AI Spend Console by Rippling - Track your AI spend and connect it to business outcomes
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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.
Replies
This feels built by people who've actually sat in a budget review meeting and gotten stuck justifying tool costs.
Rippling
@kimberly_west Thanks for the support! I'd love to hear what you think of the product. Hope you can use this in your next budget meeting.
Rippling
@kimberly_west Budget meeting jitters!! 😬 Hope this makes your next one easier.
No subscription required to start is a smart call. I'd have skipped this entirely if it demanded a Rippling account upfront.
Rippling
@balakrisnan_h Let us know what you think when you try it out! We're very open to feedback and happy to answer any questions.
I've been tracking AI tool costs manually in spreadsheets for months. Connecting spend to actual PR output is exactly the missing piece I needed.
Rippling
@ayla_reynolds Glad to hear it! Would love for you to try out AI Spend Console and let us know what you think. Once you connect your data, I would recommend trying out a prompt like this: Build a dashboard that catches low-quality AI output before it ships. Combine GitHub data with AI usage to surface the pull requests, developers, teams, and repos where AI-assisted code shows high rework, reverts, review churn, and reopened bugs, so I can spot AI slop early.
Rippling
@ayla_reynolds That's great! Would love to hear how it goes once you get it connected.
Its interesting bcz managing HR, payroll and IT in separate systems can get messy quickly. What was the biggest challenge you wanted to solve when building this platform?
Congrats @kevinmason and team!
Rippling
@kevinmason @hamza_afzal_butt The biggest challenge was ensuring users have a magical onboarding experience and can get value from the tool. For example, we spent a lot of time testing and tuning our recommended prompts so the platform surfaces interesting AI spend and usage patterns. For example, you can see which engineers spend the most on AI but have the highest number of code revisions.
Rippling
@hamza_afzal_butt "Managing HR, payroll, and IT in separate systems can get messy quickly" is basically the Rippling mantra 🙂 The goal is always giving people tools to spend less time on manual reconciliation and more time on hard problems. AI Spend Console extends that same idea, giving you one view of your costs and a clearer path to making that spend more efficient.
I like that this doesn't require a Rippling subscription to try. Too many finance tools lock the useful features behind a bigger platform commitment before you even know if it fits.
Rippling
@ramish_saje We'd love for you to try it out and share your feedback!
@kevinmason We've gotten good at buying AI. We're still figuring out how to manage it. Timely launch.
Rippling
@joseph_walker2 The journey is new for all of us -- first tokenmaxxing to boost AI adoption, then shifting to analyze how much AI is really costing us after blowing over budgets, to now finding new tools to control AI costs.
It's a delicate balance -- what's enough AI spend so people can be wildly productive, but not so much that people are using it to generate expensive daily briefs that summarize their slack messages every morning running on Opus 4.7?
I'm excited to hear what you think of the AI Spend Console!
Rippling
@joseph_walker2 @kevinmason Thanks for the support, Joseph! Excited to hear what you think.
Cost visibility is great, but connecting it to GitHub activity is what really stands out. Nice approach to measuring AI ROI.
Rippling
@noahanderson Thanks! That was the core insight for us: spend alone doesn't tell you much, but when you tie it to GitHub data like PR volume, cost per PR, and code rework rate, you can actually start to see where AI usage is translating into output versus just adding cost. Would love to hear what you think once you dig into the dashboards!
Rippling
@noahanderson Thanks! Every leader is asking "What's the ROI on my AI spend?" and connecting it to GitHub metrics is just one way to see that. Of course, there's nuance but it helps you see where AI spend translates into improved business outcomes, especially if you have a heavy eng org.
@rachel_cantor3 What made you focus on AI spend visibility instead of just usage tracking?
Rippling
@landon_matthew AI Spend Console really focuses on both! You can see metrics like daily active users but also AI spend across vendors, teams, roles. Spend and usage are both critical, so we show you both.
This is one of my favorite Rippling AI prompts I'd recommend trying: Build a dashboard that shows AI usage and spend over the last 90 days by team, manager, provider, and model. Include total spend, active users, adoption rate, requests/tokens, and spend per employee.
@rachel_cantor3 Glad to know that!
Rippling
@rachel_cantor3 @landon_matthew
We were finding that usage tracking tells you what happened. It doesn't tell you what you can do about it.
We kept seeing the same thing: a team gets told to cut AI costs, so they open a usage dashboard, and... then they're stuck Knowing how many tokens or calls you used doesn't tell you if that spend actually makes sense. What people kept telling us is they wanted to see costs by model and by use case, so they could actually tell if they were using a more expensive model than they needed. That's really it: seeing the number isn't useful on its own. You need to know what to do about it.
@rachel_cantor3 @aliciawarrenhossein Absolutely amazing. I hope you guys implement it in the same direction.
Which dashboard insight surprised your own team the most?
Rippling
@alice_hayes2 For me, it was interesting to see which teams and roles are using which models. For example, some teams and roles were using expensive models and they really don't need to for their use cases. This has helped us better control spend. I'll let the other makers chime in too!
Rippling
@alice_hayes2 Building on that, another common surprise is just how concentrated spend tends to be, often within a specific team, or even with a handful of team members. Once you can see that, it's a lot easier to build a targeted efficiency plan instead of guessing at the org level.
@lkroll Nice one!
@rachel_cantor3 Glad to know that.
Rippling
@alice_hayes2
Two things stood out to me: On my own usage, I could see that I don't open up new chats very often, I just keep working in the same conversation. The dashboard showed me that a few of those conversations were burning through context just because I didn't restart them.
On the team side, it flagged a couple of people weren't using the models at all. The dashboard actually highlighted that this wasn't a problem with cost, but rather an enablement one. Turned out to be an onboarding gap we hadn't caught, and we were able to go fix it directly.
Serand
Rippling
@rukhsar_amjad For companies who are already Rippling users, their org data already lives in Rippling. If you're new to Rippling, you'll need to bring in that data (employees, teams, departments) via CSV. Once you do that, Rippling will do all the data mapping.