Insight Partners‘ $21M investment signals AI Infrastructure is the Next Big Software Frontier
Insight Partners’ recent $21 million investment in E2B isn’t just another venture capital deal; it’s a strong validation of a critical shift in the AI landscape.The global investment firm, boasting over $90 billion in assets and a track record of 55 accomplished IPOs, is betting big on the foundational layer powering the next wave of AI adoption: infrastructure. This move signals growing confidence that AI infrastructure companies are poised to become the next major software category. Praveen Akkiraju, Managing Director at Insight Partners, articulated the firm’s excitement, stating they are backing E2B’s “visionary team” as they build “essential infrastructure for AI agents.” He highlighted the notable growth and enterprise adoption, believing E2B’s open-source sandbox standard will become fundamental to secure and scalable AI implementation across large organizations. The funding will be strategically allocated to fuel E2B’s expansion. This includes bolstering engineering and go-to-market teams in San Francisco, developing new platform features, and supporting a rapidly expanding customer base.A key focus will be strengthening their open-source sandbox protocol as a universal standard, alongside building enterprise-grade modules like secrets vaults and robust monitoring tools.Why AI Infrastructure is Now the Defining Factor for Enterprise Success
E2B’s rapid ascent demonstrates a crucial evolution in enterprise AI strategy. While the spotlight has largely been on large language models (LLMs) and AI applications, the company’s traction with Fortune 100 companies reveals a important bottleneck: specialized infrastructure. This success underscores a broader trend. As AI transitions from experimental projects to mission-critical systems, the demands on underlying infrastructure increasingly mirror those of traditional enterprise software. Security, compliance, and scalability are now paramount – often more important than model performance itself. Here’s a breakdown of why this shift is happening: Security Concerns: AI agents handling sensitive data require robust security protocols. Compliance Requirements: Highly regulated industries demand strict adherence to compliance standards. Scalability Challenges: Moving from pilot programs to enterprise-wide deployment necessitates scalable infrastructure. Operational Complexity: Managing autonomous AI agents requires complex monitoring and control tools. For technology leaders, E2B’s emergence as essential infrastructure signifies a need to re-evaluate AI transformation strategies. Simply selecting the right model and developing applications is no longer enough. Companies that prioritize investment in a specialized infrastructure layer will be best positioned to successfully scale AI agents.The Future of AI: Platforms Over agents?
We’re entering an era where AI agents are poised to automate a growing portion of knowledge work. this creates a compelling possibility: the platforms that safely enable these agents may ultimately prove more valuable than the agents themselves. Think of it like this: the internet didn’t become valuable because of individual websites, but because of the underlying infrastructure – the protocols, servers, and networks – that allowed those websites to thrive. Similarly, the true value of AI won’t be solely in the algorithms, but in the secure, scalable, and compliant infrastructure that allows them to operate effectively at scale. Insight Partners’ investment in E2B is a clear signal that the market is recognizing this fundamental truth. Stay Ahead with VentureBeat Daily: Want to stay informed on the latest AI business use cases? VentureBeat Daily delivers insights on regulatory shifts, practical deployments, and more, helping you maximize your AI ROI. Subscribe to VB Daily and read our Privacy Policy.E2B, a startup providing cloud infrastructure specifically designed for artificial intelligence agents, has closed a $21 million Series A funding round led by Insight Partners,capitalizing on surging enterprise demand for AI automation tools.
The funding comes as an remarkable 88% of Fortune 100 companies have already signed up to use E2B’s platform, according to the company, highlighting the rapid enterprise adoption of AI agent technology. The round included participation from existing investors Decibel, Sunflower Capital, and Kaya, along with notable angels including Scott Johnston, former CEO of Docker.
E2B’s technology addresses a critical infrastructure gap as companies increasingly deploy AI agents — autonomous software programs that can execute complex, multi-step tasks including code generation, data analysis, and web browsing. Unlike traditional cloud computing designed for human users, E2B provides secure, isolated computing environments where AI agents can safely run perhaps dangerous code without compromising enterprise systems.
