Prompt caching is now generally available on DigitalOcean Serverless Inference, across supported models in our open model catalog, with no changes to your request code required. Cached input is billed at up to 90% off and time to first token drops by up to 80%.* An agent never starts over. Each new call adds the result of its last tool call to the end of a context that already holds the system prompt, the tool definitions, every file it has read, and every earlier action and result. On DigitalOcean Serverless Inference, more than 90% of the input tokens that agents send are tokens the model…
DigitalOcean is aware of two critical remote code execution (RCE) vulnerabilities in PHP, tracked as CVE-2026-109618 and CVE-2026-109619, publicly disclosed on October 21, 2026. Either allows an attacker to execute arbitrary code on a server running PHP, potentially resulting in full compromise of the affected system. Proof-of-concept exploit code is publicly available, and there are no complete mitigations except patching. Updating PHP to a patched version is the only safe path to protection. PHP powers a significant share of the web, including Cloudways by DigitalOcean, and technologies…
AI hasn’t changed what secure software looks like, but what has changed is almost everything about how software gets built. Security programs designed around old processes are anchored to points on a lifecycle that no longer exist. Last month, DigitalOcean’s Product Security team held a workshop to work through what that means for us. Our discussion centered on what the post-AI software development lifecycle (SDLC) changes about the practice of producing software—the content in this article is taken from that workshop. We affirmed that the outcomes and protections necessary to secure software…
An agent starts a test suite, edits a file, calls a model, and waits. Later, it resumes work with the same processes, files, and context. Sometimes it needs to fork that environment and try two approaches at once. These workloads force a tradeoff on conventional infrastructure. Keep a machine running, and idle sessions continue to occupy resources. Destroy it, and the next interaction must rebuild the environment. Saving the filesystem helps, but it does not preserve a running interpreter, an in-memory index, or a process halfway through a task. DigitalOcean Managed Agents, now in public…
A coding agent finishes a change and wants to open a GitHub issue with a link to its results. The agent can describe the issue, but creating it belongs to a different system. It needs a tool that can access GitHub, a connection with permission to use the correct account, and a decision on whether this particular agent is allowed to open an issue. If the request needs human approval, the person must be able to see what is being proposed before the call runs. Harness Runtime provides the agent’s working environment within DigitalOcean Managed Agents, handling the outward-facing action. The same…
Every few weeks, a new model steps up and claims best-in-class at something. The launch chart makes the case, the link makes the rounds, and your team is back to the question nobody has time for: should we switch? The answer takes a week of evals, side-by-side tests and debate, and by the time your team lands on one, the next contender is already on the scoreboard. It’s a race you can’t win manually. And the launch charts only tell part of the story. They show where a model is strongest, not where it isn’t. Today, we’re enhancing the DigitalOcean Inference Router so it makes that call for…
Coding-agent platforms, sandbox products, and code-execution services all need the same thing: isolated machines that start up fast, retain their state between bursts of work, and don’t consume compute when idle. If you build it yourself, you’ll have to lease compute capacity and turn raw servers into isolated, on-demand compute. This often includes fleet management, snapshot, pause and resume, per-tenant isolation, placement, and recovery. This takes a lot of time that could be better spent on building your product. Today, we’re announcing DigitalOcean MicroVMs in public preview so you don’t…
If you run a multi-team organization, you need to see what each team can provision and change it without filing a ticket. If you sign in to DigitalOcean every day, you want to decide how long you stay signed in. Two new controls handle both. Resource Limits gives org Owners and Admins one place to view and adjust every team’s limits, and configurable session durations let anyone set their session length from 1 hour to 30 days. At the Organization level: see and manage every team’s resource limits If you run a multi-team organization, understanding how much capacity each team is using and how…
An AI agent uses compute differently than a traditional application. Over a single session, it writes and runs code, calls a model, and waits on a tool or a person before picking the work back up. As a result, it needs real compute in short, intensive bursts. It also needs a hard boundary around the code it just generated, so one agent’s mistake can’t spread to the host or to other customers. This pattern is not unique to agents. The same pattern of heavy bursts, long idle waits, and strong isolation also applies to: Sandboxes for untrusted or generated code Per-user environments you create…
Built to align with NVIDIA’s Open Agent Safety Platform strategy for securing autonomous agents Consider an agent investigating a duplicate charge. It reads the customer’s email, checks their billing history, issues a refund, and updates a support ticket. Completing that workflow requires access to confidential information and permission to change business records. At thousands of tickets a day, reviewing every intermediate action would defeat the purpose of automating the work through agents. The challenge is that the agent encounters untrusted content while exercising that authority. A…
Compute (EC2) opened the front door to the first generation cloud. Agents need something different: compute, inference, and data working as one. We built the cloud that delivers all three as one simple, intuitive developer experience for AI-natives. Every era of the cloud has its own unit of work. For the last twenty years, that unit was the virtual machine hour. Customers came for the compute, paid by the hour whether it was working or waiting, and every service that followed existed to drive more of it. Fourteen years ago our answer was a $5 Droplet, and a generation built on it because it…
As your business scales, your database shifts from a simple storage layer to the critical heart of your application architecture. For years, DigitalOcean has helped thousands of startups and growing businesses effortlessly launch and scale fully managed PostgreSQL, MySQL, Valkey, and MongoDB databases without the burden of complex routine maintenance. But when traffic surges, data footprints expand, and uptime becomes non-negotiable, high-growth workloads demand a stronger foundation. That is why we are announcing general availability of DigitalOcean Managed Databases Advanced Edition for…
Intelligence was yours. A founder’s intellectual property, business logic, differentiation, their intelligence, was once theirs alone. You built it, you owned it, and no tools/platform vendor stood between you and your customers. This is no longer clear. It is starting to look like it is “theirs.” Intelligence can now be manufactured: the models, the agents and the harnesses that drive them, and the compute they run on. Providers who own all these can produce the application, and the differentiation, in every industry. That power runs the risk of becoming increasingly concentrated in a…
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, and DigitalOcean infrastructure complements its needs. A single doctl command can provision a Droplet, run the job, call separately configured inference services, and destroy the Droplet when the work…
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 problem; developers and companies building agent harness frameworks still have to build a high-fidelity experience that can match a local session, including agent friendly execution environments, session…
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 is: the hypervisor is the boundary that keeps each customer’s workloads isolated from each other, and from our infrastructure itself. We responded, patched the entire fleet in eight days with zero…
Coinbase CEO Brian Armstrong recently posed the question every company scaling AI is asking: how do you keep spend flat while token usage grows exponentially? This isn’t hypothetical. It’s confronting companies across every sector: Uber exhausted its annual AI coding budget within the first four months of the year and subsequently introduced a $1,500 monthly limit per employee. Walmart placed token limits on its internal Code Puppy agent after employees repeatedly asked it to solve similar problems. A Priceline employee reported that a routine Cursor renewal came back 4-5x more expensive.…
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…
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…
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