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How much higher can DigitalOcean stock go? Anthony Di Pizio, The Motley Fool DigitalOcean (NYSE: DOCN) provides a suite of affordable cloud computing services exclusively to small and medium-sized businesses (SMBs). That was already a lucrative business model, but the company is now also helping its customers deploy artificial intelligence (AI) software, and demand is through the roof. The company's revenue growth accelerated last year, propelling its stock to a 41% gain. But it's up by a further 77% in 2026 already, as investors anticipate a continued surge in businesses' appetite for AI-related computing capacity. In fact, DigitalOcean recently announced plans to raise $800 million from investors to build more data center infrastructure. Will AI create the world's first trillionaire? Its team just released a report on the one little-known company, called an "Indispensable Monopoly" providing the critical technology Nvidia and Intel both need. Continue" Despite its recent gains, the stock is still trading at a relatively attractive valuation. So, how much higher can it go from here? AI for businesses of all sizes. The cloud computing industry is dominated by trillion-dollar hyperscalers like Amazon and Microsoft. Those providers usually compete for large customers with high spending potential, whereas SMB customers don't really move the needle for them in terms of revenue. As a result, SMBs often don't get the level of service they need from the hyperscalers. DigitalOcean exclusively targets those customers by offering cheap and transparent pricing, highly personalized service, and a simple dashboard which makes deploying cloud tools very straightforward. These features are ideal for start-ups and SMBs with limited financial and technical resources. The company is now helping its customers enter the AI era. Its Gradient platform provides them with access to the latest large language models (LLMs) from leading developers like OpenAI and Anthropic, which can be used as a foundation for developing AI software applications. DigitalOcean also operates data centers fitted with thousands of the latest AI chips from suppliers like Nvidia and Advanced Micro Devices, and it rents the computing capacity to SMBs for a fee. While hyperscale cloud providers try to lease thousands of chips to their customers at a time, DigitalOcean allows its customers to start with just one chip and scale as needed, which is plenty for small workloads like running an AI chatbot or deploying a few AI agents. The company says its prices are up to 75% cheaper than the hyperscale cloud providers for the same AI chips, which is a substantial saving for its budget-conscious customers.

GTC 2026 announces arrival of inference era: DigitalOcean confirms. News GTC 2026 announces arrival of inference era: DigitalOcean confirms. March 27, 2026 8:1 pm Discover more Machine Learning & Artificial Intelligence At the recent NVIDIA GTC 2026 conference, the focus was on the evolution of AI from the training phase to the production inference phase. This shift signifies a significant development in the AI industry, moving beyond just developing faster chips and more intelligent models to addressing the challenges of running AI at scale in real-world applications. Inference, the stage where AI models are put into production to deliver real products and customer experiences, is now at the forefront of the AI conversation. Factors such as cost efficiency, latency, orchestration, and uptime are becoming just as important as the accuracy of the models themselves. The industry is now looking beyond just hardware advancements to the broader infrastructure needed to support AI-native companies. As AI becomes an integral part of business operations, the focus is on creating a cohesive system that encompasses chips, platforms, models, and applications to meet the demands of customers. DigitalOcean, in collaboration with NVIDIA, announced the launch of the Agentic Inference Cloud, aimed at helping AI developers transition from experimentation to production seamlessly. This initiative includes the introduction of a new data center in Richmond designed specifically for AI inference, equipped with NVIDIA HGX B300 systems and a high-speed RDMA fabric. Additionally, DigitalOcean is integrating NVIDIA Dynamo 1.0 into its Kubernetes platform and expanding model options optimized for various use cases. The momentum towards production-level AI deployment is already evident, with over 43,000 OpenClaw deployments on DigitalOcean, showcasing strong adoption from teams developing always-on assistants and agentic applications. To further explore the practical aspects of running AI inference at scale, leaders from NVIDIA, VAST Data, vLLM, Arcee AI, Character.AI, Workato, and