CapybaraDB Beta — Semantic search made easy
@new_user__2592022c1f0aa34ef1433a0 @CapybaraDB sounds like a game-changer for AI app development—combining MongoDB and Pinecone to simplify multi-modal data management is a huge time-saver. The EmbJSON feature is especially intriguing! how does it compare to traditional vector search methods in terms of speed and scalability?
@CapybaraDB @kui_jason
You'll directly benefit from MongoDB and Pinecone's industry-leading capabilities, including speed and scalability, as well as all the other async automation.
First off, love the name—Capybaras are the chillest animals, and if your DB is anything like them, I’m sold. 😂
Jokes aside, does EmbJSON really let you run semantic search on raw images without pre-processing? If so, that’s a game-changer. How does it compare to traditional vector databases in terms of speed?
@hussein_r Lol thanks, we wanted our database to embody that chill vibe! We use Pinecone for vector search, and we've built an optimized data aggregation pipeline that traditionally would run on the client or application side. This setup delivers a faster end-to-end response time compared to a conventional in-house backend pipeline.
@denisss thanks! If you have any questions or requests, please reach out to tomo@capybaradb.co. The great thing about using our early-stage product is that you can literally get involved and shape its future development.
CapybaraDB Beta is taking an interesting approach to simplifying semantic search implementation. Launched just about 3 weeks ago (first launch on January 25th, 2025), they're already showing strong traction with their second launch ranking #2 for the day and #22 for the week with 307 upvotes.
The core value proposition is compelling: they're abstracting away the complexity of managing AI-powered search by building on established technologies (MongoDB, Pinecone, AWS S3). This is particularly valuable for developers who want to implement semantic search without dealing with the intricacies of multiple services.
Key highlights:
Built on proven technologies rather than reinventing the wheel
Asynchronous data management automation
Free tier available
Focus on high-level abstraction for AI applications
The team (Tomo Kanazawa and Hardik) seems to be moving fast with iterations, as evidenced by this being their second launch in less than a month. For developers looking to implement semantic search without the overhead of managing multiple services, this could be a significant time-saver.
The combination of SaaS, AI, and Database tags positions them well at the intersection of several growing markets.
Congrats on launching CapybaraDB, Tomo! The blend of MongoDB and Pinecone, combined with EmbJSON, sounds like a game-changer for AI app development. Loving the seamless semantic search capability without the usual indexing hassle. Can't wait to see how this evolves!
Best wishes and sending wins to the team @new_user__2592022c1f0aa34ef1433a0
hey, so www.freaky-fonts.com works here, I recommend everyone to use these strange characters. They make the producthunt community seem more interesting.
Seems like a huge step forward for AI app developers! The multi-modal support and asynchronous embedding sound particularly handy.
@gemanor Yes! Making the developer experience better is our main focus :)
@alexanderwu The direct comparison between pure vector databases and CapybaraDB (muti-database + pipeline) isn’t as straightforward as a database-to-database comparison. Think of it this way: if you build your backend logic without CapybaraDB, you’d combine databases with your custom aggregation pipeline. But with CapybaraDB, you get Pinecone plus an optimized aggregation pipeline out of the box. By focusing our resources on building this efficient pipeline, we allow developers to skip that phase and concentrate on their core application logic. Also, CapybaraDB scales horizontally as your data grows.
This is super cool! Out of curiosity, what embedding model do you use in the demo?
@karthik_kandikonda We used the default mode, which is OpenAI's text-embedding-3-small, and we'll be adding more embedding models over time. If you need any assistance, please reach out to us; we can even provide customized sample code tailored to your specific needs.
Sounds great! Signed up but the web UI seems to be non-functional.
I got no project ID, create new collection gives an error - nothing else to do.
Will check out the CLI next time
@erik_edhagen1 Hi, thanks for letting us know about this. Could you email me the details at tomo@capybaradb.co? If you don't have time, just let us know the email address you used for the sign-up. We'll make sure everything works properly.
@emeka1 Use references for that. If you need further assistance, feel free to email me.
CapybaraDB Beta is taking an innovative approach to simplifying semantic search implementation. Since its initial launch on January 25th, 2025, the platform has gained impressive traction, with its second launch ranking #2 for the day and #22 for the week, earning 307 upvotes in just three weeks.
Its core value proposition is clear: eliminating the complexity of AI-powered search by leveraging established technologies like MongoDB, Pinecone, and AWS S3 instead of reinventing the wheel. This makes it particularly appealing for developers who want to integrate semantic search without managing multiple services.
Key highlights:
✅ Built on proven technologies for reliability and scalability
✅ Automated asynchronous data management for seamless performance
✅ Free tier available to encourage adoption
✅ High-level abstraction to simplify AI-powered applications
The team—Tomo Kanazawa and Hardik—is iterating rapidly, with two launches in less than a month. For developers seeking an efficient, hassle-free way to integrate semantic search, CapybaraDB could be a major time-saver.
With its SaaS, AI, and Database focus, the platform is positioned at the crossroads of several fast-growing markets, making it one to watch. 🚀
@jeremy_maisse I assume you're referring to the debate between open source and closed source. We are considering open-sourcing our project, but since it's an irreversible and resource-intensive decision, we want to proceed cautiously. Our primary focus is on the developer experience—if open-sourcing enhances that, we'll move forward; if not, we won't.
Hello, Product Hunt! I'm Tomo, and I'm the co-founder of CapybaraDB. I'm excited to share our product today!
🙋🏻What is CapybaraDB?
Built on Top of MongoDB and Pinecone: Leverages robust underlying technologies.
High-Level Data Management Abstraction: Simplifies complex data operations.
Multi-Modal Support: Natively handles text, images, videos, audio, websites, and more.
Robust Semantic Search Automation: Delivers precise, context-aware search capabilities.
Asynchronous Processing: Embedding processes run in the background so the client isn’t left waiting.
💻Introducing EmbJSON – CapybaraDB Extended JSON:
EmbJSON lets you perform semantic searches on ANY field in your JSON document without needing a semantic index. No embedding, chunking, or media-to-text processing is required.
🧑🏻💻Example EmbJSON Usage:
Simply wrapping the "pic" and "bio" fields makes them semantically searchable 🔥
Would love to have your feedback!
Happy building!