# Pinecone
> Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away.

[Start Building](https://www.pinecone.io/agents/pinecone/)

## About Pinecone

Pinecone is the leading vector database for building accurate and performant AI applications at scale in production. Pinecone's mission is to make AI knowledgeable. More than 9,000 customers across industries have shipped agents, search, and recommendation systems faster and more confidently with Pinecone. The company is based in New York and backed by Andreessen Horowitz, ICONIQ, Menlo Ventures, and Wing Venture Capital.

## Why Pinecone

We provide the vector database for developers to build the most accurate and performant AI applications at scale – and achieve the fastest time to value for their organizations.

- Provides all the components and capabilities for high-quality, accurate end-to-end retrieval in a single place:
   - Combines cutting edge models (embedding, planning, reranking) with flexible and powerful query mechanisms (vector, keywords, filtering, namespaces,...) to retrieve relevant insights from any kind of data
   - Tightly integrating leading research with engineering enables us to rapidly develop differentiated search and retrieval quality features (e.g., Pinecone's sparse embedding and reranking models)

- Core technological differentiation from the market enables industry-leading performance for large-scale retrieval
   - Proprietary record-breaking Rust implementations of algorithms based on cutting-edge research provide low latency on top of massive datasets
   - Cloud-native architecture that separates storage from compute and reads from writes allows for hands-free scaling up to millions of namespaces, billions of vectors, and 1000s of queries per second (QPS)
   - Built-in freshness means updates are indexed and searchable in seconds for highly-dynamic workloads

- Fully-managed serverless experience provides a simple journey to production for mission-critical workloads
   - No capacity planning, infrastructure management, or tuning needed
   - Enterprise features and deployment options to meet advanced compliance and security requirements
   - Highly reliable with production-grade SLAs

- Best-in-class developer experience makes Pinecone a leading choice among developers
   - Intuitive and easy-to-use APIs, SDKs, documentation, and tooling makes onboarding fast and easy
   - Expansive integrations across the ecosystem enable developers to seamlessly integrate Pinecone into their existing workflows

- Purpose-built architecture enables extremely cost-effective retrieval at scale
   - Object storage-based architecture intelligently tiers and caches data on-demand for cost-effective serving
   - Multi-tenant architecture with efficient resource packing enables customers to only pay for the exact operations they perform
   - Namespaces, delete/fetch by metadata, provisioned capacity, schemas, TTL, and other similar features simplify data and cost management in Pinecone.

## For Agents

- [Pinecone MCP Server](https://www.pinecone.io/mcp/) — Full text search across articles published on the website.
- [Pinecone Docs MCP Server](https://docs.pinecone.io/mcp) — Query Pinecone's documentation through MCP.
- [Pinecone Agent Skill](https://docs.pinecone.io/skill.md) — Use when building vector search applications, semantic search systems, RAG pipelines, recommendation engines, or full-text search systems. Reach for Pinecone when you need to store embeddings, query by similarity, implement multitenancy with namespaces, or combine multiple search methods (semantic, lexical, full-text) in a single index.

## Products

- [Pinecone Vector Database](https://www.pinecone.io/product/md/)
- [Pinecone Assistant](https://www.pinecone.io/product/assistant/md/)
- [Pinecone Dedicated Read Nodes](https://www.pinecone.io/product/dedicated-read-nodes/md/)
- [Pinecone BYOC](https://www.pinecone.io/product/bring-your-own-cloud/md/)
- [Pinecone Nexus](https://www.pinecone.io/product/nexus/md/)

## Navigation

- [Docs](https://docs.pinecone.io)
- [API Reference](https://docs.pinecone.io/reference/api/introduction.md)
- [API Rate Limits & Quotas](https://docs.pinecone.io/reference/api/database-limits.md)
- [Customers](https://www.pinecone.io/customers/)
- [How Pinecone Works](https://www.pinecone.io/how-pinecone-works/md/)
- [Learn](https://www.pinecone.io/learn/)
- [Blog](https://www.pinecone.io/blog/)
- [Research](https://www.pinecone.io/research/)
- [Community](https://www.pinecone.io/community/)
- [Pricing](https://www.pinecone.io/pricing/md/)
- [Contact](https://www.pinecone.io/contact/)

## API Rate Limits

The Pinecone Database API enforces per-project rate limits on read and write requests. When agents exceed a limit, the API returns HTTP 429 with `x-ratelimit-limit-*`, `x-ratelimit-remaining-*`, and `x-ratelimit-reset-*` headers so clients can self-throttle. Limits and quotas vary by plan tier (Starter, Standard, Enterprise).

