Need smarter insights in your inbox? Join our weekly newsletters to get solely what issues to enterprise AI, information, and safety leaders. Subscribe Now
E2B, a startup offering cloud infrastructure particularly designed for synthetic intelligence brokers, has closed a $21 million Collection A funding spherical led by Perception Companions, capitalizing on surging enterprise demand for AI automation instruments.
The funding comes as an exceptional 88% of Fortune 100 corporations have already signed up to make use of E2B’s platform, in line with the corporate, highlighting the speedy enterprise adoption of AI agent know-how. The spherical included participation from current buyers Decibel, Sunflower Capital, and Kaya, together with notable angels together with Scott Johnston, former CEO of Docker.
E2B’s know-how addresses a essential infrastructure hole as corporations more and more deploy AI brokers — autonomous software program packages that may execute advanced, multi-step duties together with code technology, information evaluation, and internet shopping. In contrast to conventional cloud computing designed for human customers, E2B gives safe, remoted computing environments the place AI brokers can safely run doubtlessly harmful code with out compromising enterprise methods.
“Enterprises have monumental expectations for AI brokers. Nevertheless, we’re asking them to scale and carry out on legacy infrastructure that wasn’t designed for autonomous brokers,” mentioned Vasek Mlejnsky, co-founder and CEO of E2B, in an unique interview with VentureBeat. “E2B solves this by equipping AI brokers with secure, scalable, high-performance cloud infrastructure designed particularly for production-scale agent deployments.”
The AI Impression Collection Returns to San Francisco – August 5
The subsequent part of AI is right here – are you prepared? Be a part of leaders from Block, GSK, and SAP for an unique have a look at how autonomous brokers are reshaping enterprise workflows – from real-time decision-making to end-to-end automation.
Safe your spot now – house is restricted: https://bit.ly/3GuuPLF
Seven-figure month-to-month income spike exhibits enterprises betting large on AI automation
The funding displays explosive income progress, with E2B including “seven figures” in new enterprise simply up to now month, in line with Mlejnsky. The corporate has processed tons of of tens of millions of sandbox periods since October, demonstrating the size at which enterprises are deploying AI brokers.
E2B’s buyer roster reads like a who’s who of AI innovation: search engine Perplexity makes use of E2B to energy superior information evaluation options for Professional customers, implementing the aptitude in only one week. AI chip firm Groq depends on E2B for safe code execution in its Compound AI methods. Workflow automation platform Lindy built-in E2B to allow customized Python and JavaScript execution inside consumer workflows.
The startup’s know-how has additionally change into essential infrastructure for AI analysis. Hugging Face, the main AI mannequin repository, makes use of E2B to securely execute code throughout reinforcement studying experiments for replicating superior fashions like DeepSeek-R1. In the meantime, UC Berkeley’s LMArena platform has launched over 230,000 E2B sandboxes to judge giant language fashions’ internet improvement capabilities.
Firecracker microVMs remedy the damaging code drawback plaguing AI improvement
E2B’s core innovation lies in its use of Firecracker microVMs — light-weight digital machines initially developed by Amazon Internet Companies — to create fully remoted environments for AI-generated code execution. This addresses a basic safety problem: AI brokers typically have to run untrusted code that would doubtlessly injury methods or entry delicate information.
“When speaking to prospects and particular enterprises, their largest resolution is nearly all the time construct versus purchase,” Mlejnsky defined in an interview. “With the construct versus purchase answer, all of it actually comes down as to if you need to spend subsequent six to 12 months constructing this hiring 5 to 10 individual infrastructure crew that may value you not less than half one million {dollars}…or you should use our plug and play answer.”
The platform helps a number of programming languages together with Python, JavaScript, and C++, and might spin up new computing environments in roughly 150 milliseconds — quick sufficient to take care of the real-time responsiveness customers count on from AI purposes.
Enterprise prospects notably worth E2B’s open-source method and deployment flexibility. Corporations can self-host the complete platform without cost or deploy it inside their very own digital non-public clouds (VPCs) to take care of information sovereignty — a essential requirement for Fortune 100 companies dealing with delicate data.
Good timing as Microsoft layoffs sign shift towards AI employee substitute
The funding comes at a pivotal second for AI agent know-how. Latest advances in giant language fashions have made AI brokers more and more able to dealing with advanced, real-world duties. Microsoft lately laid off 1000’s of workers whereas anticipating AI brokers to carry out beforehand human-only work, Mlejnsky identified in our interview.
Nevertheless, infrastructure limitations have constrained AI agent adoption. Business information suggests fewer than 30% of AI brokers efficiently make it to manufacturing deployment, typically because of safety, scalability, and reliability challenges that E2B’s platform goals to resolve.