more will be sharing insights at the upcoming DigitalOcean Deploy event in San Francisco on April 28, 2026. This event aims to provide valuable lessons on real-world architecture, performance, economics, and operational efficiency in the field of AI inference. For more Information, Refer to this article. You may also like these: Neil is a highly qualified Technical Writer with an M.Sc(IT) degree and an impressive range of IT and Support certifications including MCSE, CCNA, ACA(Adobe Certified Associates), and PG Dip (IT). With over 10 years of hands-on experience as an IT support engineer across Windows, Mac, iOS, and Linux Server platforms, Neil possesses the expertise to create comprehensive and user-friendly documentation that simplifies complex technical concepts for a wide audience. Discover more Machine Learning & Artificial Intelligence Watch & Subscribe Its YouTube Channel Latest from hawkdive. Neil S - March 27, 2026 10:1 pm Neil S - March 27, 2026 9:1 pm Neil S - March 27, 2026 8:1 pm Neil S - March 27, 2026 7:1 pm Neil S - March 27, 2026 6:1 pm Discover more Machine Learning & Artificial Intelligence

NVIDIA GTC 2026 confirmed it: the inference era is here. Director, Partner Growth Last week at NVIDIA GTC 2026, one message was clear: AI has moved beyond the training era and into the era of production inference. The conversation was no longer just about building faster chips and smarter models; it was about what it takes to run AI at scale with the latency, reliability, and economics real products demand. Reuters called it an "inference boom," and even the CPU became part of the conversation again as inference workloads push the industry to optimize the full system, not just the accelerator. That shift matters because inference is where AI becomes a business. Training ushered in this wave of AI innovation; inference is what turns that innovation into real products and real customer experiences. It is where cost per token, time to first token, orchestration, and uptime start to matter just as much as model quality. GTC made it clear that the industry is moving beyond chips to the broader operating infrastructure architecture required to support AI-native companies. As inference becomes the operational layer of AI, the conversation has moved toward a cohesive system spanning chips, platforms, models and applications, which maps directly to what customers are asking DigitalOcean, Inc. for today. Rather than making isolated infrastructure decisions, businesses are seeking ways to run AI in production that manage latency, improve token economics, and reduce operational complexity. This need is especially critical as AI agents evolve from a new application pattern into a core infrastructure requirement, demanding fast, secure systems capable of supporting constant activity and real-world workloads at scale. That is the backdrop for what DigitalOcean, Inc. announced with NVIDIA last week and the vision for the DigitalOcean Agentic Inference Cloud. Across infrastructure, platform, and deployment, the focus was the same: help AI builders move from experimentation to production with less friction. DigitalOcean, Inc. introduced a new Richmond data center purpose-built for AI inference, featuring NVIDIA HGX B300 systems and a 400 Gbps non-blocking RDMA fabric for demanding reasoning and agentic workloads. DigitalOcean, Inc. is bringing NVIDIA Dynamo 1.0 to DigitalOcean Kubernetes and expanding model access with new options optimized for reasoning, long-context, multimodal, and agentic use cases. And DigitalOcean, Inc. is making it easier to build and deploy always-on agents through NVIDIA NemoClaw and the NVIDIA Agent Toolkit, with both a seamless deployment of agents and models from build.nvidia.com to DigitalOcean Serverless Inference and a 1-Click Droplet, simplifying and shortening setup for NVIDIA NemoClaw. DigitalOcean, Inc. has already begun to see the momentum firsthand. When OpenClaw took off, its team moved quickly to make it easier for builders to put it to work in production. Since then, DigitalOcean has seen more than 43,000 OpenClaw deployments, with strong adoption from teams building always-on assistants and agentic applications. If these themes resonate with you, I hope you'll join DigitalOcean, Inc. at DigitalOcean Deploy on April 28, 2026 in San Francisco. DigitalOcean, Inc. is bringing together leaders from NVIDIA, VAST Data, vLLM, Arcee AI, Character.AI, Workato and more to share practical lessons on what it takes to run AI inference at scale, from real-world architecture and performance to economics and operational efficiency. Meghan Grady Director, Partner Growth Join leaders from Character, Workato, VAST Data, Arcee, and the vLLM ecosystem as they break down how they are running AI in production at scale.