Clockwork vs HyperProbe: Features, Pricing & Which Is Better (2026)

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

Clockwork

Vimox Shah

Freemium

macOS app that books coding agents like meetings — scheduled, sandboxed runs with dollar caps that file a readable report.

Calendar-style booking: Schedule an agent against a repository with a profile, budget and time, one-off or recurring
Real recurrence: RRULE or cron expanded in your own IANA time zone, with daylight-saving handling and missed-run policies
Sandboxed runs: Each job executes in a macOS Seatbelt profile around a fresh git worktree, or an ephemeral Docker container with no network by default
Credential deny-list: Files under .ssh, .aws and .gnupg plus shell history are unreadable from inside a run
Enforced budgets: A dollar cap, turn cap and wall-clock timeout applied by a supervisor outside the model
Approval gates: Risky actions pause and wait; an unanswered ask fails closed after about two minutes
Structured reports: Every run files a summary, branch and diffstat, cost, approval log and full-text searchable transcript
Agent library: Thirteen specialists including Dep Surgeon, Test Doctor, Bug Hunter, Code Reviewer and Security Auditor, plus custom agents
Multi-engine: Works with Claude Code, Codex CLI, OpenCode and Hermes Agent on the logins you already have, or your own provider keys

A developer schedules dependency patch bumps to run overnight and reviews a diff in the morning instead of a scrollback
A team runs a weekly repo health digest that reports stale branches, drift and advisories every Monday at 07:00
An engineer chains a scan, a fix, a test run and a pull request so each stage receives the previous stage's report
A maintainer caps an experimental agent at a fixed dollar amount so a runaway loop cannot exceed the budget
A CI failure fires a webhook that launches the CI Investigator agent to decide whether it is a flake or a real regression
A security-conscious team runs read-only audits on a machine with no cloud account, no telemetry and data kept in a local SQLite file

Freemium

AI-native production debugger that lets coding agents place read-only probes on live running code to capture variable state without redeploying.

Read-Only Live Probes: Places a non-blocking virtual breakpoint on a specific line in a running service and snapshots the live variable state there, with no code change, redeploy or restart required.
Automated Incident Workflow: Picks up alerts from PagerDuty, Datadog or Slack, reads logs and traces to locate the offending file and line, plans the debugging flow, probes, captures and delivers a confirmed root-cause analysis.
Coding Agent Integration via MCP: Ships an MCP server so Cursor, Claude Code, Codex and opencode can install the SDK, configure probes and read captures from inside the agent session.
Multi-Runtime SDK Coverage: Supports JavaScript, TypeScript, Java, Python and Ruby services, with one SDK install covering every instance a service runs on.
Probe Safety Controls: Every probe carries a time-to-live, a capture rate limit and a hit-expiry count, so it clears itself automatically and high-traffic lines stay bounded.
Default PII Redaction: Sensitive values are redacted by default on every plan, with custom per-field and per-file redaction rules available at the Enterprise tier.
Immutable Audit Log: Every probe placement and capture is recorded in a tamper-evident log, with approval gates and organization-level policy ceilings available for regulated teams.
Per-Service Pricing Model: Billing counts running applications rather than engineers, hosts or captures, so probes and captures are unlimited on every plan including the free one.

Shortening Time to Root Cause: Cut incident investigations that would take three to four hours of log-and-redeploy cycles down to minutes by capturing the value that explains the failure directly.
Debugging Non-Reproducible Bugs: Inspect live memory and variable state for race conditions and data mismatches that only appear under real production traffic and never reproduce locally.
Eliminating Debug Redeploys: Investigate a production failure without shipping temporary logging code, avoiding the risk and delay of extra deployments during an incident.
Reducing On-Call Load on Senior Engineers: Let agents run the evidence-gathering phase of an incident so senior engineers are not pulled off roadmap work for every page.
Confirming a Fix Was Actually Correct: Verify a hotfix against captured evidence rather than accepting an educated guess that leaves the same conditions able to fire again.
Instrumenting a Whole Stack Safely: Roll probes across many services under org-level policy ceilings, approval gates and per-namespace allow lists that a security team defines.