DevTools Brew #10: Datadog: Lessons From the Journey to >1$B ARR, Interviewing Engineering Executives, The Best Way of Learning (By the Creator of Angular)...
Welcome to the DevTools Brew #10!
My name is Morgan Perry, co-founder of Qovery, and this is DevTools Brew newsletter, a weekly roundup of the latest trends and insights in the infrastructure and devtools world.
In this Issue #10:
💸 Latest Devtools Funding Rounds
📈 Datadog: Lessons From the Journey to >1$B ARR
💻 Interviewing Engineering Executives
⭐ Star History Weekly Pick
🎬 The Best Way of Learning According to The Creator of Angular
I hope you will enjoy this sneak peek.
Let's dive in!
💸 Latest Funding Rounds
A relatively quiet week in the devtools and infrastructure space, but here are the two notable fundraising rounds:
Antimetal is putting AI to work to root out cloud cost inefficiencies. The company announced a $4.3M seed investment.
Qarbon Technologies announced close of $5.5M seed funding round to launch of world's first SaaS-based orchestration platform for seamless integration of data center infrastructure
📈 Datadog: Lessons From the Journey to >1$B ARR
Disclaimer: Datadog is one of our proud investors at Qovery, so I'm even more excited to share these insights 😁
Datadog has a phenomenal track record:
Growing at >60% per year, more than twice the growth rate of its peers
Spending more on product development than sales & marketing?
Having more than 7x’d its enterprise value since IPO.
What is Datadog’s product strategy? What lessons can product leaders take from it? Enter this piece
Key Takeaways:
Lessons 1: The Problems of Big Trends Make for Great Products
The founding lesson of Datadog is that a big problem caused as a result of major trends is a potentially great product.
Datadog was founded by Olivier Pomel and Alexis Le-Quoc in 2010 after leaving Wireless Generation, where they saw the fundamental problems of monitoring and observability.
Datadog helps developers and Ops teams monitor their entire stack in the era of modern software and the cloud.
The name Datadog comes from the team's experience at Wireless Generation, where production servers were known as "dogs" and databases as "data dogs." The particularly nasty Oracle database was called Datadog 17, which was the original code name for the Datadog project.
Lesson 2: Obsess Over the Problems of the User to the Point it Generates Inbound
Datadog founders were obsessed with solving the problem of communication between devs and Ops teams
They spent 6 months studying the customer problem instead of writing code
The pair struggled to get funding because they were starting an infrastructure company in NYC
This forced them to stay grounded in the customer problem and spend all their time with customers
They realized the problem was not just for Wireless Generation, but for the whole industry (DevOps trend)
Datadog was conceptualized to work at the cloud scale across the latest ephemeral technologies
The team put together an initial architecture that still accurately describes the high-level today
Datadog solved the right problem from the get-go
Early customer obsession generated inbound through content marketing and going to conferences
Content marketing featured deep research on technical topics
Conferences allowed the team to get real feedback and generate a flywheel of getting customers inbound
Inbound market demand was the first important leg of enabling product-led growth.
Lesson 3: Enable Self-Service with Short Time to Value
Datadog launched in 2012 and raised $6.2M in Series A funding from Index Ventures towards the end of the year.
Despite customer demand, Datadog did not hire a dedicated salesperson until the company had 30 people.
The team spent time improving the product, which led to important lessons such as the importance of having a high level of confidence about alerts and understanding what customers can and cannot do on their own.
Datadog embraced a self-service and free trial strategy to enable end users to try the product directly from the website without speaking to a salesperson.
Time to value was reduced to 15 minutes, and after 14 days, users could easily purchase the product.
Datadog's self-service model did not require professional services, making it a prime example of SaaS product-led growth.
By the end of 2013, Datadog had surpassed 100 paying customers.
Lesson 4: Optimize for end users’ usage, not RFP checklists
In 2014, Datadog broadened its support for multi-cloud environments, making it compatible with any cloud, hybrid, and on-premises.
Datadog invested in the value proposition of integrating with customer’s complex environments, which helped the company achieve its product principle of "ubiquity."
The company invested in scalability and performance early on, establishing a reputation as a partner enterprise clients could trust at scale.
In 2015, Datadog focused on companies moving from legacy IT to public and private cloud, which gave the product and sales teams a clear customer.
The company stayed focused on building and optimizing for end users' needs rather than adding unnecessary features to succeed in RFPs.
Datadog defined success for its product as broad adoption at customers, rather than adding functionality for the sake of competing with other products.
The strategy worked, and by the end of 2015, Datadog had established a successful bottoms-up go-to-market funnel for a focused user persona.
Lesson 5: Build a Platform to Unify Disparate Services
Datadog had one product in the market for infrastructure monitoring.
