Forward Deployed Engineer

Render, a cloud hosting company, held its first user conference in eight years in San Francisco, targeting developers building AI-native applications. CEO Anurag Goel announced the company now serves 400,000 monthly developer sign-ups, hosts 10 million live services and handles 200 billion monthly requests. Goel argued that AI applications require fundamentally different infrastructure than traditional apps, as they dynamically provision resources at runtime rather than using static configurations. A single query might require anything from lightweight scripting to 128GB of RAM, making traditional serverless platforms unsuitable. Render's solution is "application-defined compute", allowing applications to define infrastructure needs at runtime with appropriate guardrails. However, attendees expressed uncertainty about the rapid pace of AI development, with one CTO admitting enterprise customers still expect to hear about AWS or Azure rather than Render.

LaunchPad Lab partners with Render to help clients ship faster. By: Scott Weisman / June 4, 2026 LaunchPad Lab LLC has spent 12+ years helping companies build mission-critical software. Now LaunchPad Lab LLC is making the foundation underneath that software simpler, faster, and more reliable. LaunchPad Lab LLC is excited to share that LaunchPad Lab has officially joined the Render partner ecosystem. If you haven't heard of Render, it is a modern cloud platform that handles all of the behind-the-scenes infrastructure your software needs to run (servers, databases, deployments, scaling, security patches) so that none of it becomes your problem. More than 4.5 million developers trust Render to keep their applications running, and it's now its recommended platform for hosting the custom products LaunchPad Lab LLC build. Why this matters if you're not technical. Every web application, customer portal, or AI-powered product lives somewhere. Traditionally, "somewhere" meant a complex cloud setup that required dedicated DevOps engineers to configure, monitor, and maintain. That's real money spent on infrastructure instead of the product. Render changes that equation. It's powerful enough to run demanding, modern applications, but simple enough that infrastructure fades into the background. That's a philosophy LaunchPad Lab LLC share: the best technology is the kind you never have to think about. For its clients, the partnership translates into three practical benefits: Faster time to market. Its agile, sprint-based delivery combined with Render's automated deployments means new features go live without downtime and without weeks of infrastructure setup. Your product reaches customers sooner. More budget for what matters. When the platform handles scaling, security, and maintenance automatically, your investment goes toward features that move your business forward, not DevOps overhead. A foundation built for AI. AI-native products behave differently from traditional web apps. They run long processes, orchestrate complex workflows, and scale unpredictably. Render's architecture is purpose-built for this new generation of software, which means the products LaunchPad Lab LLC build today won't outgrow their foundation tomorrow. A natural fit. Since 2012, LaunchPad Lab LLC has helped over 240 companies across financial services, healthcare, legal, education, and technology build digital products that drive real business outcomes: more than 740 projects delivered, a 4.8/5 rating on Clutch, and over 90% of its clients partnering with LaunchPad Lab LLC long-term. Its approach has always been to solve the right problem, not just what's on the surface. Render approaches infrastructure the same way: get the foundation right so teams can focus on building products their users love. LaunchPad Lab LLC bring the product strategy, design, and engineering. Render brings the platform that keeps everything running smoothly in production. You get one accountable team from strategy through launch and beyond. LaunchPad Lab LLC is already deploying client projects on Render today. Learn more about how LaunchPad Lab LLC use Render across its client engagements. Let's build something. Whether you're launching a new product, modernizing an aging system, or bringing AI into your business, this partnership means you can move faster with less risk. Get in touch to talk about your next project.

Ojus Save on how render is rethinking cloud for AI workloads. Ojus Save from Render explains how the platform is evolving cloud infrastructure for AI workloads, GPU access, and developer experience at HumanX 2026. April 15, 2026 The cloud infrastructure market is shifting fast. AI workloads have fundamentally different requirements than traditional web apps - GPU access, burst compute, model serving - and most cloud platforms weren't built for this. Warrant sat down with Ojus Save from Render at HumanX 2026 to talk about how they're adapting. The problem with cloud for AI. Cloud platforms were originally designed around stateless web services and long-running containers. You push code, it scales horizontally, and you pay for uptime. AI workloads break that model in several ways: they need GPUs, they're bursty, they require loading large model artifacts into memory before serving, and the cost profile - driven by GPU-hour pricing - looks completely different from serving a REST API. For most developers building AI-powered products, that means stitching together GPU providers, model hosting services, and traditional cloud platforms - a fragmented stack that's painful to manage. Render has been making a name for itself by simplifying cloud deployment for developers, and Ojus Save walked Saif Gunja through how they're extending that simplicity to AI workloads. What render is doing differently. Render's bet is that developers shouldn't have to become infrastructure experts to deploy AI-powered applications. The same principles that made Render popular for web apps - push to deploy, managed infrastructure, straightforward pricing - are now being applied to GPU workloads and model serving. Ojus explained how Render is approaching GPU availability and making it accessible without the complexity that typically comes with provisioning GPU instances. Instead of forcing developers to work through instance types, availability zones, and spot pricing, Render is abstracting those decisions into a higher-level interface. The goal is a workflow where deploying a model-serving endpoint feels as natural as deploying a web service. That's a concrete shift from the current state of affairs, where GPU compute often means dropping into lower-level infrastructure tooling like raw VM provisioning or Kubernetes cluster management. Developer experience still wins. The clearest theme from the conversation is that developer experience remains the differentiator - even as the underlying workloads get more complex. The teams building AI products are often the same teams that were building web apps recently. They don't want to re-learn infrastructure from scratch just because their stack now includes a model. Ojus made the case that the developer experience gap in AI infrastructure is where the biggest opportunity lies. The raw compute is increasingly available from multiple providers. What's missing is the layer that makes it usable without deep DevOps expertise. The platforms that win developer adoption are the ones that reduce time-to-production, not the ones that offer the most configuration knobs. What's next for AI infrastructure. The conversation also touched on where cloud infrastructure is heading more broadly. As AI workloads become a larger share of total compute demand, platforms need to evolve their pricing models, scaling behavior, and resource management - particularly around GPU scheduling and idle cost reduction. Render is positioning itself as the platform that grows with developers - from side project to production AI workload - without requiring a migration to a different stack as complexity increases. That continuity matters. One of the biggest friction points in the current toolchain is the gap between "easy to prototype" and "ready for production," where developers often have to switch from a managed platform to lower-level infrastructure. Ojus was candid about the challenges ahead, including GPU supply constraints and the need to build tooling that keeps pace with how quickly the AI space is evolving. The direction, though, is straightforward: make cloud infrastructure for AI as approachable as cloud infrastructure for web apps has become. Simplicity as a strategy. Render isn't trying to out-feature the hyperscalers. They're betting that simplicity and developer experience will win the next wave of cloud adoption, just as those qualities drove adoption of platforms like Heroku and Netlify before them. For developers building AI products who don't want to become infrastructure specialists, that's a compelling pitch. This interview was recorded at HumanX 2026 in San Francisco.