🚀 Pinecone BYOC is in public preview.
Run Pinecone inside your AWS, GCP, or Azure account with a zero-access operating model. - Read the blog Dismiss Build Knowledgeable AI The vector database for scale in production Start Building Get a Demo {rag} {search} {recommendations} {agents} Performance at scale for {} The purpose-built vector database delivering relevant results at any scale.
Learn More Popular productivity app providing instant Q&A across company knowledge Customer workload: Total vectors Namespaces Global writes per day Trusted in production The world's most innovative companies are already in production with Pinecone.
USE CASE: RECOMMENDATIONS Read case study Gong achieves efficient vector searches, empowering for concept tracking in conversations.
Smart Trackers to offer users precise and relevant examples USE CASE: SEARCH Read case study Before Pinecone, Vanguard’s customer support teams relied on keyword-based search solutions to search for documents where answers to a customer’s question may live.
With Pinecone and hybrid retrieval, they boosted customer support with faster calls and 12% more accurate responses.
USE CASE: AGENTS "Pinecone also , combining sparse and dense embeddings, to deliver a more robust and accurate search experience.
This flexibility allows us to optimize costs and performance, whether dealing with enterprises with extensive documentation or smaller companies with fewer pages." supports hybrid search USE CASE: RAG "Pinecone aligns with our vision to democratize data accessibility for all engineers, and we're excited to " uncover more potential with its new serverless architecture.
Developer Experience Scale simplified Fully managed and serverless for effortless scaling.
Rapid setup Launch your vector databases in seconds.
Serverless scaling Resources adjust to meet your demand automatically.
Rock-solid reliability Trust in consistent uptime for your critical applications. agent/retriever.py Quickstart guide from pinecone import Pinecone pc = Pinecone("<API KEY>") index = pc.
Index("semantic-search") index.query( namespace="breaking-news", vector=[0.13, 0.45, 1.34, ...], filter={"category": {"$eq": "technology"}}, top_k=3 ) Search Relevance, delivered Advanced retrieval capabilities for precise search across dynamic datasets.
Embeddings Choose from our leading or bring your own vectors. hosted models Optimized recall Benchmark leading algorithms . maximize recall with low latency Filters Retrieve only the vectors that match your . metadata filters Real-time indexing Upserted and updated vectors are in real-time to ensure fresh reads. dynamically indexed Full-text search Get an exact keyword match with when semantic search isn't enough. sparse indexes Rerankers to boost the most relevant matches.
Add an extra layer of precision with rerankers Namespaces to ensure tenant isolation.
Create partitions of your data with namespaces Learn how to achieve with cascading retrieval best-in-class relevance View sample code Works where you do Use Pinecone with your favorite cloud provider, data sources, models, frameworks, and more.
Explore Integrations Enterprise-ready AI Meet security and operational requirements to bring AI products to market faster.
View Security Secure With encryption at rest and in transit, hierarchical encryption keys, private networking, and more, your data is secure. to deploy a privately managed Pinecone region within your cloud.
Contact us Reliable Powering mission-critical applications of all sizes, with uptime SLAs, support SLAs, and observability.
Compliant Control your data and know it's safe.
Pinecone is SOC 2, GDPR, ISO 27001, and HIPAA certified.
Start building knowledgeable AI today Create your first index for free, then pay as you go when you're ready to scale.
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