- [Rate limits and quotas reference](https://docs.pinecone.io/reference/api/database-limits.md)
- [Pricing and plan-specific limits](https://www.pinecone.io/pricing/md/)

## Learn

- [What Indexing Algorithms Does Pinecone Use?](https://www.pinecone.io/learn/pinecone-indexing-algorithms/md/) (2026-06-14)
- [Searching for Birds with Pinecone Full-Text Search](https://www.pinecone.io/learn/searching-for-birds-pinecone-full-text-search/md/) (2026-05-11)
- [How a Knowledge Engine Works: From Artifacts to Agent-Ready Answers](https://www.pinecone.io/learn/how-knowledge-engines-work/md/) (2026-05-07)
- [Skills and MCP and CLI, oh my!](https://www.pinecone.io/learn/skills-mcp-cli-plugins-oh-my/md/) (2026-04-22)
- [Multi-domain RAG in n8n: why one knowledge base is not enough](https://www.pinecone.io/learn/n8n-multi-domain-rag-knowledge-base/md/) (2026-03-27)
- [Building RAG workflows in n8n: choosing the right Pinecone node](https://www.pinecone.io/learn/pinecone-assistant-vs-pinecone-vector-store-node-n8n/md/) (2026-03-10)
- [RAG with Access Control](https://www.pinecone.io/learn/rag-access-control/md/) (2026-01-08)
- [Inside Pinecone: Slab Architecture](https://www.pinecone.io/learn/slab-architecture/md/) (2025-11-04)
- [What is Context Engineering?](https://www.pinecone.io/learn/context-engineering/md/) (2025-07-15)
- [Chunking Strategies for LLM Applications](https://www.pinecone.io/learn/chunking-strategies/md/) (2025-06-28)
- [Beyond the hype: Why RAG remains essential for modern AI](https://www.pinecone.io/learn/rag-2025/md/) (2025-06-25)
- [Retrieval-Augmented Generation (RAG)](https://www.pinecone.io/learn/retrieval-augmented-generation/md/) (2025-06-12)
- [Using Pinecone asynchronously with FastAPI](https://www.pinecone.io/learn/pinecone-async-fastapi/md/) (2025-05-01)
- [Don’t be dense: Launching sparse indexes in Pinecone](https://www.pinecone.io/learn/sparse-retrieval/md/) (2025-03-05)
- [Unlock High-Precision Keyword Search with pinecone-sparse-english-v0](https://www.pinecone.io/learn/learn-pinecone-sparse/md/) (2025-03-05)
- [Pinpoint references faster with citation highlights in Pinecone Assistant](https://www.pinecone.io/learn/pinecone-assistant-citation-highlights/md/) (2025-02-24)
- [Getting started with llama-text-embed-v2](https://www.pinecone.io/learn/nvidia-for-pinecone-inference/md/) (2025-02-19)
- [ How to build an agentic, chat or RAG knowledge system using Pinecone Assistant](https://www.pinecone.io/learn/pinecone-assistant/md/) (2025-01-22)
- [Building a reliable, curated, and accurate RAG system with Cleanlab and Pinecone](https://www.pinecone.io/learn/building-reliable-curated-accurate-rag/md/) (2024-10-25)
- [Four features of the Assistant API you aren't using - but should](https://www.pinecone.io/learn/assistant-api-deep-dive/md/) (2024-10-15)
- [Vectors and Graphs: Better Together](https://www.pinecone.io/learn/vectors-and-graphs-better-together/md/) (2024-09-06)
- [Llama 3.1 Agent using LangGraph and Ollama](https://www.pinecone.io/learn/langgraph-ollama-llama/md/) (2024-09-02)
- [Accelerating Legal Discovery and Analysis with Pinecone and Voyage AI](https://www.pinecone.io/learn/legal-semantic-search/md/) (2024-08-21)
- [Refine Retrieval Quality with Pinecone Rerank](https://www.pinecone.io/learn/refine-with-rerank/md/) (2024-08-15)
- [LangGraph and Research Agents](https://www.pinecone.io/learn/langgraph-research-agent/md/) (2024-07-11)
- [Build Privacy-aware AI software using Pinecone](https://www.pinecone.io/learn/privacy-aware-software/md/) (2024-07-03)
- [The Practitioner's Guide To E5](https://www.pinecone.io/learn/the-practitioners-guide-to-e5/md/) (2024-06-24)
- [Interactive Introduction to Tokenization](https://www.pinecone.io/learn/tokenization/md/) (2024-05-17)
- [A Developer’s Guide to Approximate Nearest Neighbor (ANN) Algorithms](https://www.pinecone.io/learn/a-developers-guide-to-ann-algorithms/md/) (2024-05-15)
- [Your Guide to Vectorizing Structured Text](https://www.pinecone.io/learn/structured-data/md/) (2024-03-26)
