sqlsure vs Orca by Stably

Detailed side-by-side comparison to help you choose the right tool

sqlsure

🔴Developer

developer-tools

A deterministic semantic checker that catches silently-wrong AI-generated SQL — double-counted joins, summed averages, exposed PII — in 0.1 ms before the query runs, with machine-actionable fixes.

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Starting Price

$0 (self-hosted)
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Orca by Stably

🔴Developer

developer-tools

Free, open-source Agent Development Environment (ADE) that runs multiple CLI coding agents like Claude Code, Codex, and Cursor side-by-side, each in its own isolated git worktree, tracked in one desktop app.

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Starting Price

Free
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Feature Comparison

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FeaturesqlsureOrca by Stably
Categorydeveloper-toolsdeveloper-tools
Pricing Plans1 listed plan1 listed plan
Starting Price$0 (self-hosted)Free
Key Features
  • • Deterministic semantic verdicts in ~0.1 ms, before the query runs — same SQL plus same rulebook always yields the same result
  • • Catches silent correctness bugs: FANOUT and CHASM join double-counting, summed averages (ADDITIVITY), semi-additive balance sums, undeclared/keyless joins, cross joins, re-weighted AVGs, and PHI/PII exposure
  • • Machine-actionable fix on every rejection, enabling agent self-repair loops (draft, check, fix, check, execute)
  • • Run any CLI coding agent (Claude Code, Codex, Cursor, Copilot, and more) side-by-side
  • • Isolated git worktrees per agent with fan-out prompts and merge-the-winner workflow
  • • WebGL terminal splits, VS Code-style editor, and embedded Chromium browser with Design Mode

sqlsure - Pros & Cons

Pros

  • ✓Deterministic, sub-millisecond judgments make sqlsure viable inside a per-query agent gate
  • ✓Zero-config rulebook derivation from existing dbt tests — no new metadata to author
  • ✓Machine-actionable fixes make self-repair loops work end-to-end, not just error out
  • ✓Fully offline with no telemetry and no database connection required
  • ✓External benchmark on Spider/BIRD (45 flags, 0 false alarms) is unusually credible for an OSS tool

Cons

  • ✗Coverage is nine rules — real correctness bugs outside those categories will still ship
  • ✗Requires a semantic layer (dbt tests, PK/FK, OSI, or MDL) — without one, sqlsure returns 'can't verify' for most cases
  • ✗PHI/PII rule matches on declared sensitive columns; unlabeled sensitive columns won't be caught
  • ✗Python-only runtime; teams on Node or Go stacks need a subprocess boundary
  • ✗Pre-1.0 project with a small maintainer team — support model is community-only

Orca by Stably - Pros & Cons

Pros

  • ✓Run any CLI coding agent (Claude Code, Codex, Cursor, Copilot, and more) side-by-side. This directly supports the primary workflow.
  • ✓Isolated git worktrees per agent with fan-out prompts and merge-the-winner workflow.
  • ✓WebGL terminal splits, VS Code-style editor, and embedded Chromium browser with Design Mode.
  • ✓Native GitHub and Linear task workflows plus AI-diff annotation.

Cons

  • ✗Plan prices can omit usage overages, model-provider charges, implementation work, taxes, or required seats.
  • ✗Features and limits can change by plan, billing interval, region, and release; verify current documentation.
  • ✗No native MCP support is documented in this record, so agent workflows may require API work or another integration layer.
  • ✗A representative pilot is necessary because a feature list does not establish reliability or operational fit.

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Review sqlsureReview Orca by Stably