Runloop provides secure Devbox sandboxes and high-throughput execution for AI agents, code generation tools, and enterprise evaluation workflows. The platform is available as a fully managed cloud service or as an optional deployment inside your AWS VPC for additional control and compliance.
Devboxes start in under a second, deliver low-latency execution, and scale to more than 20,000 concurrent instances, enabling fast, reproducible, and parallel execution for agent development and testing.
Runloop includes an integrated benchmarking system for evaluating agent reliability, running regression tests, comparing models, and producing deterministic results. Organizations can also build custom benchmarks that encode internal workflows, multi-step tasks, and domain-specific scoring.
With reproducible environments, rapid startup, and built-in evaluation tools, Runloop helps teams confidently develop, test, and scale AI agents.
Runloop provides secure, isolated Devbox sandboxes and high-throughput execution infrastructure for AI agents, code generation tools, and enterprise evaluation workflows. The platform is built for organizations adopting agentic systems and in need of fast, reproducible, and scalable execution environments for development, testing, and automated assessment.
Runloop is offered as both a fully managed cloud service and an optional deployment inside your AWS VPC. This flexibility allows enterprises to choose the operating model that best fits their security, compliance, and integration requirements while maintaining a unified developer experience and performance profile across both modes.
At the core of the platform is the Devbox environment, a secure micro-VM that executes code in isolation with sub-second startup and p95 command execution under 50ms. Devboxes scale to more than 30,000 concurrent instances, enabling large-volume agent rollouts, parallel test runs, and multi-trajectory workloads for advanced agentic systems.
Benchmarking & Evaluation
Runloop includes a fully integrated benchmarking system designed for accuracy, reproducibility, and large-scale automation. Benchmarks can be used to:
Evaluate agent reliability and behavior across versions, models, or configurations
Run full regression suites to identify performance drift or unexpected changes in agent reasoning
Compare model variants during development, fine-tuning, or vendor selection
Generate reproducible test results using isolated, deterministic execution
Drive reinforcement fine-tuning (RFT) workflows that require consistent environments for millions of trajectories
In addition to public benchmarks, Runloop supports custom benchmarks that reflect an organization's own domain, systems, and success criteria. Custom benchmarks can incorporate internal workflows, business logic, multi-step tasks, integration scenarios, or application-specific scoring functions, enabling highly targeted evaluation and continuous improvement of AI agents in real production contexts.
Built for Engineering and Evaluation Teams
The platform simplifies operations by eliminating the need for enterprises to design and maintain their own sandboxing systems. Runloop provides reproducible Devbox environments, extremely fast startup times, stable execution, and built-in tooling for automated evaluation at scale. Teams use Runloop to develop, test, and deploy AI agents with confidence across cloud or VPC environments.
With its combination of high-performance Devbox execution, integrated benchmark capabilities, and flexible deployment options, Runloop enables organizations to scale AI agent development and evaluation securely, efficiently, and with full operational control.
Highlights
Deploy secure, isolated ephemeral development environments (Devboxes) in sub-second startup time. Scale to 30,000+ concurrent instances for large-volume agent rollouts and multi-trajectory workloads, ensuring fast, reproducible execution with p95 command execution under 50ms.
Leverage robust developer tooling built for engineering and evaluation teams. Choose a fully managed cloud service or an optional deployment inside your AWS VPC, maintaining a unified developer experience and performance profile while meeting all your security and compliance needs. Simplify operations and eliminate custom sandboxing.
Utilize a fully integrated benchmarking system for accuracy, reproducibility, and workflow integration. Run full regression testing suites to prevent performance drift, compare model variants, and drive high-volume reinforcement learning (RFT) workflows. Supports public and custom benchmarks for evaluation in production contexts.
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You pay only for what you use across nine independent metering dimensions, with no minimums. Compute costs cover Devbox CPU, Devbox Memory, and Blueprint Build usage. Storage costs are metered separately for Object Storage, Snapshot Storage, Blueprint Storage, and Devbox Storage. Active Axons meter your agent coordination event streams. A Subscription dimension covers your plan allocation. Each dimension bills on its own unit, so your total scales with actual consumption in each category. You can grow usage in one area without affecting the others.
Top-of-mind questions for buyers
What counts as one Active Axon for billing purposes?
An Axon is a distributed event stream that sequences, records, and observes your agent interactions in real time. You pay per axon-hour while it stays active. Axons that are not running move to inactive storage, which meters separately by the gigabyte per month.
Am I charged for Devboxes that are suspended or not actively running?
Compute charges for Devbox CPU and Devbox Memory apply while a Devbox runs. When you suspend a Devbox, you stop paying for its active compute. Its saved state still consumes Devbox Storage, which meters separately by the gigabyte, so storage charges continue.
Which dimensions usually drive most of my bill?
Compute charges for Devbox CPU, Devbox Memory, and Blueprint Build tend to dominate for active agent workloads. The storage dimensions—Object, Snapshot, Blueprint, and Devbox Storage—meter by the gigabyte and add up for large or long-retained data. All charges bill independently and appear together.
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