Warclick

WARCLICK

Battle-tested engineering telemetry

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Warclick vs Jellyfish: Engineering Analytics Without the Blind Spots

See every commit on every branch — including the work your current tool can't see.

In a Warclick audit, 32% of one engineer's actual non-merge commits — 86 out of 265 — were invisible to Jellyfish.

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Read-only GitHub$5 / active engineer / month7-day no-CC trialSetup in 30 minutes
  • All branches captured — every push, every branch, deduplicated by SHA
  • Fair attribution — actor-first, squash-merge credit preserved
  • AI tool adoption included — recognized tool signatures from GitHub metadata, no extra integrations
WarclickJellyfish
Pricing$5 / active engineer / month, $20/mo minimum (publicly listed)$30–$50 / engineer / month, annual contracts*
Pricing transparencyListed on homepageQuote required, no public list price*
How you startSelf-serve GitHub install, 7-day free trial, no credit cardSales call → demo → quote → annual contract
Time to first dashboard~30 minutes after installMulti-week onboarding*
Branch coverageAll branches (every push to every branch via webhooks)32% of one engineer's commits invisible to Jellyfish in Warclick audit*
Commit attributionActor-first: credits the authenticated GitHub pusherStandard Git author/email; primarily tracks default-branch activity*
Squash-merge creditingAuthor keeps credit when their PR is squash-mergedCommit-level attribution can be lost or consolidated*
Branch double-countingEach commit counted once by SHAOne row per commit per branch (1.83× inflation observed in audit)*
AI coding tool adoptionHeuristic detection across ≥10 tools (Copilot, Cursor, Claude Code, Codex, Aider, others)AI Impact via vendor / IDE integrations (Copilot, Cursor, Gemini, Sourcegraph, Windsurf)*
Engineer-level leaderboardsYes, by default on every dashboardDeclined by AI Assistant on policy grounds; data limitations acknowledged when pressed*
Data scopeGitHub, read-onlyGitHub + Jira + HRIS + finance tools
Best fit10–50 engineer teams50–500+ engineer enterprises

* Source: G2, Vendr, Capterra (April 2026) · Public reviews (April 2026) · Warclick anonymized audit (April 2026) · Jellyfish AI Assistant (April 2026) · Jellyfish.co (April 2026)

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When Warclick and Jellyfish look at the same engineer, here is what each one sees.

CategoryWarclickJellyfish (unique)Only Warclick seesOnly Jellyfish sees
Non-merge commits on main16416400
Feature-branch-only commits10115860
Merge commits460460
Unique commits total3111791320

Based on a Warclick audit comparing one engineer’s activity in both tools over the same time window. Counts only; no person, company, date, or project identifiers.

Imagine sitting down for a 1-on-1 with a builder, looking at the wrong number. You're either over-praising work that didn't happen or under-recognizing work that did. Neither one ends well.

Can you actually rank your engineers?

Ask Jellyfish's AI Assistant for a leaderboard ranking your engineers by commits or PRs. It declines, framing the refusal as a privacy policy: ranking engineers head-to-head by output metrics, it says, "falls outside what I'm able to provide".

Press on whether the refusal is policy or data, and a different answer emerges. The assistant acknowledges the gaps:

"primarily tracks activity on the default branch"
"commit-level attribution can be lost or consolidated"
"does affect the accuracy of per-person commit counts"

Even setting policy aside, the data wouldn't support a reliable leaderboard. Feature-branch work is missing. Squash-merge attribution is partial. Per-person counts are, by Jellyfish's own AI, affected.

Warclick is built around the engineer view — Warriors, calendars, leaderboards. Every dashboard answers a question Jellyfish's own product won't. Not because we're cavalier about people, but because the data is complete enough to be honest: every commit on every branch, attributed to the authenticated GitHub pusher, with squash-merge credit preserved.

Verify directly: ask Jellyfish's AI Assistant about a top-10 engineer leaderboard, then ask whether it tracks all branches and squash-merge attribution. The pattern reproduces.

All-branch reality

Warclick captures every commit on every push, regardless of branch. We deduplicate by SHA so a single commit never inflates a count just because it lives on multiple branches. Squash merges preserve credit to the original author. The attribution model is actor-first: the authenticated GitHub pusher gets credit, fixing phantom-email and multi-account misattribution out of the box.

There's a name for measuring only the work that survived to main. It's called measuring the iceberg by the tip. Useful if you're a captain. Less useful if you're trying to coach the people doing the underwater work.

AI coding tool adoption: same outcome, different cost to get there

Both tools surface AI coding tool adoption. The question is what it costs you to get there — and which tools you can see.

Warclick reports recognized tool signatures from GitHub commit metadata, plus separate message-format hints. It requires no per-tool API keys but cannot see every use of AI or quantify coding effort. Delivery Leverage puts team output, merge time and review coverage alongside that evidence without claiming causation.

Pricing reality check

At $5 per active engineer per month, Warclick costs about 12.5% of typical reported Jellyfish per-seat pricing. Run the math against the quote you actually receive.

* Based on publicly reported ranges of $30–$50/seat/month (G2, Vendr, Capterra). Your actual quote may differ.

Warclick publishes its price on the homepage. Jellyfish requires a sales call to find out theirs. If you're evaluating tools on a tight timeline, that's a difference before either tool even runs.

Frequently asked

Is Warclick a Jellyfish alternative?
Yes — for teams of roughly 10 to 50 engineers who want all-branch visibility, fair attribution, and AI coding tool adoption insights without the multi-week implementation or $30–$50 per-seat-per-month price tag that public reviews report for Jellyfish (G2, Vendr, Capterra).
How is Warclick's commit count different from Jellyfish's?
Two structural differences. First, Jellyfish reports one row per commit per branch it appears on, so a commit merged from a feature branch into develop and promoted to main can show up three times. Warclick deduplicates by SHA — every commit appears once. Second, Warclick captures every push to every branch in real time. In a Warclick audit, 32% of one engineer's actual commits were invisible to Jellyfish — primarily feature-branch work that hadn't reached main and wasn't linked to a ticket. Counting a commit three times because it lives on three branches makes the number bigger. The work it represents stays the same.
Are there other Jellyfish alternatives or competitors I should consider?
Yes — LinearB, DX (now part of Atlassian), Pluralsight Flow, Swarmia, Haystack, and GitHub Insights all play in the engineering analytics category. Each makes different tradeoffs on price, branch coverage, integration footprint, and onboarding time. The honest read: Warclick wins decisively on cost-and-onboarding and on all-branch-with-fair-attribution; Jellyfish has a deeper enterprise integration footprint if you need Jira + HRIS + finance data unified in one platform.
Is Warclick really only $5 per active engineer per month?
Yes. $5 per active warrior per month with a $20/month minimum, publicly listed. "Active" means engineers who pushed commits, opened PRs, or reviewed code in the period — so if half your team is on PTO, you pay for half. No annual contract, no platform fee, no module-pricing layers, no sales call.
Can Jellyfish produce an engineer-level leaderboard?
Not reliably. Asked for one, Jellyfish's AI Assistant declines on policy grounds. Pressed on whether the refusal is policy or data, it acknowledges that the platform primarily tracks activity on the default branch and that under squash merges commit-level attribution can be lost or consolidated — both of which, by its own admission, affect the accuracy of per-person commit counts. Warclick produces engineer-level leaderboards by default because the dataset is complete enough to support them.

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