Kastra vs OpenComputer: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Kastra and OpenComputer — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Kastra

Kastra Labs Inc

Freemium

Kastra is the runtime authorization layer for AI: it decides in sub-milliseconds what agents, models, and tools are allowed to do before they act.

Sub-Millisecond Policy Decisions: Every prompt, tool call, and API request is evaluated in under a millisecond so enforcement never becomes the bottleneck.
Deterministic Policy Engine: Rules are written and evaluated deterministically, not by an LLM judge, so the same input always produces the same allow/deny.
Kastra Edge (Local Enforcement): A local enforcement component that runs next to the agent so decisions happen even without a network round-trip.
Cryptographic Audit Trails: Signed logs of every decision give security and compliance teams tamper-evident evidence of agent behavior.
Coding Agent Integrations: First-class hooks for Claude Code, Cursor, Codex, and OpenClaw let policies wrap the tool calls those agents already make.
Kastra Recon: Discovers what actions an agent actually attempts in a codebase or environment, so policies can be authored from observed behavior instead of guesses.
Zero Implicit Trust Model: Nothing an agent asks to do runs until it is explicitly allowed by policy, aligning agent access with zero-trust principles.

Guardrails for Coding Agents: Prevent an autonomous coding agent from dropping tables, force-pushing to main, or leaking secrets during long runs.
Enterprise Rollout Approvals: Give security teams a control plane before letting employee-facing AI agents access internal APIs.
Regulated-Industry Agent Deployments: Provide the auditable authorization trail required in finance, healthcare, or government agent pilots.
Multi-Agent Systems: Enforce per-agent scopes so a research agent can read data but only a deploy agent can trigger production changes.
Incident Forensics: Reconstruct exactly what an AI agent was allowed or blocked from doing after a suspicious action.

Digger

Paid

Deploy managed AI agents as persistent, always-on cloud VMs with steerable execution and permanent HTTP endpoints.

Persistent VMs: Always-on virtual machines with a full filesystem and OS access that survive restarts, so agent state is exactly where you left it.
Elastic Compute: Resize memory (1-16 GB) and vCPU while a VM is running to match the workload of the agent harness.
Instant Checkpoints: Snapshot any VM state to fork or roll back in seconds, recovering from bad agent runs without teardown.
One-Prompt Deploy: Paste a single prompt into Claude Code, Codex, or Cursor to install the CLI, log in, initialize, and deploy an agent end-to-end.
Permanent Agent URLs: Every deployed agent gets a stable HTTP endpoint reachable from Slack, webhooks, and cron jobs.
Steerable Mid-Run: Interrupt and redirect long-running agents without killing the session, keeping durable state intact.
Hibernate & Wake: Pause idle VMs to stop paying for compute and resume them instantly when the agent is needed again.

Shipping B2B Agent Platforms: Provide end users of your Lovable/Devin/Bolt-style product with per-user VMs that remember installed dependencies and files across sessions.
Long-Running Autonomous Tasks: Run overnight research, scraping, or refactor agents that need to persist context across many hours without a sandbox timeout.
Slack & Cron-Triggered Agents: Wire a permanent agent URL to a Slack app or cron so a team can invoke the same agent state from anywhere.
Rapid Agent Prototyping from an IDE: Turn a natural-language prompt inside Claude Code or Cursor into a live, invokable agent without provisioning infrastructure.
Safe Rollbacks for Autonomous Coders: Use checkpoints to fork an agent VM before risky changes and restore instantly if the agent breaks its environment.