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Today at Deploy, we are announcing the general availability of DeepSeek V3.2, MiniMax-M2.5, and Qwen 3.5 397B on DigitalOcean Serverless Inference.
The AI industry has a compounding bottleneck, and it isn’t the models. It’s inference. What used to be a single model call has become a system of continuous interaction.
Getting a model to answer 10 inference requests concurrently is tricky but simple enough; getting it to handle 2,000 engineers hitting a coding assistant with long contexts, all day, without runaway costs, is where teams stall.
Our journey to truly understand our customer experience began with a hard look at our internal availability numbers at the start of 2025. We saw something uncomfortable: the numbers didn’t match our customers’ reality.
In large-scale cloud environments, unpredictable hypervisor crashes carry real operational cost. While traditional reactive monitoring that relies on static thresholds and post-hoc alerts were once the industry standard, this monitoring...
We know how to scale traditional web services: throw a load balancer in front of stateless microservices and horizontally scale your CPU instances as traffic grows.
We have moved past the point where a 70GB model was considered “heavy.” With the rise of models like DeepSeek-V3, the GLM series, and other massive Mixture-of-Experts (MoE) architectures, the industry is now grappling with weights...
As AI moves from experimental chat interfaces to production-grade agents, the need for a foundational memory layer to transform these AI-powered tasks into stateful models is apparent.
Load balancing for LLMs is fundamentally different from load balancing for traditional services like web servers, APIs, or databases. Prompt caching is the reason.
At DigitalOcean, documentation has always been a priority. Developers come to our docs to get unstuck, and the faster they find what they need, the better.
Introduction Prompt caching is the process of reusing already computed KV states across inference requests in order to save money and reduce latency.
The cloud AI platform ecosystem today looks more powerful than ever, with access to powerful GPUs like NVIDIA H100 and H200, massive libraries of pre-trained models, and full pipelines for fine-tuning and inference.
At DigitalOcean, we have been vocal about our strategic shift: we are building the world’s premier Agentic Inference Cloud. Our mission is to provide the foundation where AI-native enterprises build and run production inference at scale.
AI is now central to modern software development. Teams across industries are turning to AI to solve product and workflow problems in software. But building production systems is still complex.
Keeping cloud infrastructure secure at scale is challenging. Infrastructure drift, exposed services, and sprawling identities create risk, and teams don’t always have the time or expertise to maintain a consistent security posture across...
Today, we’re announcing that Arcee AI ’s Trinity Large-Thinking is now available in Public Preview on DigitalOcean’s Agentic Inference Cloud, giving developers the ability to run frontier-class reasoning workloads without managing...
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