A Swedish startup valued at nearly $6 billion just acquired a cloud hosting platform. This is not a random expansion. It is the clearest signal yet that AI-powered software creation is ready to reshape how applications move from idea to production.
Lovable, the AI-powered code generation platform, announced its acquisition of Molnett, a European cloud provider specializing in secure container deployments. The combination creates an end-to-end pipeline: from the moment a user describes an application in plain language to the moment that application serves real customers on production infrastructure.
For businesses exploring AI development tools, understanding this acquisition provides insight into where the industry is heading and what questions to ask when choosing infrastructure for AI-generated applications. InMotion Hosting has already published comprehensive guides for deploying AI-generated websites and publishing Lovable apps to help businesses navigate this transition.
The Rise of Vibe Coding: What Lovable Built
Lovable represents a category of software development that barely existed two years ago. The industry calls it “vibe coding.” Users describe what they want in conversational language, and the platform generates working applications, complete with front-end interfaces, back-end logic, database connections, and API integrations.
The adoption numbers are striking. Lovable launched publicly in November 2024. By November 2025, the company reported $200 million in annual recurring revenue, nearly 8 million users, and 100,000 new products built on its platform every single day. More than half of Fortune 500 companies are reportedly using the platform to accelerate prototyping and internal tool development.
The company raised $200 million in Series A funding at a $1.8 billion valuation in July 2025, led by Accel with participation from investors including Klarna CEO Sebastian Siemiatkowski and Slack co-founder Stewart Butterfield. Recent reports suggest a new funding round could value the company above $6 billion.
What makes this growth notable is who is using the platform. While some users are experienced developers accelerating their workflows using methods like BMad, many are non-technical founders, product managers, and business operators who previously could not build software at all. The barrier to creating functional applications has dropped dramatically.
What Molnett Brings: Security-First Infrastructure
Molnett built its reputation on solving a specific technical challenge: running containerized applications with enterprise-grade security and minimal configuration overhead.
The platform accepts Docker containers and deploys them with automatic HTTPS endpoints, SSL certificate management, networking configuration, and service discovery. Users define their services in a YAML file, deploy with a command-line tool, and receive working endpoints without wrestling with infrastructure complexity.
The security architecture sets Molnett apart. Every container runs inside a Firecracker MicroVM, the same lightweight virtualization technology that powers AWS Lambda and AWS Fargate. Firecracker provides hardware-level isolation with near-container performance, meaning each application runs in its own micro-virtual machine that protects against container escape vulnerabilities. The startup time is measured in milliseconds, enabling rapid scaling without security compromises.
Molnett also operates entirely within EU data centers, addressing GDPR compliance requirements by design. For businesses serving European customers or operating under strict data residency requirements, this architecture removes compliance friction from the deployment process.
When Molnett co-founder Mikael Knutsson announced the acquisition, he described joining Lovable as an opportunity to build “the last piece of software.” The ambition is clear: create a platform where anyone can turn an idea into a production application without encountering traditional development barriers.
The Lovable/Molnett combination addresses a problem that has become increasingly visible as AI coding tools gain adoption: the gap between generating code and running it in production.
AI tools excel at creating working prototypes. Within minutes or hours, users can go from describing an idea to seeing a functional application. The experience feels magical. Everything works on the development server. The interface looks professional. The features respond correctly.
Then comes deployment.
Moving from development to production requires decisions that AI tools do not make automatically. Which infrastructure should host the application? How should networking be configured? What security measures are appropriate? How will the application handle traffic spikes? What happens when something fails at 3 AM?
Industry observers have started calling this the “prototype-to-production gap.” Developers describe the experience as code entering a hostile environment where authentication fails, APIs vanish, and configurations that worked perfectly in development refuse to cooperate in production. The frustration is real enough that startups are raising venture funding specifically to solve this problem.
Lovable’s acquisition of Molnett represents one solution: vertical integration. By controlling both the code generation and the deployment infrastructure, the platform can optimize the entire pipeline. The system that generates the code knows exactly how that code will be deployed, enabling automation that would be difficult or impossible with separate tools.
The strategic logic behind vertical integration has implications for any business evaluating how AI tools fit into their development workflows.
When platforms control both creation and deployment, they can reduce friction for users. No need to learn separate systems, configure external services, or troubleshoot integration issues. The experience stays seamless from first prompt to production deployment.