- [Advanced RAG Techniques](https://www.pinecone.io/learn/advanced-rag-techniques/md/) (2024-03-21)
- [Manage Serverless Costs with Read Units](https://www.pinecone.io/learn/read-units/md/) (2024-02-01)
- [OpenAI's Text Embeddings v3](https://www.pinecone.io/learn/openai-embeddings-v3/md/) (2024-01-25)
- [Test Pinecone Serverless at Scale with the AWS Reference Architecture](https://www.pinecone.io/learn/scaling-pinecone-serverless/md/) (2024-01-23)
- [Build a Wikipedia chatbot, minus hallucinations ](https://www.pinecone.io/learn/wikipedia-chatbot/md/) (2024-01-15)
- [Getting Started with Mixtral 8X7B](https://www.pinecone.io/learn/mixtral-8x7b/md/) (2023-12-20)
- [Exploring the Pinecone AWS Reference Architecture](https://www.pinecone.io/learn/aws-reference-architecture/md/) (2023-11-27)
- [OpenAI Assistants API vs Canopy: A Quick Comparison](https://www.pinecone.io/learn/assistants-api-canopy/md/) (2023-11-09)
- [Making it easier to maintain open-source projects with CodiumAI and Pinecone](https://www.pinecone.io/learn/codiumai-pinecone-similar-issues/md/) (2023-09-27)
- [Making Retrieval Augmented Generation Fast](https://www.pinecone.io/learn/fast-retrieval-augmented-generation/md/) (2023-09-13)
- [An (Opinionated) Checklist to Choose a Vector Database](https://www.pinecone.io/learn/an-opinionated-checklist-to-choose-a-vector-database/md/) (2023-09-13)
- [Falcon 180B: Model Overview](https://www.pinecone.io/learn/falcon-180b/md/) (2023-09-07)
- [LLMs Are Not All You Need](https://www.pinecone.io/learn/llm-ecosystem/md/) (2023-09-06)
- [Fine-Tuning OpenAI's GPT 3.5 Turbo](https://www.pinecone.io/learn/fine-tune-gpt-3.5/md/) (2023-08-28)
- [Deploying Open Source LLMs for RAG with SageMaker](https://www.pinecone.io/learn/sagemaker-rag/md/) (2023-08-23)
- [How to use Jupyter Notebooks for Machine Learning and AI Tasks](https://www.pinecone.io/learn/jupyter-notebooks/md/) (2023-08-23)
- [Options for Solving Hallucinations in Generative AI](https://www.pinecone.io/learn/options-for-solving-hallucinations-in-generative-ai/md/) (2023-08-21)
- [AI-powered and built with... JavaScript?](https://www.pinecone.io/learn/javascript-ai/md/) (2023-08-11)
- [NeMo Guardrails: The Missing Manual](https://www.pinecone.io/learn/nemo-guardrails-intro/md/) (2023-08-11)
- [Image Search in Typescript](https://www.pinecone.io/learn/image-search-in-typescript/md/) (2023-08-08)
- [Llama 2: AI Developers Handbook](https://www.pinecone.io/learn/llama-2/md/) (2023-07-24)
- [Retrieval Augmented Generation (RAG) with Pinecone and Vercel's AI SDK](https://www.pinecone.io/learn/context-aware-chatbot-with-vercel-ai-sdk/md/) (2023-07-19)
- [Understanding Hallucinations in AI: A Comprehensive Guide](https://www.pinecone.io/learn/ai-hallucinations/md/) (2023-07-13)
- [Audio Recommendation with OpenAI and Vector DBs](https://www.pinecone.io/learn/audio-recommendation-openai/md/) (2023-07-10)
- [Semantic search with Pinecone](https://www.pinecone.io/learn/search-with-pinecone/md/) (2023-06-30)
- [Embeddings to Identify Fake News](https://www.pinecone.io/learn/embeddings-identify-fake-news/md/) (2023-06-30)
- [Evaluation Measures in Information Retrieval](https://www.pinecone.io/learn/offline-evaluation/md/) (2023-06-30)
- [Softmax Activation Function: Everything You Need to Know](https://www.pinecone.io/learn/softmax-activation/md/) (2023-06-30)
- [Generative Question-Answering with Long-Term Memory](https://www.pinecone.io/learn/openai-gen-qa/md/) (2023-06-30)
- [What are Vector Embeddings](https://www.pinecone.io/learn/vector-embeddings/md/) (2023-06-30)
- [Getting Started with Hybrid Search](https://www.pinecone.io/learn/hybrid-search-intro/md/) (2023-06-30)
- [Cross-Entropy Loss: Make Predictions with Confidence](https://www.pinecone.io/learn/cross-entropy-loss/md/) (2023-06-30)
- [K-Nearest Neighbor (KNN) Explained](https://www.pinecone.io/learn/k-nearest-neighbor/md/) (2023-06-30)
- [Optimize Classifier Training with Vector Search](https://www.pinecone.io/learn/classifier-train-vector-search/md/) (2023-06-30)
