Noticed for “General” the following: “General discussions about search, models, data, use cases, and anything else related. Share your projects, tutorials, ideas, and questions with the community.”
So I thought I’d share my project here!
Anyone else heavily using Pinecone for vector search in their AI apps, with other AI services as well, and finding the aggregate adds a significant, sometimes unpredictable, cost layer on top of everything else? You have your LLM expenses (OpenAI, Claude, Gemini), maybe workflow tools, Pinecone’s usage-based pricing for indexing and querying, etc.
I found myself digging through multiple billing details and trying to manually correlate them with other AI services’ invoices – not fun, and extremely easy to miss things.
This complexity and lack of a single pane of glass pushed me to build AIBillingDashboard.
So how does it help? I, and others, noticed the following so far:
- Unified Cost Dashboard: Upload usage logs alongside OpenAI, Claude, Gemini, etc., to see your entire AI stack’s spending in one place.
- Service-Specific Breakdown: Clearly see how much you’re spending on Pinecone versus your LLMs and other tools. Is your vector search or your generation costing more?
- Usage Analysis: Track Pinecone usage (e.g., vector counts, query volume) alongside LLM token counts to understand cost drivers in your RAG pipelines or search applications.
- Budget Monitoring & Alerts: Keep track of your Pinecone billing cycle and overall AI spend, getting alerts before payments are due.
It’s designed to bring clarity to the combined costs of building sophisticated AI applications that rely on components like Pinecone and even though it’s really an MVP for now I think it can help a lot of people besides myself and some friends.
Would love to hear from others using Pinecone extensively. How are you currently tracking its cost in relation to your other AI service expenses? What are the biggest hurdles?