“Enterprises have enormous expectations for AI agents. However, we’re asking them to scale and perform on legacy infrastructure that wasn’t designed for autonomous agents,” said Vasek Mlejnsky, co-founder and CEO of E2B, in an exclusive interview with VentureBeat. “E2B solves this by equipping AI agents with safe, scalable, high-performance cloud infrastructure designed specifically for production-scale agent deployments.”
Insight Partners’ $21M Investment Signals AI Infrastructure is the Next Big Software Frontier
Insight Partners’ recent $21 million investment in E2B isn’t just another venture capital deal; it’s a strong validation of a critical shift in the AI landscape. The global investment firm, boasting over $90 billion in assets and a track record of 55 successful IPOs, is betting big on the foundational layer powering the next wave of AI adoption: infrastructure. This move signals growing investor confidence in companies building the tools necessary to reliably deploy AI at scale.Why AI Infrastructure Matters Now
For months, the focus has been on flashy AI applications and large language models. However, E2B’s rapid traction with Fortune 100 companies demonstrates a crucial bottleneck: specialized infrastructure. Companies are realizing that simply having an AI model isn’t enough. They need a secure, scalable, and manageable habitat for those models to operate effectively. Praveen akkiraju, Managing Director at Insight partners, succinctly put it: “We believe that E2B’s open-source sandbox standard will become a cornerstone of secure and scalable AI adoption across the Fortune 100 and beyond.” This highlights a key point – the need for standardization and security as AI moves beyond experimentation.From Experimentation to Enterprise-Grade AI
the evolution of AI within enterprises is driving this infrastructure demand. Initially, AI was a playground for data scientists. Now, it’s becoming integral to mission-critical systems. This transition fundamentally changes the requirements. Here’s how the priorities are shifting: Security & Compliance: Enterprise AI can’t operate in a vacuum. Robust security measures and adherence to regulatory compliance are paramount. Scalability: Pilot projects are one thing; supporting thousands of AI agents across an organization is entirely diffrent. Infrastructure must scale seamlessly. Reliability: Downtime or unpredictable behavior is unacceptable when AI is driving core business processes. Manageability: Monitoring, auditing, and controlling AI agents are essential for responsible AI deployment. These needs mirror those of traditional enterprise software, not consumer-facing AI tools. E2B is addressing this head-on with its open-source sandbox protocol and plans to develop enterprise-grade modules like secrets vaults and monitoring tools.E2B: Pioneering a Universal standard
E2B’s success isn’t just about filling a need; it’s about establishing a standard. Their open-source sandbox protocol aims to become the universal foundation for secure AI agent operation. This approach fosters collaboration and accelerates adoption. The investment from Insight Partners will fuel: Engineering Expansion: Growing the team to accelerate platform progress. Go-to-Market Strategy: Reaching a wider audience and solidifying market leadership. Platform Enhancement: Adding features like secrets management and robust monitoring. Standardization: Strengthening the open-source sandbox protocol.The Future of AI: Platforms over Agents?