- [How Language Embedding Models Will Change Financial Services](https://www.pinecone.io/learn/nlp-financial-services/md/) (2023-06-30)
- [Long Form Question Answering in Haystack](https://www.pinecone.io/learn/haystack-lfqa/md/) (2023-06-30)
- [Using Semantic Search to Find GIFs](https://www.pinecone.io/learn/gif-search/md/) (2023-06-30)
- [Time Series Analysis Through Vectorization](https://www.pinecone.io/learn/time-series-vectors/md/) (2023-06-30)
- [Introduction to K-Means Clustering](https://www.pinecone.io/learn/k-means-clustering/md/) (2023-06-30)
- [The Missing WHERE Clause in Vector Search](https://www.pinecone.io/learn/vector-search-filtering/md/) (2023-06-30)
- [Text-to-Image and Image-to-Image Search Using CLIP](https://www.pinecone.io/learn/clip-image-search/md/) (2023-06-30)
- [Hybrid Search and Learning-to-Rank with Metarank](https://www.pinecone.io/learn/metarank/md/) (2023-06-30)
- [Introduction to Transfer Learning](https://www.pinecone.io/learn/transfer-learning/md/) (2023-06-30)
- [Fixing YouTube Search with OpenAI's Whisper](https://www.pinecone.io/learn/openai-whisper/md/) (2023-06-30)
- [Vector Embeddings for Developers: The Basics](https://www.pinecone.io/learn/vector-embeddings-for-developers/md/) (2023-06-30)
- [Building a Image Recognition App in Javascript using Pinecone, Hugging Face, and Vercel](https://www.pinecone.io/learn/pinecone-vision-app/md/) (2023-06-30)
- [Transformers Are All You Need](https://www.pinecone.io/learn/transformers/md/) (2023-06-30)
- [Semantic Search: Measuring Meaning From Jaccard to Bert](https://www.pinecone.io/learn/semantic-search/md/) (2023-06-30)
- [Weight Initialization Techniques in Neural Networks](https://www.pinecone.io/learn/weight-initialization/md/) (2023-06-30)
- [How Machine Learning is Accelerating Life Sciences](https://www.pinecone.io/learn/ml-life-sciences/md/) (2023-06-30)
- [What is Similarity Search?](https://www.pinecone.io/learn/what-is-similarity-search/md/) (2023-06-30)
- [Regularization in Neural Networks](https://www.pinecone.io/learn/regularization-in-neural-networks/md/) (2023-06-30)
- [Making Stable Diffusion Faster with Intelligent Caching](https://www.pinecone.io/learn/faster-stable-diffusion/md/) (2023-06-30)
- [Streaming Embedding Generation with Databricks and Pinecone](https://www.pinecone.io/learn/databricks-streaming/md/) (2023-06-30)
- [Building a Multi-User Chatbot with Langchain and Pinecone in Next.JS](https://www.pinecone.io/learn/javascript-chatbot/md/) (2023-06-30)
- [Plagiarism Detection Using Transformers](https://www.pinecone.io/learn/plagiarism-detection/md/) (2023-06-30)
- [Making YouTube Search Better with NLP](https://www.pinecone.io/learn/youtube-search/md/) (2023-06-30)
- [Ludicrous BERT Search Speeds](https://www.pinecone.io/learn/bert-search-speed/md/) (2023-06-30)
- [Vector Similarity Explained](https://www.pinecone.io/learn/vector-similarity/md/) (2023-06-30)
- [Testing p2 Pods, Vertical Scaling, and Collections](https://www.pinecone.io/learn/testing-p2-collections-scaling/md/) (2023-06-30)
- [How to Explain ConvNet Predictions Using Class Activation Maps](https://www.pinecone.io/learn/class-activation-maps/md/) (2023-06-30)
- [SPLADE for Sparse Vector Search Explained](https://www.pinecone.io/learn/splade/md/) (2023-06-30)
- [Straightforward Guide to Dimensionality Reduction](https://www.pinecone.io/learn/dimensionality-reduction/md/) (2023-06-30)
- [Chatbots with Pinecone](https://www.pinecone.io/learn/chatbots-with-pinecone/md/) (2023-06-23)
- [Build Better Deep Learning Models with Batch and Layer Normalization](https://www.pinecone.io/learn/batch-layer-normalization/md/) (2023-06-23)
- [Advanced Topic Modeling with BERTopic](https://www.pinecone.io/learn/bertopic/md/) (2023-06-23)
- [What is Vector Search? 2024 Guide for Developers](https://www.pinecone.io/learn/vector-search-basics/md/) (2023-06-22)
- [What is a Vector Database & How Does it Work? Use Cases + Examples](https://www.pinecone.io/learn/vector-database/md/) (2023-05-03)

