Polyaxon vs Runloop
Compare Polyaxon and Runloop across sandbox isolation, agent tooling, environment lifecycle, snapshots, evaluation, GPUs, and platform ownership.
Which platform fits
Choose Polyaxon when
The core problem is governing the complete AI workload lifecycle on organization-controlled Kubernetes.
Choose Runloop when
Software agents need isolated development computers, agent-specific integrations, and evaluation workflows.
Use both when
Runloop executes coding-agent work while Polyaxon schedules and records downstream AI workloads.
Capability comparison
This table describes product scope and operating responsibility. It is not a benchmark or a count of integrations.
Product boundary
Polyaxon
An AI workload and lifecycle platform with interactive access inside service runs.
Runloop
A platform for building and optimizing software-engineering agents using Devboxes, Axons, Blueprints, Snapshots, and Benchmarks.
Isolation
Polyaxon
Sandbox access inherits the service container boundary, runtime identity, mounts, connections, and network policy.
Runloop
Devboxes are isolated, ephemeral virtual machines intended to protect code, secrets, data, and connected systems.
Environment lifecycle
Polyaxon
Interactive sessions use scheduled service runs and the same lifecycle controls as other Polyaxon operations.
Runloop
Devboxes support ephemeral and stateful modes, including create, shutdown, suspend, resume, and snapshot flows.
Execution interfaces
Polyaxon
Python, CLI, SSH, PTY, tmux, process, and filesystem access operate inside the workload container.
Runloop
Python and TypeScript SDKs, CLI, dashboard, named shells, PTYs, SSH, files, tunnels, and command logs operate Devboxes.
Reusable environments
Polyaxon
Images, components, connections, presets, and init logic define reusable workload specifications.
Runloop
Blueprints build reusable images from setup actions or Dockerfiles; snapshots branch from an existing disk state.
Agent evaluation
Polyaxon
Evaluation workloads, artifacts, metrics, matrices, pipelines, and registries use the general Polyaxon run model.
Runloop
Benchmarks, scenarios, and orchestrated evaluations are product areas specifically aimed at agent performance.
Compute scope
Polyaxon
Jobs, services, GPU workloads, distributed clusters, pipelines, and sandboxes share Kubernetes scheduling controls.
Runloop
Devboxes offer selectable instance sizes and virtual workstation environments focused on agent code execution.
Best fit
Polyaxon
ML and platform teams operating heterogeneous AI workloads on Kubernetes.
Runloop
Teams building software-engineering agents that require managed computers, tool access, and benchmark loops.
When each platform fits
Choose Polyaxon when
- The primary outcome is reliable scheduling and governance for training, evaluation, services, data jobs, and distributed compute.
- Interactive work must share the same storage, GPU, image, and network configuration as a Polyaxon workload.
- Lifecycle metadata, artifacts, registries, and multi-cluster policy should stay in one general-purpose AI platform.
Choose Runloop when
- The main workload is a software-engineering agent operating Git repositories, build tools, browsers, and developer utilities.
- Virtual-machine isolation, snapshots, suspend/resume, network policies, and agent gateways are central requirements.
- The team wants agent-specific benchmarks and observability alongside the execution environment.
Using Polyaxon with Runloop
Runloop can host the agent's repository work, tool calls, and benchmark environment while Polyaxon receives approved code or images for GPU training, batch evaluation, pipelines, and registry workflows. The boundary should be explicit and observable.
- Separate agent workspace credentials from the credentials used to submit governed Polyaxon workloads.
- Promote immutable source revisions or images rather than relying on undocumented Devbox state.
- Link Devbox, benchmark, commit, image, and Polyaxon run identifiers in both systems.
Evaluation plan
Choose a representative agent task
Use a repository change that needs setup, command execution, file edits, network access, validation, and an inspectable result.
Exercise state and controls
Test blueprint startup, snapshot or suspend/resume, egress rules, secrets, parallel branches, logs, and cleanup.
Continue into the AI workload
Submit the resulting code or image to Polyaxon and assess queues, GPUs, artifacts, tracking, retries, and lineage.
Sources
Product capabilities change. Follow the linked documentation for current details.
Polyaxon sandboxes
Interactive process, filesystem, PTY, SSH, and workload-boundary behavior.
Polyaxon overview
Workload, scheduling, tracking, pipeline, distributed runtime, and registry scope.
Runloop overview
Devboxes, Axons, Blueprints, Snapshots, Benchmarks, and software-agent use cases.
Runloop Devboxes
Virtual-machine isolation, lifecycle modes, tools, instance choices, and SDK interaction.
Runloop Blueprints
Reusable images, Dockerfile builds, team sharing, and the distinction from snapshots.
Runloop Snapshots
Disk-state capture, rollback, fan-out, persistence, and cleanup behavior.
Runloop network policies
Controls for restricting Devbox egress destinations.
Compare against your requirements
We can map your current scheduler, tracking stack, storage, GPU policy, and migration constraints before you commit to a platform change.