The AI-Native Cloud — from silicon to agents, one platform, zero lock-in
Run production inference and agents without stitching together GPUs, inference APIs, databases, and agent tooling across vendors. Five integrated layers, one bill, no egress between them — built for how AI actually runs. Start serverless today with an OpenAI- and Anthropic-compatible API; scale to dedicated GPUs and agents on the same platform when you're ready.
Production AI runs on DigitalOcean
Your AI stack is held together by vendors, glue code, and surprise bills.
Modern AI isn't a single model call — it's inference, data, orchestration, and agents running in a loop. Assembling that across providers is where cost, complexity, and fragility creep in.
Costs are unpredictable and hard to defend.
Rising GPU, inference, and egress charges across multiple vendors make monthly spend difficult to forecast or optimize as workloads scale.
One model call becomes an entire platform.
Routing, failover, retries, caching, observability, databases, and agent runtime all get built and maintained in-house — before you've shipped the actual product.
Fragmentation taxes every layer.
Stitching GPUs, inference APIs, vector stores, and agent tooling across vendors means cross-vendor data movement, egress fees, and integration debt that compounds with scale.
Transparent, consumption-based, no surprise egress.
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
From real-time agents to trillion-token workloads, leaders in AI run on DigitalOcean.
One AI-Native Cloud — five integrated layers, from silicon to agents.
Every model on one endpoint, optimized at the kernel.
Serverless, Batch, and Dedicated inference with an intelligent Inference Router built in — on a custom-tuned engine (vLLM + TensorRT) with KV-cache optimization and GPU-aware scheduling.
OpenAI- and Anthropic-compatible API — your existing code works on day one
70+ model catalog with Day 0 access to new releases
Inference Router picks the best model per request — no code changes
INFERENCE ENGINE
Why it matters: faster inference at lower cost, in production in minutes
NO CARD · FREE UNTIL YOU MAKE A CALL · CANCEL ANY TIME
Pay for what you use — across every layer, on one bill.
Start serverless with pay-per-token pricing and no commitment.
Move to dedicated per-GPU-hour when sustained volume makes the math work — same API, same bill.
No egress fees between layers.
Off-peak and batch pricing cut costs further for workloads that can shift or wait
Inference Router is free during public preview
Hyperscalers are complex. Neoclouds give you raw GPUs. Wrappers stop at an API. DigitalOcean is the whole stack.
Teams today choose between hyperscalers, GPU neoclouds, inference wrappers, or building it themselves. Each solves part of the production AI stack and leaves gaps around cost, integration, or operational overhead.
Built for the last era — complex, costly, locked in.
Hundreds of services to stitch together, egress charged every time data moves, and unpredictable bills. Retrofitted for AI rather than built for it.
VS. HYPERSCALERS
Egress fees between services
Enterprise contracts and procurement overhead
AI services bolted onto a general-purpose cloud
Raw GPUs, but no system on top.
Rent bare-metal capacity, then build and maintain the inference stack, databases, orchestration, and agent runtime yourself.
Vs. Neoclouds
Silicon, but not a platform
Weeks of engineering before the first production request
No managed data, agents, or routing
An API to a model — and that's where it ends.
No databases, no agent runtime, no knowledge bases. When inference grows into an agentic workload, you're assembling new vendors.
Vs. Inference wrappers
Inference only
No surrounding platform
Margin stacked on someone else's compute
A permanent infrastructure project.
Internal systems for routing, failover, observability, and scaling that need constant maintenance as models, pricing, and traffic change.
Vs. Build your own
Ongoing engineering and on-call burden
Re-tuning every time the model landscape shifts
Time spent on plumbing, not product
Numbers from teams already running on DigitalOcean.
“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
Three steps and you’re making API calls.
Create a key.
Sign up with email or GitHub. Generate a scoped API key in one click.
01
Scale across the stack.
Add databases, knowledge bases, dedicated GPUs, and agents as you grow — same API, same bill, no egress between layers. Track tokens, latency, and spend in the console.
