DigitalOcean

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

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 enterprise customer can 10x your request volume overnight), latency-cost tradeoffs that shift constantly, and multi-model orchestration across text, vision, image, video, and audio — each with different API contracts and failure characteristics. Most teams spend months just getting the infrastructure stable. We built DigitalOcean Serverless Inference so you don’t have to. What Serverless Inference Is DigitalOcean Serverless Inference is a fully managed, API-first inference platform — 30+ foundation models across text, code, vision, image generation, video generation, and speech, all through a singl

Getting your hands on a capable AI model is the easy part now. Every team can reach the same frontier models through an API, so a strong model is not what sets a product apart. What separates a working product from a demo is everything around the model. You have to measure whether the agent is actually doing its job, then keep grinding on reliability until it stops making expensive mistakes in front of real users. I moderated a panel on exactly that at DigitalOcean’s Deploy 2026 conference in San Francisco, a forty-minute conversation with four founders on what they’ve learned shipping AI products that people depend on: Angela Hoover , co-founder and CEO of Andi AI , an ad-free consumer search engine that pairs generative AI with live web data to give people direct answers instead of a page of ad-heavy links. Alex Mashrabov , co-founder and CEO of Higgsfield AI , a platform that lets creators and agencies produce cinematic video without any physical production. Hovsep Seraydarian , co-

Coding agents today have a massive spending problem. Every request, whether you’re designing system architecture or writing a single-line docstring, often gets routed to the same expensive frontier model. The result: unnecessary token usage, higher inference costs, and little awareness of task complexity or budget constraints. This high cost stems from a “one-size-fits-all” approach to model usage, where premium frontier models are utilized for trivial tasks that don’t require such intensive reasoning effort. In multi-agent workflows, where orchestrators delegate work to specialized subagents, this lack of discrimination frequently leads to runaway costs and opaque failure modes. Without intelligent routing, developers can essentially be forced into closed-provider lock-in and high API usage fees, which quickly escalate during exploratory building phases. DigitalOcean Inference Router , now in Public Preview, was built to solve this problem by dynamically routing requests to the right

At Deploy 2026, we introduced the DigitalOcean AI-Native Cloud, built for the inference era. Batch Inference on the DigitalOcean Inference Engine enables high-volume asynchronous workloads. As developers move from AI prototypes to production-scale applications, the challenges of cost and rate limits often become a bottleneck. Batch Inference addresses these hurdles by allowing you to process high-volume workloads asynchronously at a fraction of the cost of synchronous requests. Whether you are performing large-scale data transformation, content generation, building embeddings or offline evaluations, Batch Inference provides a unified, consistent way to leverage the world’s leading models from OpenAI and Anthropic, all through a single DigitalOcean interface. The AI Scaling Bottleneck Real-time inference is essential for interactive AI applications such as chatbots, copilots, and search-as-you-type experiences. However, when the task involves processing 10,000 support tickets for sentim

Traffic doesn’t spike on a schedule. A product launch, a viral moment, or a flash sale can send request volume through the roof in seconds, long before your CPU metrics catch up. That gap is where performance suffers. Today, we’re excited to announce that request-based autoscaling on DigitalOcean App Platform is now generally available. Your apps can now automatically scale based on live HTTP traffic signals (requests per second and P95 response latency) so your infrastructure reacts to what’s actually happening, not what happened minutes ago. Now Available for Shared and Dedicated CPU Instances Until now, autoscaling on App Platform required a dedicated CPU plan . That meant a good portion of App Platform users (anyone running on shared CPU instances) had no path to automatic horizontal scaling at all. That changes today. Request-based autoscaling works on both shared and dedicated CPU instances . Whether you’re running an early-stage project on a shared plan or a high-throughput prod