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This is the 5th launch from Rippling. View more
AI Spend Console by Rippling
Launched this week
AI Spend Console gives Finance and Engineering leaders one place to track AI spend across tools (such as Claude and Cursor) and connect it to business outcomes. Break costs down by vendor, model, or employee, then connect spend to GitHub output data like pull request volume and the # of code revisions. You can get started for free–no Rippling subscription required.
Free Options
Launch Team
Rippling
Hey Product Hunt 👋 I'm Kevin, Product Lead behind Rippling AI Spend Console.
We built AI Spend Console because every company is adopting AI and costs are growing quickly. As a result, companies lack the infrastructure to track and manage AI spend as a new expense category. We faced this problem at Rippling.
In response, Finance teams manually pull data from vendor billing dashboards and run ad-hoc analyses just to get a point-in-time view of spend. And even then, you can see what you’re spending but not which teams, departments, or models drive the increase, and whether it’s improving business outcomes or employee productivity.
What AI Spend Console is:
AI Spend Console gives you a clear view of AI spend, connects it to business outcomes, and governs the use of approved LLMs. You can get started for free, no Rippling subscription required.
Unlike simple usage dashboards or point solutions, Rippling maps AI spend to employee attributes (such as department, team, and role) and GitHub output data (such as pull request volume and number of code revisions).
Rippling AI generates personalized dashboards using your connected data. You can even ask follow-up questions in natural language to drill down into spend and usage patterns. You can enforce policies on token spend and model access based on organizational data such as an employee’s department, team, or role*.
AI Spend Console can help you:
Know what's driving your AI bill. Break down spend by vendor, model, or individual employee in a single view.
Understand the value of spend. Map AI spend to business metrics like performance ratings or pull request volume, so you can flag inefficient use.
Control AI access and spend*. Enforce model access policies based on employee attributes, then automatically route AI requests to approved LLMs.
How it works:
Connect AI vendors (Anthropic, OpenAI Codex, Cursor), GitHub, and your employee data
Rippling AI builds you a custom dashboard based on your connected data
You can ask follow-up questions in natural language and share dashboards with anyone in your company
What we believe:
Your AI investment is like any other investment. You should only spend $$$ if it translates into actual ROI. You can’t understand that from a vendor billing dashboard.
You need to see all your AI spend in one place, tied to the teams, roles, and departments driving it, and connected to the business outcomes it's producing. Without that org context, spend is just a number. Rippling AI Spend Console gives you all three.
We'd love your feedback on:
Where the experience feels magical
What third-party integrations you'd like to see us add next
Try it for free with a 30-day Rippling AI trial at www.rippling.com/platform/ai/ai-spend-console. You don't have to be a Rippling customer.
If you’re interested in learning more about our experience tracking and controlling AI spend at Rippling, you can read more in this blog post: www.rippling.com/blog/introducing-ai-spend-console
Thanks for checking it out 🙏
-Kevin and the Rippling Team
*You can join the AI Gateway waitlist once you start a free trial in Rippling.
@kevinmason Congratulations on the launch!! What I'm most interested in is, how does Rippling AI seamlessly integrate with other AI vendors? Are there any limitations customers should be aware of? Thanks!
Rippling
@stephanie_mangos Hi Stephanie! We use third-party APIs to surface your historical data to you. Under the hood, we use incremental processing to handle partial progress, out-of-order events, and retries, while idempotent dashboard generation prevents duplicates. The initial import can take some time due to vendor rate limits. We'd love to have you try it out and let us know how it works for you!
Rippling
@stephanie_mangos @aliciawarrenhossein Hey Stephanie! The product provides some instructions on how to get connected to each of the AI products.
The biggest tip: make sure the person with admin privileges in the AI provider is the one that sets up the connector. Each connector requires creating API keys on the 3rd party's side, and then inputing that API key in Rippling to authenticate the credentials. After that, the historical data streams in and you'll be sent an email when it's ready!
Let us know how it goes!
I run a small engineering org and budget conversations with Finance usually devolve into guesswork because nobody has a shared source of truth. If this console becomes that shared reference point, it could genuinely change how I plan next year's tooling spend.