As AI agents take on more knowledge work,the platforms that enable their safe and reliable operation may ultimately prove more valuable than the agents themselves. Think of it like this: the internet revolutionized interaction, but the underlying infrastructure – the servers, networks, and protocols – were equally crucial. For technology leaders, this means a strategic shift. AI transformation strategies must prioritize infrastructure alongside model selection and request development. Investing early in a specialized infrastructure layer isn’t just a best practice; it’s becoming a necessity for scaling AI agents successfully.Stay Ahead with VB Daily: Want to stay informed about the latest in AI business applications? Subscribe to VB Daily for daily insights on regulatory shifts, practical deployments, and maximizing your AI ROI. Read our Privacy Policy.Insight Partners’ $21M Investment Signals AI Infrastructure is the Next Big Software Frontier
Insight Partners’ recent $21 million investment in E2B isn’t just another venture capital deal; it’s a strong validation of a critical shift in the AI landscape. The global investment firm,boasting over $90 billion in assets and a track record of 55 successful IPOs,is betting big on the foundational layer powering the next wave of AI adoption: infrastructure. This move signals growing investor confidence in companies building the tools necessary to reliably deploy AI at scale.Why AI infrastructure Matters Now
For months, the focus has been on flashy AI applications and large language models. However, E2B’s rapid traction with Fortune 100 companies demonstrates a crucial bottleneck: specialized infrastructure. Companies are realizing that simply having an AI model isn’t enough. They need robust, secure, and scalable systems to actually run those models in a production environment. Praveen Akkiraju, Managing Director at Insight Partners, succinctly put it: “We believe that E2B’s open-source sandbox standard will become a cornerstone of secure and scalable AI adoption across the Fortune 100 and beyond.” This highlights a key point - the future of enterprise AI hinges on establishing standards and ensuring safety.From Experimentation to Mission-Critical: The Changing Needs of Enterprise AI
The evolution of AI within enterprises is driving this infrastructure demand.Initially, AI was largely experimental.Now, it’s becoming integral to core business processes. This transition dramatically alters the requirements. Here’s a breakdown of the key shifts: Security is paramount: AI agents handling sensitive data require robust security protocols. Compliance is non-negotiable: Enterprises must adhere to strict regulatory requirements. Scalability is essential: AI systems need to handle increasing workloads without performance degradation. Performance is still vital: while not the only factor,model performance remains important. These needs mirror those of traditional enterprise software, not consumer-facing AI tools. Successfully scaling AI agents requires a dedicated infrastructure layer designed for autonomous operation.E2B: Pioneering a Universal Standard for AI Safety
E2B is tackling this challenge head-on with its open-source sandbox protocol. This approach aims to establish a universal standard for secure AI deployment. The $21 million investment will fuel: Team Expansion: Growing engineering and go-to-market teams in San Francisco. Platform Development: Adding new features to the platform. Customer Support: Scaling to meet the needs of a rapidly expanding customer base. Enhanced Security: Strengthening the sandbox protocol with enterprise-grade modules like secrets vaults and monitoring tools. This focus on standardization is critical. A fragmented landscape of proprietary solutions will hinder widespread AI adoption. E2B’s open-source approach fosters collaboration and accelerates innovation.The Future of AI: Platforms Over Agents?
As AI agents take on more knowledge work, the platforms that underpin them may become more valuable than the agents themselves. Think of it like this: the operating system is more fundamental than any single application. For enterprise technology leaders, this means a strategic shift is necessary. AI transformation strategies must prioritize infrastructure alongside model selection and application development. Investing early in specialized infrastructure isn’t just a best practice; it’s a necessity for long-term success.The companies that build and adopt these foundational layers will be the ones who truly unlock the potential of AI at scale. Insight Partners’ investment in E2B is a clear signal that the infrastructure play is poised to define the next chapter of enterprise AI.Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. we give you the inside scoop on what companies are doing with generative AI,from regulatory shifts to practical deployments,so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing.Check out more VB newsletters here.
Insight Partners’ $21M Investment Signals AI Infrastructure is the Next Big Software Frontier
Insight Partners’ recent $21 million investment in E2B isn’t just another venture capital deal; it’s a strong validation of a critical shift in the AI landscape. The global investment firm, boasting over $90 billion in assets and a track record of 55 successful IPOs, is betting big on the foundational layer powering the next wave of AI adoption: infrastructure.This move signals growing confidence that AI infrastructure companies are poised to become the next major software category. Praveen Akkiraju, Managing Director at Insight Partners, articulated the firm’s excitement, stating they are backing E2B’s “visionary team” as they build essential infrastructure for AI agents. He highlighted E2B’s impressive growth and enterprise adoption, predicting their open-source sandbox standard will become a cornerstone for secure and scalable AI implementation across large organizations. This funding will fuel E2B’s expansion in several key areas. Specifically,the company plans to grow its engineering and go-to-market teams in San Francisco,develop new platform features,and support its expanding customer base. A core focus will be strengthening its open-source sandbox protocol as a universal standard, alongside building enterprise-grade modules like secrets vaults and robust monitoring tools.Why AI Infrastructure is Now the Defining Factor for Enterprise Success
