Redpoint’s Sai Senthilkumar: Powering Cloud Infrastructure’s Next Wave of Innovation

ABSTRACT

KEY POINTS FROM SAI SENTHILKUMAR'S POV

Why is Cloud Infrastructure such an important category moving forward?

• Value creation in the growing infrastructure software market is being driven by next-generation providers. “The infrastructure software market is enormous. Gartner estimates there will be around $850B in cloud infrastructure spending in 2025,” says Senthilkumar. More impressively, nearly all of the value-chain creation within the category is coming from next-generation cloud providers. “Businesses like Confluent, Datadog, HashiCorp, Okta, Snowflake, and Twilio power virtually every industry and have created extremely successful businesses in the software market,” he adds. “In fact, 7 out of the top 10 fastest-growing cloud businesses with over $500M in revenue are infrastructure-related. As the software delivery process continues to evolve and modularize, new infrastructure SaaS vendors will emerge to abstract away important engineering problems.”
• The machine learning ecosystem is reaching an inflection point and creating unprecedented value that ripples through nearly every industry. “Advances in natural language processing (NLP) and computer vision (CV) are redefining how we think about deriving value from data. There’s also lots of buzz around generative AI and LLMs, and rightfully so. Machine learning completely flips the value chain in certain industries,” Senthilkumar says.

What are the business models that might be attached to this category?

• Open-source business models will help reduce friction in the segment and improve distribution. “The open source business model is a catalyst for improved distribution across the overall cloud ecosystem,” says Senthilkumar. “Developers are a notoriously fickle target customer segment,” he adds, “with perpetually-evolving tastes and a disposition towards building versus buying. Open-source offerings allow developers to easily try out software for free and deploy them directly without relying on centralized IT approval.”

• Core infrastructure providers will eventually service and power the underlying functions for every ML use-case application as the space evolves to follow data’s playbook. “What’s most compelling to me is the core infrastructure tooling that powers ML-powered applications. Every machine learning use-case will use these providers under-the-hood,” says Senthilkumar. Emerging infrastructure will ultimately determine what is needed to create production-ready ML applications. “Although the machine learning ecosystem isn’t as mature as the data market, similar solutions around pre-processing, data quality, scalability, observability, and real-time tools are emerging for machine learning practitioners."
  • “It’s important to note,” he adds, “that there isn’t yet the required level of interoperability within the new machine learning stack, and that stitching together these solutions is challenging. But, as with data, we expect machine learning solutions to quickly mature and create a similar open and integrated ecosystem.”

• Continued developer-centric “shift-left” trends in DevSecOps and inflating enterprise budgets for these services will spur further convergence of cybersecurity and DevOps. “Within cybersecurity, I think the next big trend is software supply-chain security. I ran a survey this year that indicated this will be the fastest growing cyber-subcategory in the near-future,” Senthilkumar says. “Developers and engineers will be both the users and buyers of security platforms. Snyk is a monster business in this category as it integrates security directly into development tools, workflows, and automation pipelines. However, there are a wave of startups like r2c and Legit Security that are addressing gaps within AppSec.”

What are some of the potential roadblocks?

• Although temporary, spending on cloud providers and overall enterprise infrastructure could fall in the near-term. “We might see a dip in the next 12-18 months with the current pullback, but companies for the most part want to be software first,” says Senthilkumar. “We’re in the early stages for cloud adoption,” he adds, “especially on the infrastructure side. Founders within this category should aim to solve critical problems that burden engineering teams. These offerings should be highly accessible so that teams can fall in love with the product.”
• Emerging security players that don’t create comprehensive solutions to critical problems risk a quick obsolescence. “There is a no-man’s land within security, where a cyber business stops growing and, if lucky, is acquired by an incumbent like Palo Alto or Crowdstrike,” he explains. “The ones that get stuck in no man’s land are often ‘band-aid’ solutions that don’t really solve a given problem. The ones that demonstrate and offer real IP tackling important problems are often characterized by quick time-to-value (where prospective customers can demo the real product in a fast POC), quick sales cycles, and high ACVs. These businesses have a real shot at breaking out and become standalone businesses.”

VISUAL: CLOUD-BASED SOLUTIONS EAT THE INFRASTRUCTURE SOFTWARE MARKET

Perhaps the most dramatic story underpinning the previous decade’s digital transformation is the adoption of cloud-based infrastructure, with a market cap that grew tenfold over the period and has significant room for expansion.

IN THE INVESTOR’S OWN WORDS

Software is eating the world, and every company is becoming a software company. Every industry, business, and function within a company today is dependent on building and delivering software.

To drive these digital transformation efforts, companies use a key layer of “invisible tech” that powers their software. This underlying software is known as cloud infrastructure, and it is the pick-and-shovel behind global software delivery and consumption.

Cloud infrastructure is the most important enabler of digital transformation efforts worldwide - behemoths like AWS and Azure have allowed countless startups to scale without having to worry about underlying infrastructure, letting them focus on product instead. What’s underpinning these digital transformation efforts is a seismic shift from static on-premise IT primitives to distributed multi-cloud architectures.