## Series

- [Beyond Retrieval](https://www.pinecone.io/learn/series/beyond-retrieval/md/)
- [Unlock Real-time Data for AI with Estuary Flow and Pinecone](https://www.pinecone.io/learn/series/unlock-real-time-data-for-ai-with-estuary-flow-and-pinecone/md/)
- [Vector Databases in Production for Busy Engineers](https://www.pinecone.io/learn/series/vector-databases-in-production-for-busy-engineers/md/)
- [Scaling AI Applications with Pinecone and Kubernetes](https://www.pinecone.io/learn/series/kubernetes/md/)
- [Retrieval Augmented Generation](https://www.pinecone.io/learn/series/rag/md/)
- [Data Sync and Search: Pinecone and Airbyte](https://www.pinecone.io/learn/series/airbyte/md/)
- [LangChain AI Handbook](https://www.pinecone.io/learn/series/langchain/md/)
- [Embedding Methods for Image Search](https://www.pinecone.io/learn/series/image-search/md/)
- [Faiss: The Missing Manual](https://www.pinecone.io/learn/series/faiss/md/)
- [Vector Search in the Wild](https://www.pinecone.io/learn/series/wild/md/)
- [Natural Language Processing for Semantic Search](https://www.pinecone.io/learn/series/nlp/md/)

