How E2B turned important to 88% of Fortune 100 corporations and raised $21 million

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 outstanding 88% of Fortune 100 corporations have already signed up to make use of E2B’s platform, in keeping with the corporate, highlighting the fast enterprise adoption of AI agent expertise. The spherical included participation from current traders Decibel, Sunflower Capital, and Kaya, together with notable angels together with Scott Johnston, former CEO of Docker.

E2B’s expertise addresses a crucial infrastructure hole as corporations 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 doubtlessly harmful code with out compromising enterprise programs.

“Enterprises have huge expectations for AI brokers. Nevertheless, we’re asking them to scale and carry out on legacy infrastructure that wasn’t designed for autonomous brokers,” stated 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.”

Seven-figure month-to-month income spike exhibits enterprises betting huge on AI automation

The funding displays explosive income progress, with E2B including “seven figures” in new enterprise simply up to now month, in keeping with Mlejnsky. The corporate has processed a whole lot 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 knowledge evaluation options for Professional customers, implementing the potential in only one week. AI chip firm Groq depends on E2B for safe code execution in its Compound AI programs. Workflow automation platform Lindy built-in E2B to allow customized Python and JavaScript execution inside person workflows.

The startup’s expertise has additionally turn into crucial 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 guage giant language fashions’ net growth capabilities.

Firecracker microVMs clear up the damaging code downside plaguing AI growth

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 might doubtlessly injury programs or entry delicate knowledge.

“When speaking to prospects and particular enterprises, their largest determination is sort of at all times 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 group that can value you at the very least half 1,000,000 {dollars}…or you should utilize 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 keep up the real-time responsiveness customers count on from AI purposes.

Enterprise prospects significantly worth E2B’s open-source method and deployment flexibility. Firms can self-host the whole platform free of charge or deploy it inside their very own digital personal 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 expertise. Current advances in giant language fashions have made AI brokers more and more able to dealing with complicated, real-world duties. Microsoft not too long ago 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 on account of safety, scalability, and reliability challenges that E2B’s platform goals to unravel.

“We’re constructing the subsequent cloud,” Mlejnsky stated, outlining the corporate’s formidable 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 at the very least 25% of the world’s software program code, whereas JPMorgan Chase saved 360,000 hours yearly by means of doc processing brokers. Enterprise leaders count on to automate 15% to 50% of guide 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 might theoretically replicate comparable performance. Nevertheless, the corporate has constructed aggressive benefits by means of its open-source method and give attention to AI-specific use instances.

“We don’t actually care” concerning the underlying virtualization expertise, Mlejnsky defined, noting that E2B focuses on creating an open commonplace for the way AI brokers work together with computing sources. “We’re even like truly partnering with a whole lot of these cloud suppliers too, as a result of a whole lot of enterprise prospects truly need to deploy E2B inside their AWS account.”

The corporate’s open-source sandbox protocol has turn into a de facto commonplace, with a whole lot of tens of millions of compute situations demonstrating its real-world effectiveness. This community impact makes it troublesome for rivals to displace E2B as soon as enterprises have standardized on its platform.

Various options like Docker containers, whereas technically potential, 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 very least $500,000 in annual prices, in keeping 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 embrace 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’ve got very robust 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 usually give attention to safety and privateness controls reasonably than elementary expertise considerations, indicating broad market acceptance of the core worth proposition.

Perception Companions’ $21M wager 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 property, 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 group as they pioneer important infrastructure for AI brokers,” stated Praveen Akkiraju, Managing Director at Perception Companions. “Such fast progress and enterprise adoption will be troublesome to attain, and we consider that E2B’s open-source sandbox commonplace will turn into a cornerstone of safe and scalable AI adoption throughout the Fortune 100 and past.”

The funding will fund growth of E2B’s engineering and go-to-market groups in San Francisco, growth of further platform options, and help for the rising buyer base. The corporate plans to strengthen its open-source sandbox protocol as a common commonplace whereas creating enterprise-grade modules like secrets and techniques vault and monitoring instruments.

The infrastructure play that might outline enterprise AI’s subsequent chapter

E2B’s trajectory reveals a elementary shift in how enterprises method AI deployment. Whereas a lot consideration has targeted on giant language fashions and AI purposes, the corporate’s fast adoption amongst Fortune 100 companies demonstrates that specialised infrastructure has turn into the crucial bottleneck.

The startup’s success additionally highlights a broader development: as AI brokers transition from experimental instruments to mission-critical programs, 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 expertise leaders, E2B’s emergence as important infrastructure means that AI transformation methods should account for extra than simply mannequin choice and utility growth. The businesses that efficiently scale AI brokers can be those who make investments early within the specialised infrastructure layer that makes autonomous AI operation potential.

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 helpful than the brokers themselves.