“We’re constructing the following cloud,” Mlejnsky mentioned, outlining the corporate’s bold imaginative and prescient. “The present world runs on Cloud 2.0, which was made for people. We’re constructing the open-source cloud for AI brokers the place they are often autonomous and run securely.”
The market alternative seems substantial. Code technology assistants already produce not less than 25% of the world’s software program code, whereas JPMorgan Chase saved 360,000 hours yearly by doc processing brokers. Enterprise leaders count on to automate 15% to 50% of guide duties utilizing AI brokers, creating huge demand for supporting infrastructure.
Open-source technique creates defensive moat towards tech giants like Amazon and Google
E2B faces potential competitors from cloud giants like Amazon, Google, and Microsoft, which may theoretically replicate comparable performance. Nevertheless, the corporate has constructed aggressive benefits by its open-source method and give attention to AI-specific use instances.
“We don’t actually care” concerning the underlying virtualization know-how, Mlejnsky defined, noting that E2B focuses on creating an open normal for the way AI brokers work together with computing sources. “We’re even like really partnering with a whole lot of these cloud suppliers too, as a result of a whole lot of enterprise prospects really need to deploy E2B inside their AWS account.”
The corporate’s open-source sandbox protocol has change into a de facto normal, with tons of of tens of millions of compute situations demonstrating its real-world effectiveness. This community impact makes it tough for rivals to displace E2B as soon as enterprises have standardized on its platform.
Various options like Docker containers, whereas technically attainable, lack the safety isolation and efficiency traits required for manufacturing AI agent deployments. Constructing comparable capabilities in-house sometimes requires 5-10 infrastructure engineers and not less than $500,000 in annual prices, in line with Mlejnsky.
Enterprise options like 24-hour periods and 20,000 concurrent sandboxes drive Fortune 100 adoption
E2B’s enterprise success stems from options particularly designed for large-scale AI deployments. The platform can scale from 100 concurrent sandboxes on the free tier to twenty,000 concurrent environments for enterprise prospects, with every sandbox able to operating for as much as 24 hours.
Superior enterprise options embody complete logging and monitoring, community safety controls, and secrets and techniques administration — capabilities important for Fortune 100 compliance necessities. The platform integrates with current enterprise infrastructure whereas offering the granular controls safety groups demand.
“We now have very sturdy inbound,” Mlejnsky famous, describing the gross sales course of. “As soon as we sort out the 87% we’ll come again for these 13%.” Buyer objections sometimes give attention to safety and privateness controls somewhat than basic know-how considerations, indicating broad market acceptance of the core worth proposition.
Perception Companions’ $21M guess validates AI infrastructure as subsequent main software program class
Perception Companions‘ funding displays rising investor confidence in AI infrastructure corporations. The worldwide software program investor, which manages over $90 billion in regulatory belongings, has invested in additional than 800 corporations worldwide and seen 55 portfolio corporations obtain preliminary public choices.
“Perception Companions is worked up to again E2B’s visionary crew as they pioneer important infrastructure for AI brokers,” mentioned Praveen Akkiraju, Managing Director at Perception Companions. “Such speedy progress and enterprise adoption might be tough to attain, and we consider that E2B’s open-source sandbox normal will change into a cornerstone of safe and scalable AI adoption throughout the Fortune 100 and past.”
The funding will fund enlargement of E2B’s engineering and go-to-market groups in San Francisco, improvement of extra platform options, and assist for the rising buyer base. The corporate plans to strengthen its open-source sandbox protocol as a common normal whereas creating enterprise-grade modules like secrets and techniques vault and monitoring instruments.
The infrastructure play that would outline enterprise AI’s subsequent chapter
E2B’s trajectory reveals a basic shift in how enterprises method AI deployment. Whereas a lot consideration has centered on giant language fashions and AI purposes, the corporate’s speedy adoption amongst Fortune 100 companies demonstrates that specialised infrastructure has change into the essential bottleneck.
The startup’s success additionally highlights a broader development: as AI brokers transition from experimental instruments to mission-critical methods, the underlying infrastructure necessities extra intently resemble these of conventional enterprise software program than client AI purposes. Safety, compliance, and scalability — not simply mannequin efficiency — now decide which AI initiatives succeed at scale.
For enterprise know-how leaders, E2B’s emergence as important infrastructure means that AI transformation methods should account for extra than simply mannequin choice and software improvement. The businesses that efficiently scale AI brokers will likely be people who make investments early within the specialised infrastructure layer that makes autonomous AI operation attainable.
In an period the place AI brokers are poised to deal with an ever-growing share of data work, the platforms that maintain these brokers operating safely might show extra helpful than the brokers themselves.
