Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away.
The purpose-built vector database delivering relevant results at any scale. The world's most innovative companies are already in production with Pinecone. 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. Pinecone also supports hybrid search, 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. Fully managed and serverless for effortless scaling. Launch your vector databases in seconds. Resources adjust to meet your demand automatically. Trust in consistent uptime for your critical applications. Advanced retrieval capabilities for precise search across dynamic datasets. Choose from our leading hosted models or bring your own vectors. Retrieve only the vectors that match your metadata filters. Use Pinecone with your favorite cloud provider, data sources, models, frameworks, and more. Meet security and operational requirements to bring AI products to market faster. Powering mission-critical applications of all sizes, with uptime SLAs, support SLAs, and observability. Control your data and know it's safe. Pinecone is SOC 2, GDPR, ISO 27001, and HIPAA certified. Create your first index for free, then pay as you go when you're ready to scale. Pinecone runs on fully managed infrastructure that scales with you. Start building today with product and support plans tailored to your needs. For trying out and for small applications. For production applications at any scale. 3 week trial includes $300 credits Pay-as-you-go for Database On-Demand, Inference, and Assistant Usage For mission-critical production applications. For trying out and for small applications. For production applications at any scale. 3 week trial includes $300 credits Pay-as-you-go for Database On-Demand, Inference, and Assistant Usage For mission-critical production applications. Estimate your costs with our pricing calculator For organizations requiring the highest level of security and control The foundation for knowledgeable AI. Backed by distributed object storage for scalable, highly available serverless indexes. Dedicated Read Nodes. Exclusive infrastructure for queries, with provisioned nodes reserved for your index – no noisy neighbors, no shared queues, no read rate limits. Pinecone Assistant is a service that allow you to build production-grade chat and agent-based applications quickly. Pinecone Support provides various tiers of assistance to ensure your application's success, from community support to dedicated engineering resources. $50 monthly minimum that's applied to your usage. Anythi
Mentions (30d)
0
Reviews
0
Platforms
2
Sentiment
0%
0 positive
Features
Industry
information technology & services
Employees
170
Funding Stage
Series B
Total Funding
$138.0M
Scaling millions of AI agents = tough. @withdelphi chose Pinecone to power retrieval for their “Digital Minds” when open source vector stores couldn't cut it. With Pinecone, Delphi has: ⚡ <100ms P95
Scaling millions of AI agents = tough. @withdelphi chose Pinecone to power retrieval for their “Digital Minds” when open source vector stores couldn't cut it. With Pinecone, Delphi has: ⚡ <100ms P95 latency across 100M+ vectors 📈 Seamless scale to 12k+ namespaces 🛡️ Enterprise-grade security (SOC 2, isolation) Today, Pinecone powers every Digital Mind on Delphi’s platform, ensuring fast, accurate retrievals that account for <30% of end-to-end response time—keeping conversations natural and engaging. Read the case study 👇
View originalPricing found: $50/month, $50/month, $300, $500/month, $500/month
Stop trusting LLMs just because they're useful. 🛑 Our Head of DevRel @RoieSchwabco breaks down the two essential parts of a reliable AI stack: ⚙️ Reasoning: What the LLM does (logic/synthesis/forma
Stop trusting LLMs just because they're useful. 🛑 Our Head of DevRel @RoieSchwabco breaks down the two essential parts of a reliable AI stack: ⚙️ Reasoning: What the LLM does (logic/synthesis/formatting). 📚 Knowledge: What the Vector DB provides (traceable/authoritative facts). If you can't trace a claim back to the source, you aren't building a knowledgeable app -- you're just vibe-coding. "Knowledge is the thing that needs to be traceable, explainable, and authoritative." Ground your model's reasoning in a verifiable data layer. 🛠️ Full @ai_rebels episode: https://t.co/N2DwgEt1Z8
View originalAdd the Pinecone Assistant node to your n8n workflow: https://t.co/qhAwIM3eAk
Add the Pinecone Assistant node to your n8n workflow: https://t.co/qhAwIM3eAk
View original12 nodes. 3 API keys. Decisions you're probably second-guessing. That's what a typical RAG pipeline in n8n used to look like. With the new Pinecone Assistant node, your entire pipeline — chunking,
12 nodes. 3 API keys. Decisions you're probably second-guessing. That's what a typical RAG pipeline in n8n used to look like. With the new Pinecone Assistant node, your entire pipeline — chunking, embedding, search, reranking — is handled automatically. What used to take 12+ nodes now takes 3. This video from @cole_medin shows exactly how it works: upload docs with a Google Drive trigger (Slack, GitHub, or webhooks work too), query them through an AI Agent node, and get back grounded answers with cited sources. No embedding model to configure. No text splitter to fiddle with. No silent retrieval failures because you swapped models. Try out the new Pinecone Assistant node for @n8n_io (link in the replies)👇
View originalFull breakdown: https://t.co/ASMZBGdzE0
Full breakdown: https://t.co/ASMZBGdzE0
View originalImmutable storage keeps the write path clean. It also means old files never delete themselves. We built Janitor to fix that — identify, verify, execute. Three deletion modes, full auditability, and p
Immutable storage keeps the write path clean. It also means old files never delete themselves. We built Janitor to fix that — identify, verify, execute. Three deletion modes, full auditability, and property-based tests that compress 30-day windows into sub-second runs. https://t.co/t51lCcNHC1
View originalThe highest compliment for an AI stack? "Boring." 💤 Oded Sagie, SVP of Product and R&D at our customer @Aquant_ai explains why the @Azure + Pinecone combo is the foundation for their R&D mindset. "
The highest compliment for an AI stack? "Boring." 💤 Oded Sagie, SVP of Product and R&D at our customer @Aquant_ai explains why the @Azure + Pinecone combo is the foundation for their R&D mindset. "AI will create value when it will be boringly reliable." 🚀 Stop thinking about the infra. 🚀 Start thinking about the product. When your users stop asking how it works and just use it every day -- that's when you've won. 🏆 Full episode of the discussion between Oded, Pinecone Senior Director of Field Engineering, Perry Krug, and @Microsoft Generate Now! podcast host James Caton: https://t.co/w3ePuaXaJP.
