How many AI subscriptions does a business really need? It's an easy question to ask, but I suspect quite a few companies would struggle to give a straight answer.
Someone signs up for ChatGPT, another department starts using a different AI tool, then somebody finds a new platform that promises to save everyone a few hours a week, and before long, the business is paying for a growing collection of subscriptions, where nobody has a particularly clear picture of which ones are actually making a difference.
That's not necessarily a problem when the costs are small, but AI is becoming a much bigger part of business spending, and eventually somebody has to look at the figures and ask whether all this money is delivering anything worthwhile.
That's where Larridin comes in - the San Francisco-based startup has just raised $30 million in Series A funding, with the investment led by Pelion Ventures and participation from Andreessen Horowitz, Gradient Ventures and several other investors, where the latest round takes its total funding to $47 million.
It's a pretty sizeable investment for a company whose main selling point is helping other businesses work out whether their AI spending is producing anything useful - The company's funding announcement comes at a time when AI costs are becoming a much bigger consideration for businesses, where according to Business Insider's coverage of the funding, AI has gone from being a relatively minor expense for some companies to one of their five to ten biggest costs.
What does Larridin actually do?
The basic idea is pretty straightforward here - companies are using all sorts of AI tools across different departments, but keeping track of what they're spending and what they're getting back isn't always easy as mentioned, as one team might be using several AI applications, another could be paying for expensive models, and individual employees might be using the same tools in completely different ways.
The finance department then sees the bills, but what it does not tell them is whether the tools are helping people get more work done - Larridin's platform connects AI usage and spending data with the work being produced, where it aims to show businesses which tools are being used, how much they're costing, where productivity is improving and where there might be opportunities to automate more work.
The company says it has measured more than 2 billion AI sessions and tracked over 82,000 workflows, and also claims its customers have seen an average 47% increase in measurable team output, alongside a median $2.4 million in AI spending optimised during the first year.
Those are Larridin's own figures, so I'd treat them as company-reported results rather than proof that every business can expect similar savings, but still, the examples it has shared are interesting I think - in one case, Larridin says 92% of engineers at a customer were actively using AI, but one engineer accounted for roughly 65% of the team's AI spending in a single week.
There's a business opportunity here too
For anyone building a SaaS company or looking for ideas for a new software product, this is the bit I'd be paying attention to - AI has created a huge market for new tools, but it's also creating problems that other software companies can build businesses around.
Managing subscriptions, monitoring usage, controlling costs, checking security and measuring results are all becoming more important as companies add AI to their everyday operations, so Larridin is targeting larger businesses, where its platform isn't necessarily something a small online business or solo founder would need, but the underlying problem isn't limited to big companies.
Even a small business can end up paying for ChatGPT, an AI writing tool, an image generator, automation software, coding tools and a handful of other subscriptions, and individually, those payments might not seem like much, but add them together over a year and it's a different story.
And how many people actually go back through their subscriptions to see which tools are still earning their place, so there's also a wider opportunity for smaller SaaS developers here.
AI tools were initially sold on what they could do, and increasingly, businesses are going to want to know what they're actually delivering, which is more of an interesting problem that software founders are going to want to solve.
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