DigitalOcean

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 model or inference router configuration on their own data before production. Run structured LLM-as-a-Judge evaluations across catalog models, fine-tuned models, BYOM imports, and router setups without stitching together a separate evaluation stack. DigitalOcean Evaluations Capabilities Evaluations provide everything teams need to validate model and router performance before production. LLM-as-a-Judge scoring runs across any candidate in your inference stack and returns per-item scores with judge rationale, plus latency, token, and cost tracking per run. Six pre-built metrics cover the most commo

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. The DigitalOcean plugin for Codex is now available in Public Preview, letting developers create and connect Codex-ready cloud development machines in their own DigitalOcean account directly from within Codex — using natural language, with no manual setup. This means that not only can your work continue running when you step away, but with Codex in the ChatGPT mobile app you can stay in control — starting, steering, or monitoring work from wherever you are. What is DigitalOcean for Codex? The DigitalOcean plugin connects your DigitalOcean account to the Codex app, letting you provision a persist

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 separate tool infrastructure to assemble, no new credentials, no orchestration layer to operate. Server-Side Tools bring web search, web fetch, DigitalOcean Knowledge Bases, MCP servers, and supported Anthropic and OpenAI tools into your inference requests , each covered below. Bring Real-Time Information Into Your AI Applications When applications need current information such as news, documentation, or live data, models can access the web directly during inference. Web Search: Get live answers from the web Web Search enables retrieval of up-to-date information from the web. This enables research w

Material Event filed 2026-06-17

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 also depends on a complex interaction between model architecture, runtime execution, memory systems, scheduling, and decoding strategy. We believe there’s a significant “performance alpha” found in specialized inference engineering. Optimizing for both speed and cost-efficiency requires a much deeper approach than standard configuration sweeps. By taking a custom approach to the software stack, we can demonstrate that achieving performance parity with more expensive hardware is entirely possible. While the current software ecosystem often presents non-obvious hurdles, deep engineering allows us

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 hired. Here’s what we did, why we did it, and what we heard from the engineers who went through it. The problem with the standard loop The traditional engineering interview loop (recruiter screen, hiring manager screen, technical phone screen, take-home, onsite) was designed for a different era of software development. It tests for pattern recognition and syntax recall. It stages information rather than creating genuine signal. And it takes weeks. More importantly, it doesn’t reflect how engineers actually work today. Production environments are collaborative. Most engineers entering the field righ

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 in Public Preview on the DigitalOcean Inference Engine , to evaluate models available on the platform, or models that you have imported from Hugging Face or DigitalOcean Spaces. Model Evaluations helps you make comparable, reproducible decisions across models, routing strategies, cost, latency, and output quality. In this guide, we walk through setting up, running, and interpreting a Model Evaluation across three inference strategies: using a single frontier model for every request, deploying a task-specific fine-tuned model, or using the Inference Router with a cost- or latency-optimized poli

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 actually need to get AI applications from prototype to production. We’re grateful to everyone who showed up and made it what it was. DigitalOcean took the covers off the AI-Native Cloud , a five-layer stack purpose-built for AI-native companies, with more than 15 product launches in a single keynote. That included Inference Router, Dedicated Inference, Managed Weaviate, Knowledge Bases, expanded GPU and model capabilities, and a new Kansas City data center with liquid-cooled B300s . The event had seven sponsors, including NVIDIA, AMD, Weaviate, OpenRouter, MongoDB, and others. Customers like Hippocrat

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, anomalies, or past cases that a keyword query would never find. Most teams end up stitching these together across different environments, where every query crosses a boundary. Latency compounds and costs grow with the complexity of the glue, not the value of the data. What holds together in a prototype starts to fracture under production load. The DigitalOcean Data & Learning layer is designed to close that gap by giving you structured, vector, and retrieval layers that work together in the same ecosystem. Real-Time Inference and Learning At the heart of any sophisticated AI application is th

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 NVIDIA and DigitalOcean Are Building the Stack for the Always-On Agentic Era.” During this 30-minute chat, Kari Briski, VP Gen AI at NVIDIA, and Salman Paracha, SVP AI at DigitalOcean, discuss why AI-native teams are demanding openness, model flexibility, and infrastructure built for agents that never sleep—and what NVIDIA and DigitalOcean are doing to build support for this next generation of AI development. Watch the full recorded session from Deploy 2026 : View YouTube video Open-Source Models Need Commitment, Not Just a Launch There are many open models in the ecosystem, but having great mode

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 same prompt prefixes and system instructions over and over again. We’ve seen this from two vantage points. From the infrastructure layer, the cost curve becomes visible at scale with clusters that look busy but aren’t efficiently utilized. From the engine layer, the picture is just as clear. Without the right caching and scheduling primitives, even a well-optimized model wastes cycles on redundant computation. The root cause is the same regardless of where you’re standing. The system lacks the memory and coordination to recognize when it’s already done the hard part. Fixing this requires w