The company needed to decide whether to invest in product development or pursue other growth strategies.
The team decided to introduce new products in existing markets by building a platform atop the existing agents and connectors deployed across customer's entire tech stacks.
Datadog developed a platform to unify disparate services, including infrastructure monitoring, application performance monitoring (APM), and logs monitoring.
The team completely separated the APM team to avoid losing leadership in the single product.
Datadog raised a $95M series D and established an enterprise sales team to expand its market.
The company acquired Logmatic.io, a platform-agnostic service for querying and visualizing logs, to complete the three use cases.
Datadog launched a log product atop the Datadog platform in 2018, completing the three use cases it discovered a few years earlier.
Datadog achieved industry-leading dollar-based net retention rates (DBNRR) by establishing clear, usage-based pricing of each APM and logs without bundling the products.
The company's IPO emphasized its SaaS platform that integrates the three products.
Lesson 6: Optimize Landing and Expanding
Datadog exemplifies optimization of the land and expand strategy after IPO, with continued growth beyond expectations.
The company continued down the path of product development, adding Synthetics and Real User Monitoring to the User Experience area in 2019, providing more opportunities for upselling.
The company also launched the Datadog marketplace, enabling developers to leverage the Datadog platform to build applications.
These new product developments drove both NRR and customer adds, with 75% of new logos landing with two or more products.
Datadog continues to emphasize frictionless adoption and time to value with new products.
The success of Datadog's land and expand optimization led to over 14,000 customers by the end of 2020.
Lesson 7: Consistently Increase Product Velocity
Product velocity has consistently increased since IPO.
The number of integrations has grown from 350+ to 450+.
The number of features announced at the annual conference has increased from 3 to over 300.
Datadog's product team measures velocity by features shipped and focuses on increasing it.
Major new products launched this year include compliance monitoring, error tracking, and continuous profiler.
Datadog's mission is to bridge gaps between teams, and it picks up smaller categories to unify teams.
Datadog outperformed revenue projections at IPO, with $1.3B in revenue in 2021 compared to a projection of $610M.
Datadog's revenue growth of 56% in the last 12 months outperforms other fast-growing SaaS companies.
Datadog is now prioritizing product development over sales and marketing.
In the story of Datadog, we have encountered the core operating principles of product-led growth, executed flawlessly. This is the operating playbook for future founders to follow.
—> Check out the full article published by Aakash Gupta
💻 Interviewing Engineering Executives
Interviewing engineering executives can be a daunting task.
This article will explore the key topics that can help you conduct an engineering executive search that culminates in hiring a leader who can support your company today and avoid the multi-month process that is ambivalent about potential candidates. Read on to discover how to design a good process for evaluating engineering executives that avoids common missteps and increases your chances of making a successful hire.
Key Takeaways
Avoid the unicorn search: Be cautious when defining the profile of the ideal candidate to avoid spending too much time searching for a rare intersection of skills. Instead, talk to seasoned engineering executives to assess the profile before starting the search.
How interviewing executives goes wrong: Interviewing executives is challenging as there are few people who are wholly qualified to assess the most senior technical leader in a company. Companies often rely on either "vibes and backchannel" or a "broadchurch" approach, neither of which are effective at evaluating candidates.
Structuring your evaluation process: A reasonable evaluation process could include recruiter screening, CEO chat(s), two to three interviews with executive peers, and a 30-minute presentation with a 30-minute Q&A. A written rubric will reduce the risk of false negatives and false positives, which unstructured executive interviews often introduce.
Focusing on four areas to evaluate engineering executives: These include technical experience, people leadership, strategic leadership, and cultural alignment. Evaluating candidates across these areas will help identify well-rounded leaders who can support the company and avoid bogging down the executive team in a multi-month process.
—> Want to hear more about “Interviewing Engineering Executives”? Here’s the full article published by Will Larson
⭐ Star History Weekly Pick
The Star History Weekly Pick is:
Bloop: “a fast code search engine written in Rust.”
⭐️ 8.6k stars reached
Github: https://github.com/BloopAI/bloop
🎬 The Best Way of Learning According to Misko Hevery (Creator of Angular)
How do you get good at coding fast? Well… despite what the internet might say, there’s no quick, easy process. However, Miško Hevery, the inventor of Angular and Qwik, has some wise words to help you along the way, young grasshopper. How do you get good at coding fast? Well… despite what the internet might say, there’s no quick, easy process. However, Miško Hevery, the inventor of Angular and Qwik, has some wise words to help you along the way, young grasshopper.
It’s already over! If you have any comments or feedback, Let’s talk about this together on LinkedIn or on Twitter.
Thanks for reading,
Morgan
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