E2B’s rapid ascent reveals a fundamental change in how businesses are approaching AI. While the spotlight has largely been on large language models (LLMs) and AI applications, E2B’s traction with Fortune 100 companies demonstrates that specialized infrastructure is now the primary bottleneck to widespread AI deployment. This success underscores a broader trend. As AI agents move beyond experimental phases and become integral to mission-critical operations, the infrastructure demands increasingly resemble those of traditional enterprise software. Security, compliance, and scalability are now paramount – frequently enough more critically important than model performance itself. Here’s a breakdown of why this shift is happening: Security Concerns: AI agents handling sensitive data require robust security protocols. Compliance Requirements: Highly regulated industries demand adherence to strict compliance standards. Scalability Challenges: Moving from pilot projects to enterprise-wide AI deployment requires infrastructure that can handle massive workloads. Operational Complexity: Managing and monitoring autonomous AI agents demands specialized tools and expertise.For technology leaders, E2B’s emergence as essential infrastructure highlights a crucial point: AI transformation strategies must extend beyond model selection and application development. Companies that prioritize investment in this specialized infrastructure layer will be best positioned to successfully scale AI agents. Ultimately, as AI agents take on an increasing share of knowledge work, the platforms ensuring their safe and reliable operation may prove to be even more valuable than the agents themselves. Stay Ahead of the Curve with VB Daily Want to stay informed about the latest AI business use cases? VB Daily delivers daily insights on generative AI, covering regulatory changes, practical deployments, and strategies for maximizing ROI. Read our Privacy policy and subscribe today!Seven-figure monthly revenue spike shows enterprises betting big on AI automation
The funding reflects explosive revenue growth, with E2B adding “seven figures” in new business just in the past month, according to Mlejnsky. The company has processed hundreds of millions of sandbox sessions as October, demonstrating the scale at which enterprises are deploying AI agents.
E2B’s customer roster reads like a who’s who of AI innovation: search engine Perplexity uses E2B to power advanced data analysis features for Pro users, implementing the capability in just one week. AI chip company Groq relies on E2B for secure code execution in its Compound AI systems. Workflow automation platform Lindy integrated E2B to enable custom Python and JavaScript execution within user workflows.
The startup’s technology has also become critical infrastructure for AI research. Hugging Face, the leading AI model repository, uses E2B to safely execute code during reinforcement learning experiments for replicating advanced models like DeepSeek-R1. Meanwhile,UC Berkeley’s LMArena platform has launched over 230,000 E2B sandboxes to evaluate large language models’ web development capabilities.
Firecracker microVMs solve the dangerous code problem plaguing AI development
E2B’s core innovation lies in its use of Firecracker microVMs — lightweight virtual machines originally developed by Amazon Web Services — to create completely isolated environments for AI-generated code execution. This addresses a fundamental security challenge: AI agents frequently enough need to run untrusted code that could potentially damage systems or access sensitive data.
“When talking to customers and special enterprises, their biggest decision is almost always build versus buy,” Mlejnsky explained in an interview. “With the build versus buy solution, it all really comes down to whether you want to spend next six to 12 months building this hiring five to 10 person infrastructure team that will cost you at least half a million dollars…or you can use our plug and play solution.”
The platform supports multiple programming languages including Python, JavaScript, and C++, and can spin up new computing environments in approximately 150 milliseconds — fast enough to maintain the real-time responsiveness users expect from AI applications.
Enterprise customers particularly value E2B’s open-source approach and deployment flexibility. Companies can self-host the entire platform for free or deploy it within their own virtual private clouds (VPCs) to maintain data sovereignty — a critical requirement for Fortune 100 firms handling sensitive information.
Perfect timing as Microsoft layoffs signal shift toward AI worker replacement
The funding comes at a pivotal moment for AI agent technology. Recent advances in large language models have made AI agents increasingly capable of handling complex, real-world tasks. Microsoft recently laid off thousands of employees while expecting AI agents to perform previously human-only work, Mlejnsky pointed out in our interview.
Though, infrastructure limitations have constrained AI agent adoption.industry data suggests fewer than 30% of AI agents successfully make it to production deployment, often due to security, scalability, and reliability challenges that E2B’s platform aims to solve.
“We’re building the next cloud,” Mlejnsky said, outlining the company’s enterprising vision. “The current world runs on cloud 2.0, which was made for humans. We’re building the open-source cloud for AI agents where they can be autonomous and run securely.”
