Download DigitalOcean Cuttlefish Now

Pay per token. No GPU contracts. No minimums.

Forecasting your inference cost should look like forecasting your AWS bill. Batch at ~50% of real-time. Off-peak dynamic pricing on Mini Max M2.5 and Kimi K2.5 today, expanding.

$1M+ customer ARR up 179% YoY in Q1 2026.

>80% of AI customer ARR now from inference + core cloud, not bare metal.

Scale-to-zero on Serverless. Reserved capacity on Dedicated when you graduate.

PREDICTABLE AI ECONOMICS

If it can’t take real traffic, it doesn’t count.

Independently ranked, custom-kernel optimized, 55+ models behind one API. VPC, zero data retention, platform guardrails, and built-in observability ship as defaults — not enterprise add-ons.

#1 by Artificial Analysis on output speed for DeepSeek V3.2 and Qwen 3.5 397B.

230 tok/sec on DeepSeek V3.2 — 3.9× faster than AWS Bedrock.

180M+ patient interactions — Hippocratic AI clinical calls/day at 400ms in production.

PRODUCTION-GRADE BY DEFAULT

Bring your model. Keep your stack open.

Open-weight out of the box: DeepSeek, Qwen, Llama, Mixtral, Phi, gpt-oss. LoRA on Serverless lands Q2; full BYOM on Dedicated today. No proprietary lock-in.

Five integrated layers: compute, network, storage, data, AI — open at every one.

Messages API for Claude Code-compatible agentic workflows.

Drop-in OpenAI and Anthropic schemas. Migrate behind a feature flag, not a rewrite.

OPEN AT EVERY LAYER

Image, video, speech, vision-language. Same API, same bill.

Stable Diffusion 3.5 for image. Wan 2.2 for video. Qwen3 TTS for speech. Nemotron and Kimi for vision-language. Plus the lifecycle around them routing, evals, observability — that wrappers don’t have.

Among inference-only competitors, only Together ships full image/video/audio. Fireworks has no video. Baseten, Groq, DeepInfra have no multimodal.

Platform content guardrails on image and video by default — not opt-in.

Native multimodal generation, not a stitched chain of vendor APIs.

EVERY MODALITY, ONE PLATFORM

Hyperscalers

bundle AI onto a general-purpose cloud, so egress and procurement overhead pile up as you scale.

“In healthcare AI, a node going down isn’t just an SLA issue — it impacts patient experience. We’ve pressed DigitalOcean hard on reliability, access to the newest hardware, and the ability to scale efficiently. They’ve delivered.”

Debajyoti Datta
Co-Founder, Hippocratic AI

“DigitalOcean was the fastest provider to get us up and running, enabling us to advance our AI programs. The collaboration on performance optimization coupled with the support from the DigitalOcean team of solutions architects, accelerated our progress by roughly two to three times.”

Oscar Wu
AI Research Scientist, Workato

GPU neoclouds

give you cheap raw compute, but you still build and maintain the inference stack, databases, and agent runtime yourself.

Inference wrappers

stop at the model call, so the moment you need agents or grounded data, you're onboarding another vendor.

Building it yourself

turns routing, failover, and observability into a permanent engineering project.