Ship agents,
not infrastructure.
One function call deploys a fully provisioned agent VM — model loaded, tools installed, SSH ready. Go from idea to running agent in under 30 seconds.
Platform
Everything your agents
need to run.
30-second deploys
One API call provisions a VM, loads your model, installs tools, and returns SSH creds. No Terraform, no Dockerfiles.
Multi-model support
Run Hermes, OpenClaw, or bring your own weights. Swap models on the fly without redeploying the agent.
Live observability
Token usage, latency percentiles, error rates, and agent reasoning traces — streamed in real time.
Global edge network
Deploy agents in 12 regions worldwide. Route traffic to the nearest node automatically.
Secure by default
Isolated VMs, encrypted SSH tunnels, rotated API keys. SOC 2 compliance in progress.
Version control
Every deployment is tracked. Roll back to any previous agent configuration in one click.
Quickstart
Three lines.
That's it.
Import the SDK, call deploy(), and you have a running agent with full SSH access. No YAML, no config files, no waiting.
By the numbers
Built for production scale.
Case Studies
Real teams, real results.
How a 4-person research lab replaced their DevOps pipeline
A specialized AI research team was burning 12+ hours per week managing brittle agent infrastructure — spinning up VMs, fighting CUDA drivers, and debugging SSH configs. With InstantAgent, they deploy 18 concurrent research agents in under 30 seconds and dedicate 100% of their bandwidth to actual model research.
Scaling from 5 to 200 support agents without adding a single infra engineer
A fast-growing Series B startup urgently needed to scale their LLM-powered support agents across multiple global compliance regions. Their previous Terraform and Docker setup required roughly one dedicated DevOps engineer for every 15 agents. InstantAgent allowed them to effortlessly scale to 200 agents globally.
Building an AI code reviewer that deploys instantly into any CI/CD pipeline
A prominent developer tools platform wanted to launch an AI code review feature that evaluates every single Pull Request. Maintaining a fleet of persistent GPU servers was destroying their margins. Now, every PR triggers an InstantAgent deploy() that spins up, reviews the diff, and terminates entirely.
Insights
From the engineering team.
Why we built InstantAgent: the hidden cost of agent infrastructure
Every team building AI agents eventually hits the same wall — the gap between 'my agent works locally' and 'my agent is running in production' is enormous. We built InstantAgent to close that gap.
How we provision agent VMs in under 30 seconds
A deep dive into our pre-warming pool, snapshot-based provisioning, block-level lazy loading, and the networking layer that makes sub-30-second deploys possible at scale.
Hermes-3 vs OpenClaw: latency and throughput benchmarks on InstantAgent
We ran both leading open-weight models through identical highly complex workloads across all 12 edge regions. Here are the hard numbers — and exactly when to use each one.
The deploy() API: how we designed a one-function-call interface
Hiding crushing complexity is hard. We went through 14 grueling iterations of the deploy() API before landing on the painfully simple version that ships today.
How it works
Three steps to a running agent.
Define your agent
Choose a model, pick your tools, select a region. One object, everything the agent needs to know.
Deploy instantly
Call deploy(). We provision an isolated VM, load model weights, install tools, and return SSH creds.
Monitor & scale
Watch token usage, latency, and logs in real time. Scale up, swap models, or roll back — all from the dashboard.
Your agents aren't going
to deploy themselves.
One line of code. No credit card. No sales call.
Just agents, running now.
InstantAgent