## Blog

- [Behind the Benchmarking Pipeline ](https://www.pinecone.io/blog/behind-the-benchmarking-pipeline/md/) (2026-07-16)
- [Text match filters for agents](https://www.pinecone.io/blog/text-match-filters/md/) (2026-07-13)
- [Sparse V3: how Pinecone's sparse index learned to skip](https://www.pinecone.io/blog/sparse-v3/md/) (2026-07-09)
- [Pinecone Nexus Is Now in Public Preview](https://www.pinecone.io/blog/pinecone-nexus-public-preview/md/) (2026-07-01)
- [Generating Test Data for Pinecone](https://www.pinecone.io/blog/generating-test-data-for-pinecone/md/) (2026-06-29)
- [Full Observability for Pinecone: Introducing an Open-Source Monitoring Stack for SaaS and BYOC](https://www.pinecone.io/blog/open-source-monitoring-stack/md/) (2026-06-09)
- [Nexus in the Wild: Real Results from Our Early Access Customers](https://www.pinecone.io/blog/nexus-ea-benchmarks/md/) (2026-06-05)
- [Inside AskData: How We Slashed Token Consumption by Over 90%](https://www.pinecone.io/blog/inside-askdata/md/) (2026-06-02)
- [The Import Tax Is Gone](https://www.pinecone.io/blog/bulk-import-promo/md/) (2026-06-01)
- [Turn Azure Data into an AI-Ready Knowledge Base](https://www.pinecone.io/blog/turn-azure-data-into-an-ai-ready-knowledge-base/md/) (2026-05-27)
- [Full Text Search in Pinecone, Now in Public Preview](https://www.pinecone.io/blog/full-text-search/md/) (2026-05-07)
- [Full Text Search: Architecture and Design](https://www.pinecone.io/blog/full-text-search-architecture/md/) (2026-05-07)
- [Builder Plan: for the stage between prototype and scale](https://www.pinecone.io/blog/builder-plan/md/) (2026-05-06)
- [Introducing Pinecone Marketplace:  Getting to Production in Minutes](https://www.pinecone.io/blog/marketplace/md/) (2026-05-05)
- [Better Models Won’t Save Your Agent ](https://www.pinecone.io/blog/introducing-nexus-knowledge-engine/md/) (2026-05-04)
- [Pinecone Nexus: The Knowledge Engine for Agents](https://www.pinecone.io/blog/knowledge-infrastructure-for-agents/md/) (2026-05-04)
- [Four New GA Features for Dedicated Read Nodes That Give Teams More Control and Observability](https://www.pinecone.io/blog/dedicated-read-nodes-ga-features/md/) (2026-04-15)
- [Pinecone Dedicated Read Nodes: Now Generally Available](https://www.pinecone.io/blog/dedicated-read-nodes-ga/md/) (2026-04-15)
- [Load Balancing AI Services for Availability and Speed](https://www.pinecone.io/blog/load-balancing/md/) (2026-04-14)
- [Pinecone Assistant: A Managed Knowledge Layer for Production AI Applications](https://www.pinecone.io/blog/assistant-managed-knowledge-layer/md/) (2026-04-02)
- [Garbage Day: How Pinecone Safely Deletes Billions of Objects at Scale](https://www.pinecone.io/blog/janitor/md/) (2026-03-05)
- [When "Performance" Means Two Different Things](https://www.pinecone.io/blog/performance-as-a-measurement/md/) (2026-03-03)
- [Pinecone BYOC: Pinecone in your AWS, GCP, or Azure account, no vendor access](https://www.pinecone.io/blog/byoc/md/) (2026-02-19)
- [Use the Pinecone Plugin for Claude Code to develop AI Applications Faster](https://www.pinecone.io/blog/pinecone-plugin-for-claude-code/md/) (2026-02-11)
- [Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse](https://www.pinecone.io/blog/millions-at-stake-melange/md/) (2026-02-09)
- [Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge](https://www.pinecone.io/blog/pinecone-assistant-node/md/) (2026-01-28)

