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

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

Cadenya

Paid

A managed agent runtime that layers your tools, agents and objectives so teams can test agent behavior safely and iterate fast.

Unified Tool Layer: Connect MCP servers, OpenAPI specs and existing endpoints once, and expose them to every agent through a single managed interface.
Model-Agnostic Variations: Set a default model and run canary variations on other providers side by side to compare behaviors before promoting a change.
Progressive Tool Discovery: Tool schemas stay out of the context window until an agent asks for them, with configurable max tools per search, search hints and a rerank threshold, so every request gets smaller.
Live Token Metering: Track cost as it accrues across loops, active variations, memory entries and widgets, so usage is visible rather than discovered on the invoice.
Webhooks and SSE Streaming: Push agent events — assistant messages, tool results, approval requests, sub-agent spawns, compaction, timeouts — into your own apps in real time.
Memory Layers: Attach stored documents such as playbooks and policy sets to an agent so its guidance persists across objectives.
Outcome Feedback Scoring: Collect scored comments on each objective, attributed to the variation and model that produced it, to see which behaviors actually work.
Embeddable Widgets: Drop an agent experience into any frontend as a widget rather than building the conversational surface yourself.

Operational Exception Handling: Run an agent that detects stalled shipments or orders and reroutes them through your dispatch API within policy.
Safe Model Migration: Evaluate a new frontier model as a canary variation against live objectives before switching the default.
Wrapping Existing APIs: Turn internal OpenAPI endpoints into agent-callable tools without rewriting the services behind them.
Embedding Agent Chat in a Product: Ship a conversational agent surface into an existing frontend using widgets instead of building it in-house.
Cost Control at Scale: Use progressive discovery and live metering to keep context size and per-loop cost down as agent traffic grows.
Agent Quality Review: Compare scored feedback across variations to understand which prompt or model changes improved real outcomes.

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.