LiquidIndex 2.0 - The Stripe Checkout of RAG. Fast, scalable, effortless.
RAG made as easy as Stripe Checkout. Create a customer, connect data, and query. Fully multi-tenant, scalable, and effortless. No infrastructure headaches, just seamless retrieval-augmented generation in minutes. Launch your AI-powered app faster.
Replies
Hey Product Hunters! 👋
I’m Karthik, founder of LiquidIndex, a fully managed platform that makes integrating AI search as easy as Stripe Checkout.
Super excited to share this with you all today, and massive thanks to @thisiskp_ for hunting us! 🙏
LiquidIndex helps developers add RAG (retrieval-augmented generation) to their apps in just a few minutes—with no need to build ingestion pipelines, deal with vector DB configs, or glue together a dozen tools. It’s built to make retrieval feel like a native part of your app, not a backend burden.
Here’s what makes LiquidIndex different:
⚡ 3 API calls to production-ready AI search
Create a tenant, upload data, and query. That’s it.
🔁 Multi-tenant by design
Each customer’s data is isolated, indexed, and optimized for fast, scalable retrieval.
📥 Managed ingestion pipeline
We handle file parsing, chunking, metadata, and syncing—so you don’t have to.
🔌 Multiple data sources
Currently supports file uploads and Google Drive, with Notion, Dropbox, and S3 on the way.
⚙️ Customizable internals
Choose your vector DB, your embedding model, and more—no vendor lock-in.
📊 Query playground + clean developer dashboard
Play with your data, test your queries, and manage tenants with ease.
We’re currently in beta and onboarding teams!
You can try it now at liquidindex.dev
🎉 Launch Special:
Use code LQX410 to get the Pro Plan for $10 your first month (originally $25/month).
We’ve spent the last few months refining the core architecture, polishing the experience, and preparing for scale. Now, we’re excited to get it into more hands.
If you’re building AI apps and want a simpler way to handle search, I’d love to hear what you think. Feedback, questions, ideas—drop them below or DM me anytime. Let’s build better infra together. 🙌
Dola: AI Calendar Assistant
@thisiskp_ @karthik_kandikonda RAG is always helpful. Congrats on launching LiquidIndex!
@thisiskp_ @sophiaatdola Thank you!
AI Launchpad
Hey PH friends! 👋
I'm thrilled to introduce LiquidIndex to the Product Hunt community today! 🚀
Ever try adding AI search to your app and end up drowning in vector databases, embedding models, and ingestion pipelines? LiquidIndex is the lifeline you've been waiting for.
This is RAG for developers who want to build features, not infrastructure. With just 3 API calls, you can give your users the kind of intelligent search they expect in 2025.
What makes LiquidIndex special:
⚡ Production-ready AI search in minutes, not weeks
🔁 Multi-tenant architecture that scales with your user base
📥 Fully managed ingestion that just works (goodbye chunking nightmares!)
🧩 No vendor lock-in – customize what matters, forget the rest
I recently got a chance to see the demo and hear from LiquidIndex founder @karthik_kandikonda and was blown away by how seamlessly it handles the entire pipeline. The developer experience is refreshingly straightforward – it's like Stripe Checkout for AI search.
Whether you're building a knowledge base, document search, or customer support bot, LiquidIndex lets you focus on your product instead of wrangling vector databases.
Try it out and drop your thoughts below! LiquidIndex team is here today to answer questions and collect feedback. 💬
P.S. Don't miss their launch price of $10 for your first month (with the code LQX410) 🎉
@karthik_kandikonda @thisiskp_ Congrats on the launch, Karthik! 👏 I have tried wiring up RAG before, and it’s usually a maze of configs and tools. LiquidIndex really simplifies that. really like the idea of “3 API calls to production” super developer-friendly. good job
@thisiskp_ @hamza_afzal_butt Thank you! Really means a lot. Simplicity has always been the goal since I started this, I’m glad that came through
@karthik_kandikonda Huge congratulations on this launch! Making RAG this seamless and developer-friendly is a massive achievement. Excited to see how this powers the next wave of AI apps!