## Case Studies

- [Pinecone-Powered Knowledge Infrastructure Helps Jenova's Agent Platform Quickly Reach $1M ARR and 200,000+ Signups](https://www.pinecone.io/customers/jenova/md/) (2026-06-10)
- [ZoomInfo Delivers High-quality, Real-time Contact Recommendations for GTM Teams with Pinecone, Driving a 50% Increase in User Engagement](https://www.pinecone.io/customers/zoominfo/md/) (2026-04-15)
- [Allspice Transforms the Culinary Experience with Semantic Search Powered by Pinecone](https://www.pinecone.io/customers/allspice/md/) (2026-03-25)
- [Powering High-stakes Patent Search at Scale: How Melange Built a Reliable AI System on Pinecone](https://www.pinecone.io/customers/melange/md/) (2026-02-09)
- [Fast, Accurate Retrieval for Creators at Scale: Delphi’s Path Toward a Million Conversational Agents with Pinecone](https://www.pinecone.io/customers/delphi/md/) (2025-08-21)
- [Obviant Makes 30% More Accurate Defense Acquisition Recommendations Combining Sparse and Dense Retrieval with Pinecone](https://www.pinecone.io/customers/obviant/md/) (2025-06-24)
- [Terminal X AI Agents, Powered by Pinecone, Turn Complex Financial Data Into Production-grade Insights at Scale](https://www.pinecone.io/customers/terminal-x/md/) (2025-06-09)
- [Aquant Delivers Scalable, Expert-level Service Intelligence with Pinecone](https://www.pinecone.io/customers/aquant/md/) (2025-06-04)
- [Domain-specific AI Agents at Scale: CustomGPT.ai Serves 10,000+ Customers with Pinecone](https://www.pinecone.io/customers/customgpt-ai/md/) (2025-05-06)
- [How Vanguard Worked with Pinecone to Boost Customer Support with Faster Calls and 12% More Accurate Responses](https://www.pinecone.io/customers/vanguard/md/) (2025-03-25)
- [How 1up Turns Sales Reps Into Product Experts with Pinecone](https://www.pinecone.io/customers/1up/md/) (2025-03-06)
- [Stravito Turns Market and Consumer Data Into Actionable Insights with Pinecone Inference](https://www.pinecone.io/customers/stravito/md/) (2024-12-17)
- [Pinecone Helps Deep Talk Deliver World-Class AI Assistants with  Lower Engineering Overhead](https://www.pinecone.io/customers/deep-talk/md/) (2024-09-03)
- [Assembled Delivers Better, Faster AI- Driven Support with Pinecone](https://www.pinecone.io/customers/assembled/md/) (2024-09-03)
- [TaskUs Partners with Pinecone to Enhance Customer Service Satisfaction](https://www.pinecone.io/customers/taskus/md/) (2024-05-10)
- [InpharmD Redefines Evidence-Based Healthcare with Pinecone](https://www.pinecone.io/customers/inpharmd/md/) (2024-03-11)
- [DISCO Revolutionizes Legal Technology with Pinecone](https://www.pinecone.io/customers/disco/md/) (2024-01-22)
- [Revolutionizing Revenue Intelligence: Gong's Strategic Partnership with Pinecone](https://www.pinecone.io/customers/gong/md/) (2024-01-16)
- [Chipper Cash Thwarts Fraudsters in Real-time with Pinecone](https://www.pinecone.io/customers/chipper-cash/md/) (2023-04-12)