The market possibility appears substantial. Code generation assistants already produce at least 25% of the world’s software code, while JPMorgan Chase saved 360,000 hours annually through document processing agents. Enterprise leaders expect to automate 15% to 50% of manual tasks using AI agents, creating massive demand for supporting infrastructure.
open-source strategy creates defensive moat against tech giants like Amazon and Google
E2B faces potential competition from cloud giants like Amazon, Google, and Microsoft, which could theoretically replicate similar functionality. However, the company has built competitive advantages through its open-source approach and focus on AI-specific use cases.
“We don’t really care” about the underlying virtualization technology, Mlejnsky explained, noting that E2B focuses on creating an open standard for how AI agents interact with computing resources. “We are even like actually partnering with a lot of these cloud providers too, as a lot of enterprise customers actually want to deploy E2B inside their AWS account.”
The company’s open-source sandbox protocol has become a de facto standard, with hundreds of millions of compute instances demonstrating its real-world effectiveness. This network effect makes it arduous for competitors to displace E2B once enterprises have standardized on its platform.
Choice solutions like Docker containers,while technically possible,lack the security isolation and performance characteristics required for production AI agent deployments. Building similar capabilities in-house typically requires 5-10 infrastructure engineers and at least $500,000 in annual costs, according to Mlejnsky.
Enterprise features like 24-hour sessions and 20,000 concurrent sandboxes drive Fortune 100 adoption
E2B’s enterprise success stems from features specifically designed for large-scale AI deployments. The platform can scale from 100 concurrent sandboxes on the free tier to 20,000 concurrent environments for enterprise customers, with each sandbox capable of running for up to 24 hours.
Advanced enterprise features include extensive logging and monitoring, network security controls, and secrets management — capabilities essential for Fortune 100 compliance requirements. The platform integrates with existing enterprise infrastructure while providing the granular controls security teams demand.
“We have very strong inbound,” Mlejnsky noted, describing the sales process. “Once we tackle the 87% we will come back for those 13%.” Customer objections typically focus on security and privacy controls rather than fundamental technology concerns,indicating broad market acceptance of the core value proposition.
Insight Partners’ $21M bet validates AI infrastructure as next major software category
Insight Partners‘ investment reflects growing investor confidence in AI infrastructure companies. The global software investor, which manages over $90 billion in regulatory assets, has invested in more than 800 companies worldwide and seen 55 portfolio companies achieve initial public offerings.
“Insight Partners is excited to back E2B’s visionary team as they pioneer essential infrastructure for AI agents,” said Praveen Akkiraju, Managing Director at Insight Partners. “Such rapid growth and enterprise adoption can be difficult to achieve, and we believe that E2B’s open-source sandbox standard will become a cornerstone of secure and scalable AI adoption across the Fortune 100 and beyond.”
The investment will fund expansion of E2B’s engineering and go-to-market teams in San francisco, development of additional platform features, and support for the growing customer base. The company plans to strengthen its open-source sandbox protocol as a universal standard while developing enterprise-grade modules like secrets vault and monitoring tools.
The infrastructure play that could define enterprise AI’s next chapter
E2B’s trajectory reveals a fundamental shift in how enterprises approach AI deployment. While much attention has focused on large language models and AI applications, the company’s rapid adoption among Fortune 100 firms demonstrates that specialized infrastructure has become the critical bottleneck.
The startup’s success also highlights a broader trend: as AI agents transition from experimental tools to mission-critical systems, the underlying infrastructure requirements more closely resemble those of traditional enterprise software than consumer AI applications. Security, compliance, and scalability — not just model performance — now determine which AI initiatives succeed at scale.
For enterprise technology leaders, E2B’s emergence as essential infrastructure suggests that AI transformation strategies must account for more than just model selection and application development. The companies that successfully scale AI agents will be those that invest early in the specialized infrastructure layer that makes autonomous AI operation possible.
In an era where AI agents are poised to handle an ever-growing share of knowledge work, the platforms that keep those agents running safely may prove more valuable than the agents themselves.
