AI-native startups built Lego stacks
Edo Liberty, founder and CEO of Pinecone, on the companies indexed on OpenAI
The real signal here is that AI native startups treated the stack like Lego, not like enterprise software. Teams already comfortable with APIs would combine an LLM, a vector database, and an app hosting layer in a weekend, because each piece did one narrow job, embeddings made data searchable, Pinecone stored and retrieved it, and tools like Vercel shipped the app. That made best of breed infrastructure feel more natural than buying one big bundled product.
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In practice, the common build was simple. Data got chunked and turned into embeddings, the embeddings were stored in a vector index, a user question was embedded the same way, relevant chunks were fetched, and the LLM answered with that context. Google Cloud now documents this exact multi service RAG pattern across storage, embedding, vector search, and app services.
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This favored developer first vendors. Pinecone sold a managed database that handled low latency search, uptime, and scaling, while LangChain sat in the middle so builders could swap model providers or vector stores later. That reduced lock in fears and made it easier for small teams to start with hosted tools instead of self managing open source infra.
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The tradeoff is that what feels easy for a startup can feel messy to a big company. Larger enterprises often prefer a single approved vendor and tighter procurement, which is why cloud platforms keep bundling embeddings, vector search, and model hosting together. That turns the market into best of breed specialists versus full stack cloud suites.
The next phase is less about proving that cobbled together stacks work, and more about who becomes the default assembly layer. As AI apps move from prototypes into core workflows, the winners will be the vendors that keep the flexibility developers want, while adding the reliability, security, and distribution that pull larger enterprises onto the same stack.