It might be surprising, but vector databases predate the recent hype around large language models (LLMs). LLMs began gaining serious public momentum in late 2022 with the release of ChatGPT. Pinecone itself was founded in 2019. That said, vector search systems had already been used for years in areas like computer vision and recommendation systems. Today, they’ve become essential infrastructure in the age of large-scale AI.
As interest in LLMs has grown, many legacy database vendors have added vector search features, with varying levels of performance and depth. Meanwhile, the Pinecone team often emphasizes that a dedicated vector database offers unmatched performance and scalability, especially under high-load scenarios. That message can sometimes make Pinecone seem like it’s built only for ML experts or enterprise-grade systems. But that’s far from the truth.
Pinecone is also an excellent choice for developers who are just beginning to explore what vector databases can do, thanks to:
Of course, we’d say that -- it’s our product. However, it’s better to hear it from people who are just starting to code.
We recently partnered with the School of Code during their AI Week. The students used Pinecone to build working AI applications in just a few days. This short video captures their projects and impressions:
While the apps are indeed beginner-level, they show that even newcomers can build practical AI experiences with modern tools. The signal is clear: Pinecone is not just for enterprises; - it’s for anyone who wants to build knowledgeable AI -- today.
If you’re curious to try it yourself, Pinecone offers a free plan that’s perfect for learning and experimentation. You can explore examples, read more about how vector databases work, or start building your own AI-powered app—all at your own pace. No experience required.
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