Pinecone

powering our vector search and making real personalization actually work.

From prototypes to prod, Pinecone is the gold standard for semantic search.

Pinecone helped our agents access data via a knowledge base and perform vector searches.

Shoutout to Pinecone for powering lightning-fast vector search in our production AI systems. Their managed infrastructure lets us focus on building great AI experiences instead of managing database complexity—exactly what enterprise clients need.

Powered our AI brain 🧠 Pinecone gives Pocket Squats the memory and context it needs to learn from every workout, adapt in real time, and make each user’s training experience smarter and more personal.

We’re excited to announce Cognitia AI is launching on Product Hunt September 29!

Big shoutout to Pinecone! Your technology has powered our journey from concept to reality. Cognitia AI automates workflows and boosts productivity across email, calendar, and more—all made possible with these incredible platforms!

Support us on launch day, and comment for early access! 💡

#ProductHunt #CognitiaAI #OpenAI #Vercel #Pinecone #Launch

Sydo ingests enterprise docs and turns them into a conversational knowledge layer inside Slack/Teams. For that to work, we need lightning-fast, reliable vector search that scales with messy, real-world data.

I chose Pinecone because:

⚡ Low latency: Enterprise users can’t wait 5 seconds for an answer — Pinecone keeps responses instant.

🔍 Semantic accuracy: Dense retrieval from Pinecone makes the bot feel “smart” instead of keyword-based.

📈 Scalability: From a few hundred docs to thousands across departments, Pinecone scales without me babysitting infra.

🔒 Data isolation: Each enterprise’s vectors stay logically separate, a big plus for trust and compliance.

⏱️ Focus: Instead of building/maintaining my own ANN infra, I can spend time on product + user experience, not ops.

👉 In short: Sydo’s promise of “knowledge at your fingertips” wouldn’t be possible without Pinecone powering the retrieval layer.

Made it super easy and fast to set-up a semantic search! By far the easiest way to implement compared to algolia or cohere

As a vector database, Pinecone is fast, reliable, and crucial for our AI-powered search and retrieval workflows.

Pinecone has been instrumental in powering Spark’s AI Whatsapp assistant with fast and accurate search. Unlike other vector databases we tested, Pinecone offered consistent low-latency retrieval and simple scalability. This made it the best choice for building a knowledgeable, responsive assistant for our beauty and personal care platform

We gave Pinecone 4 stars because while it’s incredibly fast and handles vector search at scale really well, the overall experience isn’t as smooth as we expected. The interface feels a bit raw, and working with sparse vectors or hybrid search still feels early-stage and under-documented. Setting up and tuning search isn’t as intuitive as we’d hoped — it requires more trial and error than expected. Pinecone is powerful, no doubt, but we thought it would be easier to work with out of the box

Pinecone is well-designed, developer-friendly, and offers a solid free tier. It also provides access to high-quality multilingual embedding models, making it easy to build semantic search and RAG systems without managing separate infrastructure.

Huge shoutout to the Pinecone team — making vector search fast, scalable, and dead simple. We used Pinecone to give our AI employees memory — so they don’t just respond, they remember. If you're building anything that needs long-term knowledge, this is your go-to. 🙌