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Pinecone

Pinecone Assistant: A Managed Knowledge Layer for Production AI Applications Multi-domain RAG in n8n: why one knowledge base is not enough Allspice Transforms the Culinary Experience with Semantic Search Powered by Pinecone | Pinecone Building RAG workflows in n8n: choosing the right Pinecone node Knowledge needs a meta-knowledge layer Garbage Day: How Pinecone Safely Deletes Billions of Objects at Scale When "Performance" Means Two Different Things Pinecone BYOC: Pinecone in your AWS, GCP, or Azure account, no vendor access True, Relevant, and Wrong: The Applicability Problem in RAG Use the Pinecone Plugin for Claude Code to develop AI Applications Faster Millions at Stake: How Melange's High-Recall Retrieval Prevents Litigation Collapse Powering High-stakes Patent Search at Scale: How Melange Built a Reliable AI System on Pinecone | Pinecone Pinecone Assistant Node in n8n: Turn Any Data Source Into Knowledge RAG with Access Control Pinecone Dedicated Read Nodes are now in Public Preview Inside Pinecone: Slab Architecture New Bulk Data Operations: Update, Delete, and Fetch by Metadata The Hidden Cost of Building: Lessons from Aquant Simplifying Vector Embeddings with Pinecone Integrated Inference Capabilities Pinecone joins Microsoft Marketplace as a Launch Partner GTM Engineering: Clay + Pinecone for AI-powered Sales Outbound Build an AI knowledge assistant with Google Docs and Pinecone Moving Pinecone forward with Ash Ashutosh as CEO and Edo spearheading our growing AI ambitions as Chief Scientist Pinecone Founder Edo Liberty to Spearhead Pinecone’s Growing AI Ambitions; Appoints Ash Ashutosh as CEO to Expand Vector Database Market Leadership Fast, Accurate Retrieval for Creators at Scale: Delphi’s Path Toward a Million Conversational Agents with Pinecone | Pinecone Announcing Pinecone Pioneers: A Program for Builders, Organizers, and Community Leaders What is Context Engineering? Chunking Strategies for LLM Applications Beyond the hype: Why RAG remains essential for modern AI Obviant Makes 30% More Accurate Defense Acquisition Recommendations Combining Sparse and Dense Retrieval with Pinecone | Pinecone Build more knowledgeable AI applications with new LLMs and greater control in Pinecone Assistant #NYTECHWEEK 2025 Retrieval-Augmented Generation (RAG) Accurate and Efficient Metadata Filtering in Pinecone’s Serverless Vector Database | Pinecone Terminal X AI Agents, Powered by Pinecone, Turn Complex Financial Data Into Production-grade Insights at Scale | Pinecone Aquant Delivers Scalable, Expert-level Service Intelligence with Pinecone | Pinecone Cascading retrieval with multi-vector representations: balancing efficiency and effectiveness Vector databases aren't just for large-scale enterprise AI Unveiling DIME: Reproducibility, Scalability, and Formal Analysis of Dimension Importance Estimation for Dense Retrieval | Pinecone Fast and Effective Early Termination for Simple Ranking Functions | Pinecone Domain-specific AI Agents at Scale: CustomGPT.ai Serves 10,000+ Customers with Pinecone | Pinecone Using Pinecone asynchronously with FastAPI A Flexible Resource for Top-Weighted Comparisons Between Sets and Rankings | Pinecone Build secure, scalable agentic AI workflows with Rubrik Annapurna and Pinecone Tool up: Pinecone’s first MCP servers are here Add context to your agent with Pinecone Assistant MCP remote server E2Rank: Efficient and Effective Layer-wise Reranking | Pinecone ColBERT-serve: Efficient Multi-Stage Memory-Mapped Scoring | Pinecone Efficient Constant-Space Multi-Vector Retrieval | Pinecone How Vanguard Worked with Pinecone to Boost Customer Support with Faster Calls and 12% More Accurate Responses | Pinecone Pinecone Named to Fast Company's Annual List of the World's Most Innovative Companies of 2025 Launch Week: Pinecone for agents, search, recommendations, and more Optimizing Pinecone for agents (and more) Retrieval Inference for scale and performance How 1up Turns Sales Reps Into Product Experts with Pinecone | Pinecone Don’t be dense: Launching sparse indexes in Pinecone Unlock High-Precision Keyword Search with pinecone-sparse-english-v0 Evolving Pinecone's architecture to meet the demands of Knowledgeable AI Pinpoint references faster with citation highlights in Pinecone Assistant Bringing the leading vector database to your cloud Getting started with llama-text-embed-v2 Natural Language Counterfactual Explanations for Graphs Using Large Language Models | Pinecone Easily build knowledgeable chat and agent-based applications in minutes with Pinecone Assistant, now generally available How to build an agentic, chat or RAG knowledge system using Pinecone Assistant Real-time RAG with Pinecone and Estuary Flow BigQuery to Pinecone in Real-Time with Estuary Flow Stravito Turns Market and Consumer Data Into Actionable Insights with Pinecone Inference | Pinecone Accelerate prototyping and development with Pinecone Local First-of-its-kind Pinecone Knowledge Platform to Power Best-in-class Retrieval for Customers Introducing integrated inference: Embed, rerank, and retrieve your data with a single API Strengthening security and increasing control with CMEK and API key roles Introducing Pinecone Rerank V0 Introducing cascading retrieval: Unifying dense and sparse with reranking From Idea to Action: How Pinecone Assistant Meaningfully Accelerates AI Business Building AI apps on Azure with Pinecone just got a lot easier Building a reliable, curated, and accurate RAG system with Cleanlab and Pinecone Four features of the Assistant API you aren't using - but should Deploying Pinecone with Infrastructure as Code (IaC) Streamlining CI/CD with Pinecone Local September 2024 Product Update Results of the Big ANN: NeurIPS'23 competition | Pinecone Introducing import from object storage for more efficient data transfer to Pinecone serverless Simplify, enhance, and evaluate RAG development with Pinecone Assistant, now in public preview Vectors and Graphs: Better Together August 2024 Product Update Pinecone Helps Deep Talk Deliver World-Class AI Assistants with Lower Engineering Overhead | Pinecone Assembled Delivers Better, Faster AI- Driven Support with Pinecone | Pinecone Llama 3.1 Agent using LangGraph and Ollama Build knowledgeable AI with Pinecone serverless, now generally available on Microsoft Azure Pinecone serverless is now generally available on Google Cloud, adding knowledge to AI assistants and other applications Accelerating Legal Discovery and Analysis with Pinecone and Voyage AI Bridging Dense and Sparse Maximum Inner Product Search | Pinecone Refine Retrieval Quality with Pinecone Rerank Introducing reranking to Pinecone Inference to simplify building accurate AI July 2024 Product Update Connect to Pinecone within your platform to enable a seamless AI development experience Introducing Pinecone API Versioning RAG Brag with Inkeep Co-Founder Nick Gomez LangGraph and Research Agents Introducing Pinecone Inference to streamline your AI workflow
Builder Plan: for the stage between prototype and scale
Harshita Daddala · 2026-05-06 · via Pinecone

