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 firms 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 present buyers Decibel, Sunflower Capital, and Kaya, together with notable angels together with Scott Johnston, former CEO of Docker.
E2B’s know-how addresses a crucial infrastructure hole as firms more and more deploy AI brokers — autonomous software program packages that may execute complicated, multi-step duties together with code technology, knowledge evaluation, and net looking. Not like conventional cloud computing designed for human customers, E2B offers safe, remoted computing environments the place AI brokers can safely run probably harmful code with out compromising enterprise methods.
“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,” stated Vasek Mlejnsky, co-founder and CEO of E2B, in an unique interview with VentureBeat. “E2B solves this by equipping AI agents with safe, scalable, high-performance cloud infrastructure designed specifically for production-scale agent deployments.”
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Seven-figure month-to-month income spike reveals 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 lots of of hundreds of thousands of sandbox periods since October, demonstrating the dimensions 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 knowledge 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 person workflows.
The startup’s know-how has additionally grow to be crucial infrastructure for AI analysis. Hugging Face, the main AI mannequin repository, makes use of E2B to soundly 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 massive language fashions’ net improvement capabilities.
Firecracker microVMs remedy the harmful code drawback plaguing AI improvement
E2B’s core innovation lies in its use of Firecracker microVMs — light-weight digital machines initially developed by Amazon Net Companies — to create fully remoted environments for AI-generated code execution. This addresses a elementary safety problem: AI brokers typically must run untrusted code that would probably harm methods or entry delicate knowledge.
“When talking to customers and special enterprises, their biggest decision is almost always build versus buy,” Mlejnsky defined 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 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 keep up the real-time responsiveness customers anticipate from AI functions.
Enterprise clients significantly worth E2B’s open-source strategy and deployment flexibility. Firms can self-host your entire platform free of charge or deploy it inside their very own digital non-public clouds (VPCs) to keep up knowledge sovereignty — a crucial requirement for Fortune 100 companies dealing with delicate info.
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 massive language fashions have made AI brokers more and more able to dealing with complicated, real-world duties. Microsoft just lately laid off 1000’s of staff whereas anticipating AI brokers to carry out beforehand human-only work, Mlejnsky identified in our interview.
Nevertheless, infrastructure limitations have constrained AI agent adoption. Trade knowledge 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 building the next cloud,” Mlejnsky stated, outlining the corporate’s bold imaginative and prescient. “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 alternative seems substantial. Code technology assistants already produce at the least 25% of the world’s software program code, whereas JPMorgan Chase saved 360,000 hours yearly by way of doc processing brokers. Enterprise leaders anticipate to automate 15% to 50% of handbook duties utilizing AI brokers, creating large 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 way of its open-source strategy and deal with AI-specific use circumstances.
“We don’t really care” in regards to the underlying virtualization know-how, Mlejnsky defined, noting that E2B focuses on creating an open customary for a way AI brokers work together with computing assets. “We are even like actually partnering with a lot of these cloud providers too, because a lot of enterprise customers actually want to deploy E2B inside their AWS account.”
The corporate’s open-source sandbox protocol has grow to be a de facto customary, with lots of of hundreds of thousands of compute cases demonstrating its real-world effectiveness. This community impact makes it tough for opponents to displace E2B as soon as enterprises have standardized on its platform.
Different 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 usually requires 5-10 infrastructure engineers and at the least $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 clients, 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 present enterprise infrastructure whereas offering the granular controls safety groups demand.
“We have very strong inbound,” Mlejnsky famous, describing the gross sales course of. “Once we tackle the 87% we will come back for those 13%.” Buyer objections usually deal with safety and privateness controls quite than elementary 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 firms. The worldwide software program investor, which manages over $90 billion in regulatory property, has invested in additional than 800 firms worldwide and seen 55 portfolio firms obtain preliminary public choices.
“Insight Partners is excited to back E2B’s visionary team as they pioneer essential infrastructure for AI agents,” stated Praveen Akkiraju, Managing Director at Perception Companions. “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 funding will fund enlargement of E2B’s engineering and go-to-market groups in San Francisco, improvement of extra platform options, and help for the rising buyer base. The corporate plans to strengthen its open-source sandbox protocol as a common customary whereas growing 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 elementary shift in how enterprises strategy AI deployment. Whereas a lot consideration has centered on massive language fashions and AI functions, the corporate’s speedy adoption amongst Fortune 100 companies demonstrates that specialised infrastructure has grow to be the crucial bottleneck.
The startup’s success additionally highlights a broader pattern: as AI brokers transition from experimental instruments to mission-critical methods, the underlying infrastructure necessities extra carefully resemble these of conventional enterprise software program than shopper AI functions. 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 utility improvement. The businesses that efficiently scale AI brokers shall 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 information work, the platforms that maintain these brokers operating safely might show extra priceless than the brokers themselves.
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