🚀 RAG Battle: Gemini File Search vs. OpenAI Vector Store. Which one to choose?

🔥

Hello community! 👋

The AI automation landscape is moving at breakneck speed. One of the current keys is equipping our agents with specific knowledge (RAG) without dying trying.

Until recently, this required setting up complex pipelines with Pinecone, Supabase, etc. But recently, both 𝗚𝗼𝗼𝗴𝗹𝗲 (𝗚𝗲𝗺𝗶𝗻𝗶) and 𝗢𝗽𝗲𝗻𝗔𝗜 have launched solutions that promise to drastically simplify this process.

I've been analyzing both options thoroughly, comparing costs, ease of use, and integration (especially thinking about workflows like n8n), and here is the final verdict. 👇

🔥 𝗧𝗵𝗲 𝗠𝗶𝗹𝗹𝗶𝗼𝗻 𝗗𝗼𝗹𝗹𝗮𝗿 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻: W𝗵𝗶𝗰𝗵 𝗼𝗻𝗲 𝗶𝘀 𝗯𝗲𝘁𝘁𝗲𝗿?

The short answer is: It depends on your priority. Are you looking for maximum economy and speed, or the most elegant integration into your workflow?

🥇 𝗚𝗲𝗺𝗶𝗻𝗶 𝗙𝗶𝗹𝗲 𝗦𝗲𝗮𝗿𝗰𝗵 𝗔𝗣𝗜 (𝗧𝗵𝗲 𝗞𝗶𝗻𝗴 𝗼𝗳 𝗘𝗰𝗼𝗻𝗼𝗺𝘆 & 𝗦𝗶𝗺𝗽𝗹𝗶𝗰𝗶𝘁𝘆)

If your priority is low cost and getting it running fast, Gemini is unbeatable.

💸 𝗔𝗹𝗺𝗼𝘀𝘁 𝗭𝗲𝗿𝗼 𝗖𝗼𝘀𝘁: Storage is currently free. Indexing a 120-page PDF costs less than $0.15. It's ridiculously cheap compared to the competition.
🚀 𝗡𝗼 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲: Forget about managing external vector databases. Google takes care of chunking, embedding, and storage. Just upload the file and you're done.

🥈 𝗢𝗽𝗲𝗻𝗔𝗜 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗲 (𝗧𝗵𝗲 𝗞𝗶𝗻𝗴 𝗼𝗳 𝗡𝗮𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻)

If you value a clean workflow and integrated tools within the OpenAI ecosystem.

🧠 𝗡𝗮𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻: It shines especially if you use n8n. You access the vector base directly via the OpenAI API, without weird extra nodes.
🛠️ "𝗕𝘂𝗶𝗹𝘁-𝗶𝗻" 𝗧𝗼𝗼𝗹𝘀: It allows adding file or web search directly in the agent configuration, eliminating the need for external tools like Perplexity in many cases. It's a much more elegant design.

📊 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝘁𝗵𝗲 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 (𝗘𝘅𝗰𝗹𝘂𝘀𝗶𝘃𝗲 𝗜𝗻𝗳𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰𝘀)

I have prepared detailed infographics for the community that visually summarize:

The quick verdict.
The brutal difference in prices.
How they differ from traditional RAG and their limitations (watch out, they aren't magic!).

👇 Check out the attached images to get the full picture! 👇

🗣️ 𝗗𝗲𝗯𝗮𝘁𝗲: W𝗵𝗶𝗰𝗵 𝗼𝗻𝗲 𝗱𝗼 𝘆𝗼𝘂 𝗽𝗿𝗲𝗳𝗲𝗿?

Personally, I am using Gemini for projects with a large volume of data where cost is critical, but I prefer OpenAI's Vector Store for quick agents within n8n due to the cleanliness of the flow.

W𝗵𝗮𝘁 𝗮𝗯𝗼𝘂𝘁 𝘆𝗼𝘂? Have you tried these new "easy" systems yet? Or do you remain loyal to traditional RAG (Pinecone/Supabase) to have more control?

Let me know in the comments! 👇

3 comments