This integration also creates valuable feedback loops. When a platform knows exactly how its generated code behaves in production, it can improve its code generation accordingly. Which patterns cause performance problems? Which architectural decisions create scaling bottlenecks? Which configurations lead to security vulnerabilities? Integrated platforms learn faster.
For users, the tradeoff involves flexibility. Tightly integrated platforms optimize for simplicity, which often means making assumptions about how applications should be structured and deployed. Those assumptions work well for many use cases. They become limiting when requirements diverge from what the platform expects.
The practical question for businesses: does your use case fit the assumptions built into integrated platforms, or do you need the flexibility to configure infrastructure according to your specific requirements?
When AI-Generated Applications Need Independent Infrastructure
Many AI-generated applications will run perfectly well on the platforms that created them. Prototypes, internal tools, and simple customer-facing applications often fit comfortably within platform constraints.
Other scenarios call for independent infrastructure. Businesses in this category typically share certain characteristics.
Compliance and data residency requirements beyond what platforms offer. While Molnett provides EU data residency, businesses with specific geographic requirements, industry certifications, or audit obligations may need infrastructure they fully control.
Performance optimization needs that exceed platform defaults. High-traffic applications, latency-sensitive workloads, and resource-intensive processing may benefit from dedicated resources and custom configurations.
Integration with existing systems that platforms do not support natively. Enterprises with established technology stacks, internal tools, and legacy systems often need hosting that connects smoothly with what they already operate.
Long-term cost predictability at scale. Platform pricing models that work well for small applications sometimes become expensive at larger volumes. Businesses planning for growth may prefer infrastructure with more predictable economics.
Strategic independence from any single vendor. Some organizations prefer to avoid deep dependencies on platforms that might change pricing, policies, or capabilities in ways that affect their operations.
For businesses in these categories, the rise of AI-generated applications creates a different question: how do we get the benefits of rapid development while maintaining control over our infrastructure?
Deploying AI-Generated Applications to Your Own Infrastructure
AI development tools generate standard code. The output from platforms like Lovable typically consists of React components, Node.js backends, database schemas, and API configurations. This code runs on any infrastructure that supports modern web applications.
The deployment process depends on what the AI tool exports. Static HTML/CSS/JavaScript sites are the simplest, deploying to any web server with minimal configuration. Framework-based applications require build processes and appropriate runtime environments. Full-stack applications with databases need additional infrastructure components.
All InMotion Hosting products fully support websites and applications generated by AI code generators. Whether you choose Shared Hosting, VPS Hosting, Dedicated Servers, or Cloud infrastructure, you can deploy AI-generated code using standard workflows. InMotion Hosting has published comprehensive documentation specifically for this use case, including step-by-step guides for deploying websites from AI builders to your server and publishing Lovable-created React apps via GitHub.
For Shared Hosting and cPanel environments, InMotion Hosting’s infrastructure fully supports GitHub Actions and remote Git repositories. You can set up automated deployment pipelines that push changes from Lovable directly to your hosting account. The process involves connecting your Lovable project to GitHub, configuring Git Version Control in cPanel, and setting up GitHub Actions workflows that build and deploy your application automatically.
For VPS and Dedicated Server environments, the workflow typically involves provisioning a server, configuring a web server like NGINX, setting up SSL certificates, and deploying the application files. Modern automation tools like Ansible can reduce this process to a few commands. InMotion Hosting’s Cloud VPS platform is an unmanaged solution that includes an Ansible Control Node with curated playbooks, simplifying deployment of common application stacks for users comfortable with server administration. For those who prefer a managed experience, Managed VPS Hosting and Managed Dedicated Servers with cPanel fully support AI code deployments through Git Version Control and familiar cPanel tools, combining the power of dedicated resources with an accessible management interface.
For teams comfortable with container workflows, platforms that support Docker deployments offer a middle ground between fully managed platforms and bare-metal configuration. RamNode’s Cloud VPS provides high-performance KVM instances with NVMe storage across multiple geographic locations, giving development teams the flexibility to run containerized applications with full root access and custom configurations.
For organizations building private cloud environments, enterprise-grade infrastructure provides maximum control. InMotion Cloud delivers OpenStack-powered infrastructure with predictable pricing, eliminating the surprise charges that often accompany hyperscaler deployments. The platform’s AI Co-Pilot is designed to accelerate migration from hyperscalers, legacy virtualization platforms, or on-premise hardware, providing guidance through the transition while maintaining control over costs and configurations.
