Search through billions of items for similar matches to any object, in milliseconds. It’s the next generation of search, an API call away.
Pinecone Serverless is praised for its robust performance, especially in scenarios requiring fast retrieval times and extensive scalability, such as managing AI agents. Users appreciate its integrations and extensions, like the Gemini CLI, which enhance functionality and ease of use. However, there are concerns about cost-effectiveness, with some users citing an issue with the expensive nature of large query processing compared to retrieval-augmented generation (RAG) techniques. Overall, Pinecone enjoys a positive reputation for its reliability and innovative enhancements in AI and retrieval systems, yet there's a desire for more competitive pricing for some high-resource tasks.
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3 positive
Pinecone Serverless is praised for its robust performance, especially in scenarios requiring fast retrieval times and extensive scalability, such as managing AI agents. Users appreciate its integrations and extensions, like the Gemini CLI, which enhance functionality and ease of use. However, there are concerns about cost-effectiveness, with some users citing an issue with the expensive nature of large query processing compared to retrieval-augmented generation (RAG) techniques. Overall, Pinecone enjoys a positive reputation for its reliability and innovative enhancements in AI and retrieval systems, yet there's a desire for more competitive pricing for some high-resource tasks.
Features
Use Cases
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: $20/month, $50/month, $50/month, $300, $500/month
Your AI isn't failing because of "bad chunking." 🛑 It’s failing because of a vibe mentality. Our Head of DevRel @RoieSchwabco explains why anecdotal testing is the #1 roadblock for AI devs: ✅ Dete
Your AI isn't failing because of "bad chunking." 🛑 It’s failing because of a vibe mentality. Our Head of DevRel @RoieSchwabco explains why anecdotal testing is the #1 roadblock for AI devs: ✅ Deterministic software has unit tests. 🧪 Non-deterministic LLMs need evaluations. "Most cases that hit roadblocks are those that didn't think about how they were going to know whether their system is operating well or not." Stop vibe-checking. Start instrumenting. 🛠️ Full @ai_rebels episode: https://t.co/N2DwgEsu9A
View originalPinecone now has a native extension for Gemini CLI. gemini extensions install https://t.co/6OknIfIvQb → 9 MCP tools → 7 skills (quickstart, query, assistant, RAG and more) → Vector search from your
Pinecone now has a native extension for Gemini CLI. gemini extensions install https://t.co/6OknIfIvQb → 9 MCP tools → 7 skills (quickstart, query, assistant, RAG and more) → Vector search from your terminal One command. That's it. https://t.co/sF9jFsgAAr
View originalStop 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 originalPricing found: $20/month, $50/month, $50/month, $300, $500/month
Key features include: Performant, Serverless, Reliable, Secure, Real-time indexing, Tiered storage, Fast accurate reads, Semantic search.
Pinecone Serverless is commonly used for: What teams build with Pinecone.
Pinecone Serverless integrates with: AWS Lambda, Google Cloud Functions, Azure Functions, Zapier, Slack, Salesforce, Tableau, Looker, Kubernetes, Apache Kafka.
Based on user reviews and social mentions, the most common pain points are: token usage.
Come build with Pinecone
Apr 3, 2026
Based on 58 social mentions analyzed, 5% of sentiment is positive, 91% neutral, and 3% negative.