## Research Publications

- [Nearly Optimal Attention Coresets](https://www.pinecone.io/research/Nearly-Optimal-Attention-Coresets/md/) (2026-05-08)
- [Accurate and Efficient Metadata Filtering in Pinecone’s Serverless Vector Database](https://www.pinecone.io/research/accurate-and-efficient-metadata-filtering-in-pinecones-serverless-vector-database/md/) (2025-06-12)
- [Unveiling DIME: Reproducibility, Scalability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval](https://www.pinecone.io/research/unveiling-dime-reproducibility-scalability-and-formal-analysis-of-dimension-importance-estimation-for-dense-retrieval/md/) (2025-05-09)
- [Fast and Effective Early Termination for Simple Ranking Functions](https://www.pinecone.io/research/fast-and-effective-early-termination-for-simple-ranking-functions/md/) (2025-05-07)
- [A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings](https://www.pinecone.io/research/a-flexible-resource-for-top-weighted-comparisons-between-sets-and-rankings/md/) (2025-05-01)
- [E2Rank: Efficient and Effective Layer-wise Reranking](https://www.pinecone.io/research/e2rank-efficient-and-effective-layer-wise-reranking/md/) (2025-04-10)
- [ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring](https://www.pinecone.io/research/colbert-serve-efficient-multi-stage-memory-mapped-scoring/md/) (2025-04-07)
- [Efficient Constant-Space Multi-Vector Retrieval](https://www.pinecone.io/research/efficient-constant-space-multi-vector-retrieval/md/) (2025-04-07)
- [Natural Language Counterfactual Explanations for Graphs Using Large Language Models](https://www.pinecone.io/research/natural-language-counterfactual-explanations-for-graphs-using-large-language-models/md/) (2025-01-27)
- [Results of the Big ANN: NeurIPS'23 competition](https://www.pinecone.io/research/results-of-the-big-ann-neurips-23-competition/md/) (2024-09-25)
- [Bridging Dense and Sparse Maximum Inner Product Search](https://www.pinecone.io/research/bridging-dense-and-sparse-maximum-inner-product-search/md/) (2024-08-19)
- [Foundations of Vector Retrieval](https://www.pinecone.io/research/foundations-of-vector-retrieval/md/) (2024-06-15)
- [Rank-Biased Quality Measurement for Sets and Rankings](https://www.pinecone.io/research/rank-biased-quality-measurement-for-sets-and-rankings/md/) (2024-06-01)
- [DeeperImpact: Optimizing Sparse Learned Index Structures](https://www.pinecone.io/research/deeperimpact-optimizing-sparse-learned-index-structures/md/) (2024-05-27)
- [Optimistic Query Routing in Clustering-based Approximate Maximum Inner Product Search](https://www.pinecone.io/research/optimistic-query-routing-in-clustering-based-approximate-maximum-inner-product-search/md/) (2024-05-20)
- [Faster Learned Sparse Retrieval with Block-Max Pruning](https://www.pinecone.io/research/faster-learned-sparse-retrieval-with-block-max-pruning/md/) (2024-05-02)
- [Improved Learned Sparse Retrieval with Corpus-specific Vocabularies](https://www.pinecone.io/research/improved-learned-sparse-retrieval-with-corpus-specific-vocabularies/md/) (2024-03-23)
- [Efficient and Effective Tree-based and Neural Learning to Rank](https://www.pinecone.io/research/efficient-and-effective-tree-based-and-neural-learning-to-rank/md/) (2023-12-01)
- [Enhancing Sparse Retrieval via Unsupervised Learning](https://www.pinecone.io/research/enhancing-sparse-retrieval-via-unsupervised-learning/md/) (2023-11-26)
- [An Approximate Algorithm for Maximum Inner Product Search over Streaming Sparse Vectors](https://www.pinecone.io/research/an-approximate-algorithm-for-maximum-inner-product-search-over-streaming-sparse-vectors/md/) (2023-11-08)
- [An Analysis of Fusion Functions for Hybrid Retrieval](https://www.pinecone.io/research/an-analysis-of-fusion-functions-for-hybrid-retrieval/md/) (2023-08-18)
- [Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library](https://www.pinecone.io/research/yggdrasil-decision-forests-a-fast-and-extensible-decision-forests-library/md/) (2023-08-04)
- [Exact PPS Sampling w/ Bounded Sample Size](https://www.pinecone.io/research/exact-pps-sampling-w-bounded-sample-size/md/) (2023-05-01)
- [Lower Bounds for Pseudo-Deterministic Counting in a Stream](https://www.pinecone.io/research/lower-bounds-for-pseudo-deterministic-counting-in-a-stream/md/) (2023-03-23)
- [SDR: Efficient Neural Re-ranking using Succinct Document Representation](https://www.pinecone.io/research/sdr-efficient-neural-re-ranking-using-succinct-document-representation/md/) (2022-05-31)
- [Relative Error Streaming Quantiles](https://www.pinecone.io/research/relative-error-streaming-quantiles/md/) (2021-12-07)
- [Projective Clustering Product Quantization](https://www.pinecone.io/research/projective-clustering-product-quantization/md/) (2021-12-03)

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