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# Introducing integrated inference: Embed, rerank, and retrieve your data with a single API
- URL: https://www.ai-360.online/pinecone-introducing-integrated-inference-embed-rerank-and-retrieve-your-data-with-a-single-api/
- Published: 2024-12-02T12:00:00.000Z
- Updated: 2026-10-25T22:30:53.000Z
- Description: ← Blog Introducing integrated inference: Embed, rerank, and retrieve your data with a single API Gibbs Cullen , Gareth Jones Dec 2, 2024 Product Share:…
- Author: Nicholas Lipinski
- Tags: news, #company-pinecone, 1. Enterprise AI

← Blog Introducing integrated inference: Embed, rerank, and retrieve your data with a single API Gibbs Cullen , Gareth Jones Dec 2, 2024 Product Share: Share: Subscribe to Pinecone Get the latest updates via email when they're published: Get Updates Update: Indexes for sparse vectors are now generally available, [the company said on 2 December 2024](https://www.pinecone.io/blog/integrated-inference?ref=ai-360.online).

Pinecone: Introducing integrated inference: Embed, rerank, and retrieve your data with a single API, the company said on 2 December 2024\. We’re excited to announce expanded inference capabilities alongside our core vector database to make it even easier and faster to build high-quality, knowledgeable AI applications with Pinecone. We introduced embedding and reranking earlier this year.

Today, we’re announcing the GA of those capabilities and bringing inference closer to the database with a native integration and fully managed support for new models developed by Pinecone and Cohere so you can now embed, rerank, and query your data with a single API. Tl;dr: What’s new Database endpoints that integrate embedding and reranking More models, fully hosted alongside our database: Pinecone reranking model, pinecone-rerank-v0 Pinecone sparse embedding model, pinecone-sparse-english-v0 Cohere’s Rerank 3.5 model, cohere-rerank-v3.5 Streamline AI development with fast, easy access to leading models To help you unlock the full value of your unstructured data, Pinecone has expanded its knowledge platform with native access to leading embedding and reranking models, all accessible through new database endpoints .

For enterprise buyers, the useful next step is to map the claim to controls, data residency and measurable outcomes—and to ask for evidence on failure modes, oversight and auditability before treating the release as production-ready fact.

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