Pinecone Blog

Pinecone introduces three new bulk data operations—Update, Delete, and Fetch by Metadata—enabling developers to manage vector database records at scale using metadata filters instead of collecting IDs manually. These operations simplify common tasks like bulk updates, GDPR compliance, and data cleanup.

This blog post, "The Hidden Cost of Building: Lessons from Aquant," discusses the challenges and hidden costs associated with building and maintaining in-house AI infrastructure, particularly vector databases. It highlights how what initially seems like a cost-effective solution can become a liability as systems scale, leading to increased complexity, knowledge loss, and delayed product features. The article uses Aquant's experience as a case study, where they ultimately chose to partner with Pinecone for their vector database needs, allowing them to focus on their core business innovation. The post concludes by emphasizing that the true cost of building is measured in time, expertise, opportunity, and momentum, rather than just dollars.

This article explores Pinecone’s integrated inference capabilities for generating vector embeddings directly within your Pinecone workflow. It walks through how integrated inference simplifies embedding pipelines by removing the need for separate model hosting or API calls, and explains a real-world customer scenario where a metadata limit was encountered. The post also provides a simple workaround using the Inference API directly, helping developers get the best of both simplicity and control.

We are excited to join the new Microsoft Marketplace and be a featured partner in the AI Apps and Agents category. If you’re already on Azure or leveraging Azure’s OpenAI Service, you can now quickly add Pinecone to your AI stack with just a few clicks.

Learn how to build AI-powered sales outreach using Clay and Pinecone. Automate personalized emails with vector search, web crawling, and contextual case studies for GTM teams.