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DigitalOcean
DigitalOcean unveiled its AI-Native Cloud platform at the Deploy 2026 conference in San Francisco on April 28, marking a significant expansion of the cloud provider's offerings for production artificial intelligence workloads. The integrated platform spans five layers covering infrastructure, core cloud, inference, data and managed agents, and the company says it is already running production workloads at Higgsfield AI, Hippocratic AI, ISMG, Bright Data and LawVo.
The AI-Native Cloud is built across five layers designed to work together. Managed Agents covers open agent harness support and secure sandboxes. Data and Learning brings PostgreSQL pgvector and Valkey capabilities. The Inference Engine provides serverless endpoints and an intelligent model router. Core Cloud handles Kubernetes and CPU/GPU compute. Infrastructure spans 20 global data centers with Nvidia H100, H200 and HGX B300 GPUs, AMD Instinct MI300X, MI350X and MI355X GPUs and a 400G RoCE RDMA fabric, according to the company.
The platform targets three primary workload patterns. The first is cloud-native software-as-a-service products adding AI features. The second is AI-native products where every interaction consumes tokens. The third is agent-native systems running autonomously in extended loops.
Alongside the platform announcement, DigitalOcean launched its Inference Engine with four core capabilities. The Inference Router is designed to solve inefficiencies in agentic AI by matching requests to models based on task, context, cost, latency and developer-defined preferences. The system includes Batch Inference, Serverless Inference and Dedicated Inference options, giving development teams a single engine to match workload types to appropriate performance and cost profiles.
DigitalOcean, citing Artificial Analysis benchmarks, says its Inference Engine achieved 3x faster time-to-first-token and 3x higher output speed than Amazon Bedrock on DeepSeek V3.2 at 10,000 input tokens. Artificial Analysis' public provider page lists DigitalOcean as the fastest provider by output speed for DeepSeek V3.2, while Google Vertex leads on time to first token. Benchmark rankings can vary by workload and over time.
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The customer cost and performance figures cited below come from DigitalOcean's own announcements and customer-reported results, not independent audits. According to the company, legal-tech platform LawVo achieved a 42% inference cost reduction after switching to DigitalOcean while processing 500 million tokens weekly across 130 AI agents, with zero code changes. ISMG reduced infrastructure costs by more than five times after consolidating on the platform. Bright Data scaled from 4,000 Droplets to 75,000 vCPUs over eight months, according to DigitalOcean.
The company also provided a modeled pricing comparison showing monthly costs of $67,727 for a representative corporate-travel agent workload on its platform, compared to $84,827 on Baseten with AWS and $110,337 on AWS AgentCore. Those figures reflect DigitalOcean's own workload assumptions and should be validated against individual production traffic before treating them as broadly applicable.
The platform supports 70+ open-source and frontier models, with day-zero access to select releases through a centralized Model Catalog. New additions include Nvidia Nemotron 3 Nano Omni, DeepSeek V3.2, Llama 3.3 70B, Qwen 3.5 and MiniMax M2. The company says the stack supports open standards including OpenCode, LangGraph, PostgreSQL, MySQL and Kubernetes.
Nvidia Vice President of Generative AI Software Kari Briski said open models are giving builders more choices in how they build AI applications and that the Nvidia-DigitalOcean collaboration brings Nemotron models to an open, full-stack platform designed to help developers build and scale real-world AI applications.
Additional launches include Knowledge Bases providing a retrieval-augmented generation pipeline, Managed Weaviate for vector database operations and expanded evaluation and guardrails services. The platform arrived with more than 15 new general availability and preview features. Several capabilities, however, remain in public or private preview and are not fully available to all customers. Teams evaluating the platform should check region availability, data-residency requirements and enterprise support terms against their own requirements before migrating workloads.
DigitalOcean cited projections that the world will process more than 500 trillion inference tokens per day by 2030, up from approximately 50 trillion today. The company also noted that agentic systems consume approximately four times more CPU capacity than traditional workloads and 15 times more tokens than human users.
CEO Paddy Srinivasan said AI-native companies are no longer building simple applications that make a single model call, but are instead building distributed, stateful, multi-agent systems that require infrastructure, inference, data, orchestration and agents working in concert.
The AI-Native Cloud platform is available to customers today, with the company serving more than 640,000 customers across 20 data centers globally.
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