Category: New Investment

Billing infrastructure has been a vexing issue for generations of software and Internet companies.  It is mission critical infrastructure and as such, feels like a problem that should have been sold a long time ago. Yet ask any company and you’ll generally get the same reply: they’re dissatisfied with their billing system, which doesn’t offer the level of flexibility to address their specific needs and the many edge cases that inevitably pop up.

The problem is only getting worse as the software industry transitions from subscription-based to consumption-based revenue models. What started as a trickle is becoming mainstream, as usage based pricing unquestionably builds better alignment with customers. This transition will only be accelerating, as an entire generation of new AI companies coming online that are almost all using that pricing model.

Today, we’re excited to announced our Series A investment in Lago, the leading open source metering and usage-based billing company. Lago offers both a self-hosted and cloud, scalable and modular architecture, and found strong product market fit both as an open source project and a commercial product.

Our investment thesis is pretty simple:

• Usage-based native: Lago is natively focused on the specific problem of usage-based billing and as such, incredibly well positioned to be the default solution for a whole new generation of companies, including in particular AI companies – it is not accident that customers already include AI unicorns like Mistral and Together AI
• Open source: other than the fact that we love open source infra in general (as evidenced by our investments in Cockroach Labs, ClickHouse, Astronomer, SurrealDB, Quickwit, etc), open source is a particularly formidable advantage for the billing space in general, as it enables a level of extensibility and composability that is uniquely suited to edge cases. Lago is a natively open source company with strong OSS traction and a vibrant community
• Europe/US: Lago is one of those bi-continental companies we love at FirstMark, with product and tech based in the vibrant Paris tech ecosystem and GTM, over time, in the US. Particularly as its ambitions go beyond “just” billing, Lago will be building a tremendous amount of product over the next few years, and will be well served by the relative cost advantage that a European location offers.
• Team: Needless to say, first and foremost we love the Lago team, led by co-founders Anh-Tho and Raffi, both incredibly thoughtful and gritty. The Lago team comes from a place of deep industry knowledge, having built the entire billing infrastructure at fintech unicorn Qonto.
• Bonus: getting to hear my colleague Aman repeat “open source metering and usage-based billing”, an area he’s particularly passionate about, thousands of times with equal enthusiasm

We very much look forward to working with Lago, and we’re excited to join a great group of prior investors including our friends at New Wave, SignalFire and some of the “French AI mafia” like Clement Delangue from Hugging Face and Romain Huet from OpenAI.

Of course, Lago is hiring.

If you scroll through the list of biggest companies in the world by market capitalization, it will come as no surprise that, in the now tech-dominated ranking, commodities companies continue to be heavily represented, with many familiar names such as Saudi Aramco, Exxon Mobil, Chevron, Shell, Petrochina or Total.

Those companies are not just large, they are also cash-generating machines: 11 of the 20 most profitable companies in the world are commodities companies.

Of course, the world of commodities (energy resources, metals, agricultural goods, etc) also manifests through commodities trading – the exchange of different assets, typically futures contracts, where investors make bets on the expected future value of a given commodity, whether for economic or speculative reasons. Alongside equities and fixed income, commodities is one of the key asset classes in financial markets.

At FirstMark, we believe that every company is going to become not just a software company, but a data company.

For that to happen, it is essential that technologies that leverage data be democratized. For the foreseeable future, the global demand for digital innovation will continue to vastly outweigh the number of developers, engineers and scientists. Therefore, some of the technical complexity must be abstracted away to enable a broader group of people to build data-driven products and companies.

Billions of dollars have been invested in the rise of data science and machine learning as mainstream disciplines in the world of business, one of the most exciting tech trends of the last (and next) decade.

In the enterprise, many of the applications of data science and machine learning ultimately produce a prediction: which customers are the most likely to buy? Or churn? Which transactions are most likely to be fraudulent? What part of town is likely to place the most food deliveries tomorrow afternoon?

However, powerful though it may be, there is one thing machine learning generally doesn’t tell you: once you have a prediction, what do you do with it? For example, once you have predicted high demand for food delivery in a certain part of town, how do you decide which delivery team member to dispatch where and when, to optimize for efficiency and maximize revenue and customer satisfaction?

Enter decision science. While the term has not crossed over to mainstream consciousness like its data science cousin, decision science has been around for decades. Also often known as Operations Research, it encompasses a variety of advanced analytical methods and quantitative models to help with decision-making and efficiency, including simulation, mathematical optimization, queuing theory, etc.  

Business planning is, of course, one of the vital functions in the enterprise: hard to run a successful company beyond a certain size without a clear sense for objectives and resources.

Yet, to this day, business planning is a often a cumbersome, rigid and time-intensive process. Typically led by the finance team, it is largely done through email, excel spreadsheets and meetings. In large companies involving multiple business units and geographies, the process can take several months. As a result, business planning tends to effectively happen once a year.

Great power, great responsibility: as the data revolution continues, and companies big and small are increasingly able to collect and analyze massive amounts of data, they are facing rapidly mounting pressure to become much better data stewards. Security, privacy and compliance were largely an afterthought, but there are becoming crucial topics. Regulation is rapidly spreading around the world – witness for example GDPR, CCPA and the even more ambitious New York privacy bill.

As abundantly evidenced in the news, corporations are woefully unprepared to handle the challenge of identifying, managing and securing sensitive data in its various forms — PII, PHI, attorney privileged information, etc. The problem keeps getting worse as the amount of unstructured data keeps increasing in the enterprise. Too often, corporations tend to deal with it after the fact, once a crisis has occurred, in the context of litigation. Only a very small minority is able to address those issues proactively.