@smrati_tiwari4 Thank you so much! I’m excited to see what people build with it too!
EverTutor AI
@karthik_kandikonda A huge— Congratulations on your launch man! @thisiskp_ great hunting boss 💪
@thisiskp_ @suryansh_tiwari2 Thank you so much!
IndexFlow
Wow it's really helpful.
@saidevdhal Thank you! I'm glad you liked it!!!
Awesome launch, congrats! I may have overlooked on the site, does LiquidIndex offer API to manage a large volume of data? It seems more on a file-by-file basis.
@thefullstack Thank you so much! Just to clarify, do you mean being able to upload large volumes of data in 1 API call? Right now, uploading a lot of files has to be done through the session, or did you mean something else?
@karthik_kandikonda For context, I have roughly 6-digit number of entries where each is on average 500 characters. Looking for a RAG solution that can salably help us handle this.
@thefullstack I appreciate the context. To be honest, the system can scale to that level, but today it’s still early days and better suited for more incremental data volumes. That said, if the data is centralized (like in an S3 bucket or through structured ingestion), I could work with you on optimizing the pipeline to support larger-scale RAG ingestion at that size, but right now its just not there yet. Things are moving fast though, and it’s absolutely something I want to support fully in the near future. Happy to chat deeper if you'd like!
SimilarTube
Congrats on the launch, Karthik! Excited to see how LiquidIndex simplifies AI integration. Looking forward to trying it out!
@jun_zhao3 Thanks so much! Would love to hear your thoughts once you get a chance to play with it.
Loving this! Looking forward to seeing more apps starting to use RAG and this will definitely accelerate the process!!
@benjamin_astrand Thank you so much! Super excited to see what apps people build with this!
RAG integration made easy? Count me in! 😄
@shenjun Glad you liked it! I'd love to hear your thoughts on it!
Congrats on the launch!
I do have a few questiosn tho! it seems like this is a wrapper around pinecone?
what differentiates your application from just doing the set up in Pinecone?
Funny thing is I'm actually launching a similar product soon haha anyway always nice to meet the competition!
Anyway looks promising!
@andres_vlaeminck Hey! Appreciate you checking it out. I’m not totally sure I follow your question. The entire point of the product is so developers don’t have to set anything up in Pinecone (or worry about parsing files, chunking, embedding, etc.). LiquidIndex handles ingestion, chunking, storage, retrieval, and tenant data management — all behind the scenes — so devs can just plug it into their app and go. No pipeline building required.
Happy to go into more technical detail if you'd like!
@karthik_kandikonda Thank you for your reply;
I am pretty curious as to how the tech stack works? especially the chunking part as this is crucial to ensure good results when you do vector searches. (by the way small bug, i can't click the youtube video on your landing page 😁)
It's a great platform
Congrats on the launch
@urmi_hembram Thank you so much! Glad you liked it!
this tool is very useful to everyone and make work so well.
@daigo_umehara Thank you! Glad you liked it!
@karthik_kandikonda A huge— Congratulations on your launch man
@arpita_bag1 Thank you so much!
Congrats on launching LiquidIndex!
@tam_le_quang1 Thank you!
Congrats on the launch, looks like this is a competitive terrain idea. Here's your free idea report:
https://shouldibuild.it/idea/48d3e2e9381fe2b49d3ccb035eb853afe02d42476b363d6736bd25d378e9a675/
Wow—this makes RAG feel ridiculously simple! ⚡ Love the Stripe Checkout-style flow. Huge win for devs building AI apps fast and at scale!
@riya_patel12 That means a lot! Making RAG feel simple and fast is exactly what I set out to do. Glad the Stripe-style flow resonated!
LiquidIndex 2.0 appears to offer cutting-edge features and enhanced functionality compared to its predecessor. The upgraded platform likely provides more accurate data analysis, improved user experience, and potentially more robust trading capabilities. Users can probably expect better insights and decision-making tools to navigate complex markets.