Rippling
@puja_sharma13 Definitely. Working from a shared source of truth is critical, especially when you're dealing with a fast-growing line item like AI spend. Once you have that visibility, you can make more informed vendor and budget decisions. I'd love to hear what you think of the product! You can also invite your Finance team to AI Spend Console so they can view shared dashboards too.
Rippling
@puja_sharma13 Hope this becomes the source of truth for your team! It's great for teams to use together since you and Finance are looking at the same permissioned dashboards instead of reconciling separate exports, which is usually where the guesswork creeps in to begin with. Please let us know how it goes!
Rippling
@puja_sharma13 This is exactly the type of use case we built it for! Bring all your data into one place, use our AI dashboard generation to quickly get up to speed and get a first sense of how much things cost, then refine the dashboards to get it ready to present & work with finance. Excited to hear how it goes!
Would love to know if it tracks spend trends over time, not just snapshots. I care more about trajectory than a single month's number.
Rippling
@robert_smith52 Yes, it tracks AI spend over time. It's not a static dashboard. One view I really like is "AI spend by vendor over time".
Rippling
@robert_smith52 Would love to hear more about what trends you're hoping to see over time. Keep us posted!
My CFO keeps asking for proof that my AI tools are worth the license fees. A dashboard that ties spend directly to code output gives me something concrete to bring to that conversation.
Rippling
@uttam_kumar35 Yes! You can look at tons of GitHub metrics like pull request volume, number of code revisions, code rework, etc. Let us know what you think!
Rippling
@uttam_kumar35 Hope this gives you exactly what you need for that conversation!
Rippling
@uttam_kumar35 We actually have a bunch of connectors. You could pull your data in from your CRM or ticketing system too -- check out the Connectors tab to see all the sources that you can connect to! That way you can expand the use case to more than just engineering output in Github
What stands out to me is the employee-level breakdown. I've always suspected some engineers get far more value from AI tools than others but I've never had data to actually confirm that suspicion.
Rippling
@soni_kumari25 Such a great point. I had that assumption too and we've learned a lot at Rippling about who's actually driving that spend. You can look at employees or teams, roles, departments, levels.
Rippling
@soni_kumari25 It's a good gut-check for assumptions, sometimes the loudest AI users aren't the ones actually driving the most value, and now there's a way to see!
Rippling
@soni_kumari25 We have learned so much about how AI is helping us ship products faster to customers.
But it's so much more than just "how many PRs or lines of code were sent?". A good question to dig into is how many PRs required multiple comments back and forth before getting approved (implying the code is just full of AI slop). That's where we really started to see a difference between engineers being more productive versus just tokenmaxxing.
The vendor-level breakdown caught my eye. I've got Claude, Cursor and two other tools all billing separately, and reconciling that manually is tedious.
Rippling
@ayesha_mughal1 Glad to hear you're interested! The vendor level breakdown gives you a good idea of what you're spending across all your vendors, then you can further break that down by which models, which teams, and which roles are driving spend. Excited to hear what you think!
Rippling
@ayesha_mughal1 Yes, that manual reconciliation pain is exactly what pushed us to build this in the first place. Hoping it gives you back some time for more useful work!
Rippling
@ayesha_mughal1 Would love to hear how the AI Spend Console works for you! Getting a sense of all the spend across disparate vendors is a pain. Classic data problem that usually ends up in spreadsheets. Would love to save you from some Excel hell :).
Breaking spend down by model, not just vendor, is underrated. Some models cost three times more for marginal quality gains and I need that visibility.
Rippling
@yolanda_c_schneider Couldn't agree more. The vendor level is good visibility but that's only the surface. Using AI Spend Console, our team has been able to answer questions like:
Which models are used most frequently and by which teams?
Which roles and levels are driving up our AI bill?
How does AI spend per pull request differ between our top and bottom performers?
Which engineers have high AI spend, whose peers frequently ask them to redo work in code reviews?
Rippling
@yolanda_c_schneider Building on that, the "marginal quality gains" point is a good one to flag internally too, once you can see model-level cost next to output quality, it becomes a lot easier to set defaults so people aren't reaching for the priciest model out of habit rather than actual need.