DigitalOcean

Material Event filed 2026-08-04

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, integrating it into DigitalOcean’s Inference Engine, and showcasing its unique attributes on day 0 takes three things: the right hardware, a tuned serving stack, and rigorous verification against Moonshot’s own benchmarks. Here are the lessons we learned along the way: Hardware selection and implementation We selected NVIDIA HGX™ B300 and AMD Instinct™ MI350x GPUs to run K3 because these instances provide the memory capacity, FLOPs, and interconnect horsepower necessary for a model of K3’s size and architecture. We built our distributed inference stack with llm-d because it includes native support for G

Material Event filed 2026-07-24

Anyone building with AI runs into the same tradeoff: how to get the most intelligence per dollar, the right model at the right cost for each task. DigitalOcean Inference Engine is built to help you make that tradeoff, and one way is finding the right model for each job. But sometimes one model isn’t enough. On deep-research tasks, we found that running several models and synthesizing their outputs beats relying on one: an all-open-source panel (GLM 5.2 + Kimi K2.6) scored higher than every single model we tested, including Fable 5, at about half its cost per task. Model synthesis , a new server-side tool on DigitalOcean Inference Engine , does that orchestration for you. It runs from a model configuration you define: a panel of models that process each request in parallel, and a synthesizer model that reviews the panel’s outputs and combines them into one response. Start from an optimized preset or define the panel and synthesizer yourself. It pays off. We benchmarked model synthesis o

Material Event filed 2026-07-23

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 and how they affect you. On-Demand GPU Price Adjustments Effective August 1, 2026, on-demand pricing for NVIDIA and AMD GPU droplets will be updated as follows: What this means for your bill: Any active workloads running on or after August 1, 2026 will be billed at the new rate. By continuing to access or use the services on or after August 1, 2026, you are agreeing to accept and pay the updated rates. These changes will be reflected in your total bill on September 1, 2026. If you do not wish to continue using the service at the updated rate, you will need to take action by August 1, 2026 to de

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 running. Weaviate has become a critical part of that stack — its open-source AI-native vector database powers semantic search, RAG, and agentic workflows for thousands of companies. Self-hosting Weaviate is doable but it comes at a cost. You’re on the hook for backups, version upgrades, security patches, high availability configuration, and storage scaling. That’s real time and real engineering capacity that isn’t going toward your product. Managed alternatives from larger cloud vendors exist, but they often come with per-query fees, per-dimension surcharges, and pricing models that are difficult

Material Event filed 2026-07-07

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 problem of decisions, trade-offs, and architecture. At DigitalOcean Deploy 2026 , we hosted a panel of engineering leaders from Workato , Hippocratic AI , and ISMG. Moderated by Karnik Modi, DigitalOcean’s Senior Manager of Engineering, panelists shared the lessons they’ve learned while running inference workloads at scale. The session focused on managing P99 latency spikes in real-time interactions, restricting agent permissions to prevent “admin” vulnerabilities, and ensuring infrastructure is policy-aware before production traffic hits. These insights move beyond model performance to address t

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