I joined Pinecone six months ago to lead product-led growth. My focus: what it takes to go from building to scaling on the platform.

Talking to customers, I learned what they were building: legal research tools, medical search assistants, educational knowledge bases replacing scattered docs and whitepapers. Real products, with real users, doing real work.

What I heard consistently was that while the Starter tier is generous, free and capable enough to get a lot done, it started to feel tight past the prototype stage. The next available tier is Standard at $50/month, which is reasonable for teams already at scale, but hard to justify when your usage is still early and your workload generates nowhere near that every month. Having worked on developer tooling for close to a decade, much of it in open source, I recognized this friction immediately. Making powerful tooling accessible without surprising developers on cost was the job.

Builder is our attempt to fix that: a $20/month flat-rate plan designed for builders who've outgrown Starter, are ready to take their app to production, but haven't yet reached the scale that justifies Standard.


What Builder is designed for

The limits on Builder are based on what we know about how thousands of builders at this stage actually work.

  • 10 indexes keeps dev, staging, and production as separate environments without having to tear one down to spin up another.
  • 1,000 namespaces per index lets you give each user or tenant an isolated space, which is usually what takes a product from internal tool to something shippable.
  • 200 assistants per project supports document-heavy products where every user or use case gets its own context.
  • More storage and usage headroom to scale comfortably at this stage.

Builder also includes free support, which tends to matter more than you'd expect the moment your users are real.

One note on availability: Builder runs on aws-us-east-1 now. Multi-region and multi-cloud support across AWS, GCP, and Azure are coming soon.

For the full breakdown, see the pricing page.

Which tier fits your stage

Builder fills a specific gap. Pinecone now has four tiers, each sized for a distinct stage of building, and Builder slots in between Starter and Standard.

Starter is free, and it's not a demo. With enough storage, indexes, and usage to build something real and take it pretty far. Most people who stay on Starter eventually need to create a second project, add a teammate, or create more indexes as their architecture evolves. That's the natural point where Starter starts to feel tight.

Builder is for when those ceilings start to matter. When you want to add a teammate, spin up another project, or build out an architecture that needs more indexes. Maybe it's a side project that picked up users faster than you expected. Maybe it's a startup product six weeks from launch. Maybe it's an internal tool your team depends on. Builder is sized for that stage: more indexes, more projects, more teammates, and a flat monthly cost that fits where you are.

Standard is for teams with established workloads who want to pay proportionally to scale. When you're there, usage-based pricing is the right call — and Standard is built for it.

Enterprise is for organizations with more complex requirements — dedicated infrastructure, advanced security and compliance, SLAs, and support built around how larger teams operate. If you're evaluating Pinecone at that level, reach out directly.

Available today

Builder is available now. Anyone who upgrades to Builder before May 31st gets their first month free — no code needed, applied automatically when you upgrade.

I believe infrastructure pricing should match the stage of building, not just the scale of it. Builder is our version of that to enable actively building, shipping, growing.

We've sized the limits based on what we know, and we'll keep adjusting as we learn how teams use the plan. If something specific isn't working for you, tell us! You can reach us at our Discord channel. Feedback is how this gets better.