Specifically, four key categories within cloud infrastructure excite me: data, machine learning, DevOps, and cybersecurity.

Data is at the center of digital transformation efforts worldwide and is becoming a real differentiator for companies of all sizes. Today’s enterprises have launched advanced analytics and data engineering initiatives to better inform and accelerate decision-making. Virtually every company, from SMBs to Fortune 500 businesses, embeds data into its internal workflows and product.

While machine learning budgets are smaller than data ones, machine learning is the fastest growing budget within cloud infrastructure. We surveyed 60 ML leaders in our network within organizations of 500+ FTEs, and over 50% expect to double their budget in the next 5 years. Around 25% expect to triple their budget.

In the past decade, DevOps has likewise emerged as an important enabler of software innovation. These companies abstract away an important class of problems that costs companies money and development resources.

FInally, cybersecurity remains one of the largest and most critical problems within software. Security solutions are what are known as tier 0 services, the highest level of criticality ascribed to third party software akin to cloud providers like AWS. On top of that, the cloud only exacerbates the problem. If companies have live applications or websites, then they are usually using a cloud provider like AWS or Azure on the backend to host and deploy their software. The growing use of the cloud creates gaps in security coverage and significantly increases attack surface areas.

MORE Q&A

Q: What other key trends are emerging within DevOps?

A: Observability, which is sometimes grouped with DevOps. Over the past decade, the complexity of enterprise infrastructure dramatically increased, primarily driven by the rapid adoption of hybrid and multi-cloud architectures as well as the rise of distributed systems. As infrastructure complexity grew, the volume of log and machine data grew exponentially. You can think of log and machine data as ‘side data’ of a transaction.

The observability market capitalized on this explosive growth of log and machine data, becoming a massive market with a TAM of $50B+. Existing vendors have already built enormous businesses; Splunk surpassed $3B in ARR last quarter. Datadog is a ~$1.3B ARR business growing 70% YoY. The observability market is enormous, and these companies have substantial runway for continued growth as they introduce new products and offerings. At the same time, there are gaps in the market that new startups are addressing.

Q: How has the success of cloud infrastructure among publicly-listed SaaS companies impacted private market valuations?

A: Cloud infrastructure markets are enormous — AWS, Azure, and GCP have continued to grow at a mind-boggling scale, eclipsing $100B in revenue run-rate last year. As companies migrate to the cloud and their underlying infrastructure changes, a whole new toolchain has emerged.

We see the effects of faster growth and attractive outcomes for public infrastructure companies trickle down into the private markets. It’s remarkable to see how much higher the typical Series B and Series C infrastructure startup is valued in recent years. The median Series C valuation for an infrastructure startup was ~$200M post in 2017. Last year, infrastructure software founders were fetching ~$1B post-money for their companies, a ~5x expansion! Investors are underwriting larger outcomes and providing companies with several years worth of capital at extremely attractive terms.

It will be interesting to track where 2022 infrastructure valuations within the private markets shake out, and see if there is as much of a pullback there as we have seen in the public markets. Anecdotally, although tier I infrastructure assets within the private markets are still going for 2021 prices, it seems that, as a whole, valuations across the Series B and Series C have noticeably declined in the past few months.

WHAT ELSE TO WATCH FOR

The convergence of software development and machine learning. “Machine learning engineers are highly sought-after, and hiring is usually the bottleneck for ML teams,” Senthilkumar says. “Through MOOCs and online learning, software engineers can become capable machine learning engineers fairly quickly, contributing to ML endeavors within organizations and wearing multiple hats within development.”

• The adoption of deep learning use-cases will grow within the ML sector. “Deep learning, a subset of machine learning that uses neural networks inspired by the human brain to ‘learn’ large amounts of data, has historically been confined to research labs with limited enterprise adoption,” Senthilkumar says. “The limiting factors have been a lack of structured labeled data, appropriate modeling frameworks, and adequate computing resources. Recently, startups like Lambda Labs are enabling enterprise use-cases around deep learning.”

The adoption of micro-services within DevOps. “Micro-services allow a large application to be separated into smaller independent parts, with each part having its own role. Companies are embracing service-oriented architectures, modularizing their applications to build more scalable, resilient, and agile systems,” he says.

The proliferation of agentless security. “Agentless security is a new, dynamic approach in which no-code is deployed on workloads (container, VM, bare-metal server, etc.) to capture information related to the security of an environment,” explains Senthilkumar. “The advantages of agentless over agent-based security include ease of deployment and maintenance as well as minimal performance impact; the more an agent monitors, the more resources it’ll consume. It’s also typically easier to get your IT team to approve a security solution that doesn’t deploy active agents in your corporate environment." Recent startups that employ an agentless approach include Cyera, Laminar, Orca, and Wiz.

STARTUPS MENTIONED IN THIS BRIEF

• Confluent, Cyera, DataDog, HashiCorp, Laminar Security, Legit Security, Orca, Okta, r2c, Splunk, Snowflake, Snyk, Twilio, Wiz