LlamaIndex
The leading data framework for connecting custom data sources to large language models through advanced RAG.
The serverless vector database designed for billion-scale AI application infrastructure.
Pinecone is a cloud-native, fully managed vector database designed to handle the complex requirements of high-dimensional data at massive scale. By 2026, Pinecone has evolved its serverless architecture to provide a total decoupling of storage and compute, allowing developers to pay only for exact usage without provisioning clusters. Its core engine utilizes advanced indexing algorithms such as HNSW (Hierarchical Navigable Small World) and proprietary proximity graphs to deliver sub-50ms latency across billions of records. The platform's market position is anchored by its 'RAG-first' features, including integrated metadata filtering, hybrid search capabilities (combining dense and sparse vectors), and automatic namespace isolation. It serves as the long-term memory for Large Language Models (LLMs), enabling contextual retrieval and real-time knowledge updates without retraining models. Pinecone's architecture is optimized for high-throughput upserts and complex filtering, making it the preferred choice for enterprise-grade generative AI, semantic search, and recommendation systems that require SOC2 Type II compliance and multi-region availability.
Separates vector storage from compute resources, scaling automatically based on query demand.
The leading data framework for connecting custom data sources to large language models through advanced RAG.
The Production-Ready Open Source RAG Framework for Pinecone Ecosystems.
Enterprise-grade AI-powered search and discovery platform for hyper-personalized digital experiences.
Industrial-grade vector similarity search for billion-scale datasets.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Allows for attribute-based filtering using MongoDB-style query operators alongside vector search.
Combines dense vector embeddings with sparse keyword vectors for enhanced retrieval relevance.
Support for vectors that contain both semantic meaning and keyword importance (BM25 style).
Ensures that upserted data is available for queries with minimal eventual consistency delay.
Replicates vector data across multiple cloud regions for low-latency global access.
Built-in utilities to migrate data between pod types or from pod-based to serverless without downtime.
Employees cannot find specific internal documentation across siloed PDFs and wikis.
Registry Updated:2/7/2026
Retrieve top contexts via Pinecone
Feed contexts to LLM for final answer
Users struggle to describe complex patterns or clothing styles with text keywords.
Rule-based systems miss sophisticated 'zero-day' attack patterns that don't match known signatures.