building its own models, focusing instead on delivering value to customers.

Strategic Considerations

Application-layer startups thrive by targeting niche markets or verticals, such as legal document automation or customer support chatbots. Using open-source models or APIs from providers like Cohere allows startups to focus on user experience rather than model development. Incorporating RAG or fine-tuning can enhance personalization, while cloud platforms provide scalable hosting and compliance tools.

Plugging into Other Layers

Startups can maximize their impact by integrating across layers using open-source models and cloud infrastructure. Open-source frameworks like Hugging Face’s Transformers or Meta’s Llama allow startups to build on existing models, reducing development costs. Cloud platforms like AWS, Google Cloud, or OCI provide scalable compute, storage, and security, enabling startups to deploy models efficiently. For example, an application-layer startup like Jasper can use Cohere’s APIs to power its platform, hosted on Google Cloud for scalability. Similarly, an API-layer startup can leverage CoreWeave’s GPU infrastructure to train models cost-effectively.

Choosing the Right Layer

The decision to focus on infrastructure, APIs, or applications depends on a startup’s resources and goals. Infrastructure offers high impact but requires significant capital and expertise, making it suitable for well-funded teams with hardware experience. The API layer balances technical innovation with accessibility, ideal for startups with AI expertise and enterprise connections. The application layer is the most accessible, perfect for founders who prioritize user needs and rapid market entry.

For most startups, the API or application layer offers a lower barrier to entry. By leveraging open-source models or cloud infrastructure, founders can focus on differentiation—whether through customized APIs or user-centric applications—without the prohibitive costs of building LLMs. Examples like Cohere and Jasper show that success lies in addressing specific needs, whether enabling developers or empowering end users.

Building a generative AI startup is an exciting but complex endeavor. By carefully choosing a layer—whether infrastructure, APIs, or applications—founders can align their strategy with their resources and market opportunities. Infrastructure demands heavy investment but powers the AI ecosystem; APIs enable developer access with flexibility; and applications deliver direct value to users. Leveraging open-source models and cloud platforms allows startups to integrate across layers efficiently, reducing costs and accelerating growth. As the generative AI market evolves, startups like Cohere and Jasper demonstrate that strategic focus, combined with the right tools, can drive meaningful impact in this transformative field.