03
Point your code.
Update your OpenAI- or Anthropic-compatible SDK (or LangChain / LlamaIndex) to the DigitalOcean endpoint and pick a model — or router:general to let the platform choose. No rewrites.
02
A few things teams typically want to know.
Is there a free way to start?
Yes — sign up and start calling the API immediately. Inference Router is free during public preview. You are billed only for model calls at standard rates.
Q · 01
Do I have to adopt the whole platform?
No. Every layer is independently useful. Most teams start with Serverless Inference and add data, agents, or dedicated GPUs as they grow — without changing vendors or rewriting code.
Q · 02
How do I migrate from OpenAI or Anthropic?
Swap your base URL to the DigitalOcean endpoint and select a supported model. OpenAI- and Anthropic-compatible SDKs keep working, along with LangChain and LlamaIndex. Most teams validate on a small workload before fully switching.
Q · 03
What about lock-in?
The platform is open-source-first at every layer — open weights (DeepSeek, Llama, Qwen), open standards (vLLM, Postgres, Weaviate, LangGraph, MCP), and S3-compatible storage. Bring your weights, your harness, your tools.
Q · 04
When should I use Serverless vs. Dedicated?
Serverless is the starting point — usage-based and auto-scaling. Dedicated Inference is for sustained, high-throughput production. Both use the same API and billing, so moving between them is a config change, not a migration.
Q · 05
© DigitalOcean, LLC.
The problem
Our solution
Pricing
HOW THE MARKET BREAKS DOWN
In production
Up and running in minutes
Have questions?
Production AI runs on DigitalOcean
From real-time agents to trillion-token workloads, leaders in AI run on DigitalOcean.
Stop stitching your AI stack together. Build on the AI-Native Cloud.
One platform from silicon to agents — serverless inference, managed data, core cloud, and agent runtime — with no egress between layers and zero lock-in. Start in three steps; scale without re-platforming.
AI-Native Cloud
What happens when a model provider goes down?
The Inference Router (Public Preview) automatically reroutes to the next best model in your pool — no dropped calls, no manual failover.
Q · 06
Each layer is independently useful and works on its own. The advantage is the integration: no egress between layers, no cross-vendor data movement, no orchestration tax. As inference workloads grow into agentic systems, you don't onboard new vendors — the rest of the platform is already there.
Ground models and agents in real-time data.
Managed databases, vector search, and Knowledge Bases that assemble context at runtime — natively integrated with the Inference Engine.
Managed Postgres, MySQL, MongoDB, Valkey, OpenSearch, Kafka
pgvector + Managed Weaviate for retrieval; Knowledge Bases (RAG-as-a-service) exposed as an MCP tool
No cross-vendor data movement between layers
DATA & LEARNING
Why it matters: more relevant outputs, fewer broken agent loops
Cloud primitives built for how AI actually runs.
Compute, networking, Kubernetes, and storage tuned for the bursty CPU + GPU mix agentic workloads demand.
GPU and CPU Droplets on the same platform; DOKS for orchestration
Firecracker MicroVMs with ~200ms cold start for agent sandboxes
Spaces object storage — no in-region retrieval fees
CORE CLOUD
Why it matters: simpler operations at any scale
Run long-running agents without building the plumbing.
A production runtime that separates agent infrastructure from your agent logic.
Open Harness — bring LangGraph, CrewAI, OpenCode, or DigitalOcean's ADK
Plano open-source data plane; secure sandboxing; persistent memory & checkpointing
MANAGED AGENTS
Why it matters: focus on agent logic, not agent plumbing
Owned silicon and networking, built for inference at scale.
Global, DigitalOcean-owned infrastructure co-engineered with NVIDIA and AMD — so your unit economics improve as you scale.
NVIDIA H100, H200, HGX B300 and AMD Instinct MI300X–MI355X GPUs
400 Gb RoCE networking; no egress fees between layers; 99.95% SLA on DOKS with High Availability enabled
INFRASTRUCTURE
Why it matters: performance and economics you own as you grow
