DigitalOcean Blog

Omarchy is a keyboard-first Linux desktop built on Arch and Hyprland by David Heinemeier Hansson (DHH), and its production pipeline, packaging builds, PR review agents, and QA testing, now run on DigitalOcean. Agent workloads don’t behave like a typical web server. They’re bursty and parallel: an agent spins up, does one job, and shuts down. Omarchy’s pipeline is a real example of that pattern, a…

AI agents are helping developers, teams, and businesses do more: writing and executing code, conducting research, and running dynamic workflows across systems. But that ability is often bounded by where they run. Close the laptop, and the work stops there. You can’t pick it up on another device, hand off to a teammate, or scale it across users. Moving agents to cloud VMs solves part of this probl…

We’re excited to introduce v5 Droplets, a new generation of compute built on 5th Gen AMD EPYC™ processors. v5 Droplets are purpose built to deliver higher performance for demanding workloads such as compute-intensive agentic AI platforms, AI/ML tools, high throughput audio/video transcoding, and high-traffic distributed web applications and APIs. v5 Droplets deliver up to 30% higher performance p…

Setting the stakes In early July, security researcher Hyunwoo Kim discovered Januscape (CVE-2026-53359), a flaw in KVM’s handling of nested virtualization that could allow a malicious guest to escape into the host hypervisor. It was disclosed publicly on July 6 via the Linux oss-security mailing list . For a cloud provider, a guest-to-host escape is the most serious class of vulnerability there i…

DigitalOcean launched Kimi K3 on day 0. It’s already one of the most popular models on the platform and across the market: second most likes on Hugging Face, sixth most traffic on OpenCode. Getting a model this size running well on day zero took real work across several teams. Thanks to Moonshot AI, Inferact, RadixArk, NVIDIA, and AMD for the help getting there. Standing up a new model, integrati…

Anyone building with AI eventually hits the same tradeoff: how to get the most intelligence per dollar, the right model at the right cost for each task. That’s what DigitalOcean Inference Engine is built for. On the hardest deep-research tasks, synthesizing several models’ outputs outperforms relying on one: an all-open-source panel (GLM 5.2 + Kimi K2.6) scored higher than every single model we t…

Effective August 1st, 2026, we will be updating prices on select GPUs. This change reflects strong demand for advanced GPU capacity and helps us expand reliable access to high-performance compute for customers. Even with the updated rates, DigitalOcean continues to offer some of the most competitive GPU infrastructure pricing in the market. Below is a detailed breakdown of these upcoming changes …

Production Weaviate in minutes, managed by DigitalOcean. Starting at $20/month. Vector databases have become a core piece of the AI application stack. Whether you’re building retrieval-augmented generation (RAG), semantic search, agentic workflows and memory, or similarity-based recommendations, you need a vector store that’s reliable, fast, and doesn’t require a dedicated ops engineer to keep ru…

Moving AI from a flashy demo to a high-volume production environment is a transition filled with hidden technical debt and infrastructure challenges. There’s a difference between calling the OpenAI API in a weekend prototype and serving 50,000 concurrent users who need sub-200ms latency, graceful fallbacks, and reliable output every single time. It is rarely a “model problem.” Instead, it is a pr…

Choosing the right model or inference router for production means more than reading a leaderboard. It means validating any model or routing configuration on your own data using your prompts and your evaluation criteria before it ever reaches production, and comparing quality, latency, and cost in one place. Evaluations, now available on the DigitalOcean Inference Engine, lets teams validate any m…

As your agents are working on more complex, long-running work, they need a clean, persistent environment to keep running. Setting up a persistent remote machine by hand means creating a cloud server, configuring SSH keys, installing dependencies, and wiring everything back to your workflow. It’s a lot of infrastructure work before you write a single line of code. Today, we’re making that easier. …

AI applications and agents are only as capable as the tools, data, and systems they can access. With Server-Side Tools, now in Public Preview for DigitalOcean Inference Engine, a model can call out to search the web, read your data, call your systems, and take action all from inside a single inference request. You can enable the new tools with your existing DigitalOcean Model Access Key. No separ…

At DigitalOcean, we’re committed to providing high-performance infrastructure for the next generation of AI, which is why we’ve been focused on hosting frontier Large Language Models (LLMs) on frontier GPUs—including AMD GPUs . We see inference performance as an intricate systems-level challenge. For frontier open-weight models, achieving peak output speed is not just about the raw hardware. It a…

Earlier this year, we needed to hire a cohort of engineers in Seattle, fast. We had a product launching at our marquee conference, Deploy , a hard deadline, and a clear picture of what the work would actually require. What we didn’t want was an interview process designed for a world that no longer exists. So we rebuilt it from scratch and opened a brand-new office in Bellevue for everyone we hire…

Most teams running inference at scale do not fail because they cannot find a “good” model. They fail because they ship a routing policy that looks fine in a playground, but drifts the moment it sees real prompts, real latency tails, and real per-token cost. The routing policy breaks on the prompts you never tested and your users find out before you do. Now you can use Model Evaluations, available…

Deploy 2026 came and went, and we’re still buzzing. For one day at Convene 100 Stockton in San Francisco, developers, startup founders, customers, and partners filled the room to talk about a shared challenge: how to build and scale AI products without unnecessary complexity. Conversations moved from infrastructure to inference costs, production workloads, vector databases, and what teams actuall…

Building an AI-native application requires a data layer that can do two things at once: handle the structured, transactional queries your application runs on, and understand meaning well enough to power semantic search across unstructured content. An AI application needs both — precise SQL for account balances and transaction records, and vector search to surface conceptually related patterns, an…

The growth of generative AI isn’t driven solely by AI companies with proprietary models. Open-source AI is reshaping the developer ecosystem, fueled by a growing community of builders. But what does it take to go from open models to production-ready agentic AI, and what do developers need to know to get there? This question was the focus of the DigitalOcean Deploy session, “Open by Design: How NV…

Introduction Inference demand is growing fast, and it’s only accelerating. By 2030, inference is expected to account for the majority of AI compute globally. But scaling inference isn’t just a hardware problem. Most teams discover too late that a significant portion of their compute spend is avoidable, primarily because their systems are silently repeating work they have already done, recomputing…

The Problem: Inference Gets Hard at Scale If you’ve shipped an AI feature to production, you already know: the hard part isn’t making a model respond to a prompt. The hard part is making it respond more reliably, at scale, across multiple models, without burning through your budget. The moment real users show up, you’re dealing with GPU resource contention, traffic unpredictability (a single ente…