Kipps.AI is your all-in-one AI assistant for marketing, sales, and customer support. Build AI agents that capture leads, qualify prospects, answer customers, and automate workflows—without complex setup. Designed for small teams to save time, reduce manual work, and scale faster with smart, always-on AI.
Launch AI Voice, WhatsApp or Chat agents, and integrations in minutes—no heavy tech required. Scale faster, work smarter, and let AI handle the busywork while your team focuses on growth.
This is the 6th launch from Kipps AI. View more
Kipps.AI CostLens: Open Source
Launching today
When asked, “What will this AI solution actually cost to run?”, most teams don’t have a clear answer. This open-source AI Cost Calculator helps estimate the cost of chatbots, voice agents, and AI workflows before implementation. Model token usage, reasoning overhead, prompt caching, tool calls, conversation history, and compare pricing across models. Designed to help builders make better architecture, pricing, and unit economics decisions before scaling.
Free
Launch Team / Built With
Vapi
Kipps AI
Hi Product Hunt 👋
I’m Nishit, founder of Kipps AI, and excited to share CostLens with you today.
Why we built this:
The idea came from a problem we kept seeing in customer conversations. People could discuss prompts, models, and agents — but when asked, “What will this AI solution actually cost to run?” The room often went quiet. There wasn’t a simple way to estimate cost before building.
The problem:
AI costs are rarely just “input + output tokens.” Real costs can include conversation history growth, tool calling overhead, reasoning, prompt caching, and model choice. These hidden variables can change margins, pricing, and even whether a project is viable.
What CostLens does:
CostLens is an open-source AI Cost Calculator that helps estimate the approximate cost of chatbots, voice agents, and AI workflows before implementation. This project is in very early stages. Please show us the support 🙏, so that we can develop more around it.
It can help you model:
* LLM input/output costs
* Reasoning overhead
* Prompt caching effects
* Tool calling costs
* Conversation history impact
* Compare pricing across models
Benefits:
* Make better architecture decisions early
* Estimate unit economics before scaling
* Reduce surprises in production
* Compare tradeoffs across models and workflows
We’re launching this early because we’d love community feedback and contributions. If there are cost drivers we should include, I’d love to hear them.
Thanks to the Product Hunt community for checking it out, and thank you to everyone building in AI who shares feedback openly — it helps make tools like this better. 🙏
Would love your thoughts: What hidden AI cost has surprised you most in production?
Also, we might have missed some parameters. Please comment on the parameter.