The Support Dimension: What Changes with AI-Generated Code
Technical support requirements shift when applications are generated by AI rather than written by human developers.
Traditional support scenarios assume the person requesting help understands the code they are running. They may not know the solution to a problem, but they typically know what their application is supposed to do, how its components connect, and where to look for diagnostic information.
AI-generated applications often arrive with users who lack this context. They described what they wanted, the AI built it, and now they need help with an application whose internal structure they did not design and may not fully understand.
This creates support scenarios where technical expertise matters more, not less. Someone needs to bridge the gap between “it is not working” and the specific configuration changes that will fix the problem. Someone needs to evaluate whether the AI-generated architecture is appropriate for the user’s actual requirements. Someone needs to provide guidance on security, scaling, and optimization that the original generation process did not address.
InMotion Hosting built its reputation on human support expertise. Our technical staff averages four years of professional hosting experience, with a minimum of 160 hours of internal training covering the LAMP stack, email configuration, operating systems, and everything in between. This foundation positions us well for a world where users need help understanding applications they did not personally write.
The support relationship extends beyond troubleshooting. As AI-generated applications grow, users often discover they need capabilities the original generation did not include. Adding custom domains, configuring caching, implementing backup strategies, connecting to external services. These tasks benefit from guidance that AI tools do not provide and that generic documentation does not address for specific situations.
What Comes Next
The Lovable/Molnett acquisition will not be the last move in this space. Expect other AI development platforms to pursue similar integration strategies, either through acquisition or by building deployment capabilities internally.
The broader pattern points toward a software industry where more people create applications, more applications run in production, and the traditional boundaries between development and operations continue to blur.
For businesses navigating this transition, a few principles should guide infrastructure decisions.
Match infrastructure to requirements, not trends. Integrated platforms work well for use cases that fit their assumptions. Independent infrastructure works better for use cases that require flexibility. The question is not which approach is superior in general but which approach fits your specific needs.
Plan for growth from the beginning. Applications that start as prototypes sometimes become critical business systems. Choose infrastructure that can scale with success rather than forcing painful migrations later.
Prioritize support quality. As AI tools generate more of the code your business runs, the value of expert human support increases. The people who help you when things go wrong will be worth more than marginal cost savings on hosting fees.
Maintain strategic options. Technology changes quickly. Infrastructure choices that create deep dependencies on single vendors may limit your ability to adapt. Whenever possible, choose infrastructure that runs standard technologies and allows migration if circumstances change.
Conclusion
Lovable’s acquisition of Molnett reflects the maturation of AI-powered software development. What started as impressive demos has become a production-ready pipeline capable of taking applications from natural language descriptions to running infrastructure.
For businesses exploring AI development tools, this evolution creates new possibilities. Software that previously required development teams can now be created by anyone who can clearly describe what they need. The barrier to building applications has genuinely dropped.
The infrastructure decisions that follow remain important. Where applications run, how they are configured, and who supports them when problems arise: these questions do not disappear because the code was generated differently.
Whether you choose integrated platforms, independent cloud infrastructure, or some combination, the goal remains the same: reliable, performant, secure applications that serve your business objectives. The tools for creating those applications are changing. The standards for running them should not.
Build Your Next Project on Infrastructure That Scales
Whether you’re deploying AI-generated applications or building from scratch, InMotion Hosting provides the performance, flexibility, and expert support your projects deserve. All InMotion Hosting products fully support websites and applications created with AI code generators like Lovable, Cursor, and other vibe coding platforms.
Deployment Guides for AI-Generated Applications:
Hosting Solutions:
Shared Hosting: Beginner-friendly hosting with cPanel, Git Version Control, and GitHub Actions support for deploying AI-generated sites.
VPS Hosting: Scalable virtual private servers with full root access, NVMe storage, and included Ansible automation.
Dedicated Servers: Maximum performance and control with enterprise-grade hardware for resource-intensive applications.
RamNode Cloud VPS: High-performance KVM instances with global data center options and competitive pricing.
InMotion Cloud: OpenStack-powered infrastructure with predictable pricing and AI-assisted migration from hyperscalers and legacy platforms.
Talk to our team to find the right solution for your AI-powered applications.