IT Infrastructure

[

IT Infrastructure

thesis

]

Across industries, enterprises are accelerating their transitions to the cloud-native world.

Offline transactions are moving online, on-premise business software is increasingly hosted by third-party vendors, and infrastructure that was designed for an on-premise world is struggling to keep up.

Application software is evolving to use microservices instead of monoliths, to be stateless rather than stateful, and to handle unprecedented scale from increasingly demanding customers. Developers are pushing the boundaries of what is possible, and this has created a massive opportunity at the infrastructure level, as fledgling companies vie to build the foundations that will enable modern application development. Meanwhile, old worlds are crumbling and new ones are emerging, as SaaS platforms spring up to alternately unbundle and defragment essential functions.

What's Driving It?

More than ever, complex infrastructure is available to developers via open source offerings, making it easier and cheaper to build application software. Concurrently, IT budgets are growing increasingly decentralized, with leaders of individual departments and teams becoming their own software budget-holders.

This democratization & modularity has led to a massive increase in applications being built for different user personae in the enterprise, resulting in an explosion of departmental sources of truth. This has precipitated a secular feedback loop, in which use case innovation has driven shifts in the infrastructure layer, which in turn enable further innovation of applications.

‍

Enterprises increasingly need help to aggregate, manage, draw inferences, and extract business value from rapidly growing datasets - both from the online data directly powering applications, and the offline data used for business intelligence. In the best case, AI-driven applications are fueled by the rich, contextualized data needed to train models and make workflow and intelligent process automation effective. These applications require new underlying tools and platforms, reimagined for an era in which data is a profit, not cost, center. This evolutionary pressure is driving advances across many categories: better abstraction & performance at the data management layer; faster, more reliable, and adaptive deployment methods; rich monitoring & observability solutions; new feedback loops for application iteration; and endpoint & application security covering the increased surface area resulting from these innovations.

[drivers]

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Over the past decade, the technologies of the Bio-IT wave have catalyzed numerous fields of study, each of which enables the creation of new companies.

Fields

Application Frameworks & Infra

Modern Data Stack

Machine Learning & AI Platforms

Monitoring & Observability

Privacy & Security

Cloud Deployment & Orchestration

Application Frameworks & Infra

Application developers have emerged as one of the most pivotal and fastest-growing personas in the modern data-driven enterprise, and as key enablers of business outcomes in their own right. Their productivity has grown dramatically with advances in microservices and containerized development, and changing paradigms around data access. As old problems give way to novel ones, we have backed some of the most promising companies addressing defining challenges in performance, security, communication, and API management at the infrastructure level.

Modern Data Stack

Rampant unbundling across the data management landscape has resulted in over- specialization, fragmentation, and a highly complex toolset. Combined with explosions in data scale and complexity, new personas and budgets, and systems of record, spinning up even a basic modern cloud-native data stack can be extremely difficult without a dedicated team of expert data engineers. We are exceptionally bullish on companies that have set out to reverse these trends, creating core building blocks from first principles in such foundational areas as cloud database infrastructure, central data management, and data integration and migration.

Machine Learning & AI Platforms

The promises of AI and ML have been almost universally tempered by frustration, as enterprises confront tools that can’t perform at scale, exquisite models that lack access to meaningful data, and infrastructure without the expertise needed to derive real value. Across tooling, infrastructure, and application software, we have identified companies addressing critical gaps, while consolidating access to users, datasets, and proven models. Their platforms are establishing new frontiers for conducting data science and analytics at scale, intelligent process automation, and elegant workflow applications that learn to eliminate repetitive tasks.

Monitoring & Observability

In the beginning, there were logs, and the concept of monitoring was solely concerned with them. In today’s cloud-native world, simple monitoring is no longer sufficient, leading to the expanded category of observability. In terms of inputs, observability includes metrics, events, and traces as well as logs. Furthermore, while monitoring was once considered an add-on, observability has been elevated to a firm operational requirement. Within the 8VC portfolio, observability encompasses both dedicated tools, and constitutes a cross-cutting theme that appears in many different platforms and may refer to infrastructure, application performance, data quality, traffic, and more.

Privacy & Security

The urgency and necessity of strong application security and compliant data policies are self-evident, but look any further, and things get murkier. Security and privacy tools are typically limited in scope, and enterprise solutions are designed for IT, infosec, and data engineers. These are also especially crowded spaces, with numerous point solutions and plugins. By contrast, we have invested in a handful of privacy and security companies, with a few common threads: 1. They are building platforms for the whole company, not just experts. 2. They combine granular controls and universal applicability. 3. They are eliminating presumed tradeoffs between assurance and friction.

Cloud Deployment & Orchestration

While the cloud era has democratized application deployment and enabled previously unimaginable scale, it’s introduced its own set of challenges that legacy orchestration tools have been inadequate to solve. In an edge-native, multi-environment world, application deployment has emerged as a first-class concern alongside application development. We are excited to support companies delivering seamless deployability, resource and performance optimization, business continuity, quality of experience, and cost optimization to a truly worldwide audience.

[Emerging Fields]

Key advisors

Ameet Patel

Pankaj Patel

Ganesh Krishnan

Scott Cook

Doug Merritt

[thesis advisors]

Explore some of our Thesis

investments

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[Our Investments]

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