View original@Krishna2008SR Nice work!
@Krishna2008SR Nice work!
View originalCome build with Pinecone https://t.co/8Gg5M9PGjc
Come build with Pinecone https://t.co/8Gg5M9PGjc
View originalDeterministic matching is where good recipe apps go to die. 🍳 Food tech platform AllSpice was stuck at ~20% accuracy trying to parse "large farm-fresh eggs, beaten" vs. "2 eggs" using traditional se
Deterministic matching is where good recipe apps go to die. 🍳 Food tech platform AllSpice was stuck at ~20% accuracy trying to parse "large farm-fresh eggs, beaten" vs. "2 eggs" using traditional search. The technical debt was literally blocking their product roadmap. The Fix: They ditched the keyword-matching headache for a Semantic Layer using Pinecone. The Dev ROI: ✅Infrastructure: Serverless + managed = pipeline validated in one afternoon. ✅Accuracy: Jumped from 20% to 97% by matching intent, not just strings. ✅Extensibility: That same vector index now powers fuzzy recipe search and chatbot normalization. Stop trying to Regex your way out of unstructured data. Build a semantic layer and ship in days, not months. 🚀 https://t.co/QMj4xN53ez
View original@jennapederson You can also watch over on: 📺 YouTube: https://t.co/pmirQDnuGx 💼 LinkedIn: https://t.co/c5MpRqvpZW
@jennapederson You can also watch over on: 📺 YouTube: https://t.co/pmirQDnuGx 💼 LinkedIn: https://t.co/c5MpRqvpZW
View original🔴 We're going live tomorrow for another Come build with Pinecone session! We'll center around building with Claude Code + Pinecone, but as with every technical conversation, we'll head off on some f
🔴 We're going live tomorrow for another Come build with Pinecone session! We'll center around building with Claude Code + Pinecone, but as with every technical conversation, we'll head off on some fun tangents (Gemini Embedding 2, robots, AR). Join @jennapederson, Arjun, and Roie to build, chat, and get your questions answered live. You can find us right here: 🗓️ Wednesday, March 25, 2026 ⏰ 10am PT / 1pm ET / 5pm GMT 🐦 X: @pinecone Drop your questions in the replies or bring them to the stream.
View originalStop trying to make your SQL DB do a vector DB's job. 🛑 Here's how it should work: 🔹 Pinecone (knowledge): Policies, context, and docs. The "Why." 🔹 SQL (transactions): Order history, IDs, and h
Stop trying to make your SQL DB do a vector DB's job. 🛑 Here's how it should work: 🔹 Pinecone (knowledge): Policies, context, and docs. The "Why." 🔹 SQL (transactions): Order history, IDs, and hard data. The "What." The agent is the orchestrator between the two. 🤖 "The vector database and the traditional database serve completely different purposes. They are complementary." Build the bridge, don't just flatten the stack. 🛠️ Watch the full DM Radio episode with our CEO @ashashutosh and host @eric_kavanagh here: https://t.co/dnulk9B6PY
View original🛑 Stop flattening your SQL tables into vector chunks. You’re killing your RAG accuracy by averaging too much info into a single embedding. Senior Developer Advocate Arjun Patel breaks down the hybri
🛑 Stop flattening your SQL tables into vector chunks. You’re killing your RAG accuracy by averaging too much info into a single embedding. Senior Developer Advocate Arjun Patel breaks down the hybrid architecture that actually scales: ✅ SQL: Keep your prices, specs, and structured metadata here. ✅ Pinecone: Put your reviews and unstructured text here. 🔗 The Link: Use a unique_id in your Pinecone metadata to map back to your SQL row. "Figure out the exact thing you want to help people find and build the search for that thing." 🎯 Check out the full conversation with @MikeBirdTech on @ToolUsePodcast that covers sparse/dense embeddings, reranking, Pinecone’s Claude Code plugin, and more: https://t.co/NKrpyOQo0f
View originalICYMI Pinecone BYOC is available in public preview. 🔒 Security review is where production AI goes to die. If “no vendor access” is the bar, managed services won’t pass. Self-hosting passes, but the
ICYMI Pinecone BYOC is available in public preview. 🔒 Security review is where production AI goes to die. If “no vendor access” is the bar, managed services won’t pass. Self-hosting passes, but the ops get dumped on you. 🚀 Pinecone BYOC runs Pinecone in your AWS, GCP, or Azure account with a zero-access operating model: ✓ Vectors + queries stay in your VPC ✓ Outbound-only, pull-based ops ✓ Auditable ops via k8s resources ✓ Public endpoint or private-only via AWS PrivateLink/GCP PSC/Azure Private Link ✓ Same managed Pinecone workflow you're used to
View originalPricing found: $50/month, $50/month, $300, $500/month, $500/month
Key features include: Performant, Serverless, Reliable, Secure, Real-time indexing, Tiered storage, Fast accurate reads, 99.95% uptime SLA.
Based on user reviews and social mentions, the most common pain points are: token usage.
Based on 55 social mentions analyzed, 0% of sentiment is positive, 100% neutral, and 0% negative.
