AI workloads move fast: machines, projects, models and teams all consume resources. One clear view shows what is running, what it costs and where to optimise. No hidden compute. No surprises at the end of the month.
Control AI spend before it controls you.
AI is easy to prototype, hard to scale. Run demanding workloads with high performance: text and audio streaming, image analysis, content generation, knowledge-grounded apps. Use the models you need, serve real-time experiences, all without managing the complexity behind it.
More capacity when you need it. Less friction when you grow.
An optional SDK to declare, provision and manage your Cloud workloads directly from code, with an OpenAI-compatible endpoint for inference. Available for Python and TypeScript.
Optional add-on: the platform works fully from the dashboard without it.
Read the SDK quickstart docs.inferencekey.com Open source · Apache-2.0Always-on support bot on vLLM, one replica, never sleeps.
from inferencekey import ManagementClient, WorkloadSpec, Backend, ExecutionPolicy
mgmt = ManagementClient.from_env(project="acme")
ref = mgmt.ensure(WorkloadSpec(
name="support-bot", slug="support-bot",
model="meta-llama/Llama-3.1-8B-Instruct",
backend=Backend.VLLM, task_type="text2text",
command="vllm serve meta-llama/Llama-3.1-8B-Instruct",
execution_policy=ExecutionPolicy.FIXED,
execution_policy_config={"replicas": 1},
))Your developers shouldn't fight the infrastructure to ship AI. Run multiple instances of vLLM, SGLang, Ollama or llama.cpp behind one endpoint, swap engines as you scale, and keep building on the tools they already know. The platform handles the infra, your devs handle the AI.
Built for teams scaling AI, not fighting infra.
Same URL · swap the engine behind it
Not every workload needs a machine running 24/7. Smart rules let you define how workloads run: start machines when there is work, stop them when there is none, group tasks, prioritise what matters. Less idle time. Lower costs. Better use of infrastructure.
Let rules handle the work. Keep compute under control.
A 200-developer team on Claude Max pays about $40,000 a month, and its code goes to Anthropic's servers. We ran the same kind of work on one machine with four B200 GPUs, serving DeepSeek V4.1 Flash through InferenceKey: 100 Claude Code agents at once, each with almost half a million tokens of context, and zero errors.
Real tests on 4x B200. The 200-subscription figure assumes power users running two agents each and comes from a simulation calibrated with those tests.
Unlimited cloud servers on every plan. Financial control on every plan. You only pay for how many private servers you run.
Create a project, connect a server, route your first workload in minutes.
Open InferenceKey Cloud cloud.inferencekey.com Watch the full tour 9:24Five workloads on one GPU, cloud H100s on demand. Live traffic, real costs, autoscaling.
Run it on your own GPUs.
Create a project, connect a server and route your first workload in minutes.
Why data leaks through outside AI and how InferenceKey brings it inside: open models, on your servers or in your cloud, cost per team and a real proof.
Your own AI, inside your company.
Create a project, connect a server and route your first workload in minutes.
DeepSeek V4.1 Flash on four B200 GPUs with InferenceKey: what it costs, how it holds 100 agents at once, and where your code stays.
One machine instead of 200 subscriptions.
Create a project, connect a server and route your first workload in minutes.