Navigara vs Jellyfish: how the two measure engineering throughput
Jellyfish answers where engineering investment went, in percentages and R&D dollars, by joining Git and planning data to roster and payroll records. Navigara answers whether throughput changed by scoring each merged change and comparing it to that same team’s pre-AI baseline. One is an allocation instrument for finance. The other is a performance instrument for the engineering leader. Teams that need capitalization reporting and teams that need a defensible AI ROI number are asking different questions.
What Jellyfish measures
Jellyfish reads Git, the planning system, and the people data. That last part is the distinguishing choice: by linking engineering activity to roster and payroll, Jellyfish can express engineering work in dollars rather than in engineering units.
The headline number is allocation. What share of the quarter went to new features, to keeping the lights on, to security work, to the platform. Expressed as a percentage of engineering investment, then as a dollar figure that finance can reconcile against actual spend.
That makes it strong on a specific set of jobs. R&D capitalization reporting. Board slides about investment mix. The conversation where a CFO wants to know what the engineering line bought this quarter. Jellyfish publishes AI spend attribution at the token level, reported by tool, team, or initiative. Per-developer views cover AI usage and impact rather than token spend.
It also reports at the individual level. Jellyfish’s product includes per-person views, and the company has published its own guidance warning against using engineering metrics to evaluate individual performance. Both things are true at once, and which one governs depends on the org installing it.
What Navigara measures
Navigara reads the repository and scores the change.
Engineering Throughput Value rates each merged change on what it was: a complex refactor, a performance fix, a feature unblock, a dependency bump. Three lines of code can be any of those, and an activity metric that counts the three lines cannot distinguish them.
The comparison point is the team’s own history. Navigara scores the repository back through the pre-AI window and reports the current quarter against that baseline, rather than against an industry average. An average carries limited information when your stack, team composition, and constraints differ from the average.
Measurement runs at team and repository level. There is no per-developer throughput view, which is a deliberate product decision. A leaderboard changes the behavior it observes within about two sprints, and the changes are usually smaller pull requests and deferred refactors.
Side by side
| Jellyfish | Navigara | |
|---|---|---|
| Primary data | Git, planning systems, roster and payroll | Git repository history |
| Headline number | Investment allocation percentages and R&D dollars | Throughput against the team’s own pre-AI baseline |
| Comparison point | Investment mix over time, industry benchmarks | The same team’s pre-AI window |
| AI spend attribution | Yes, token level, by tool, team or initiative | Token and tool cost measured against throughput change |
| Individual-level reporting | Yes, with published guidance against performance use | No per-developer throughput view |
| R&D capitalization | Yes | Not a published feature |
| Published pricing | None | $7 and $30 per developer per month, $4,500 one-off private benchmark |
Pricing is worth noting because of what it implies about the sales process. Jellyfish publishes no pricing, which, in this category, means an enterprise sales cycle with a discovery call before a number. Navigara’s Measure tier is $7 per developer per month for up to 30 developers, and Pro is $30 per developer per month, with a 14-day free Explore tier and a $4,500 one-off private benchmark. A 40-engineer org can price Navigara from the website this afternoon.
Where the two overlap
More than the positioning suggests. Both read Git. Both aggregate to team level. Both report on AI tool spend. Both are built for an engineering leader who has been asked a question by someone outside engineering and needs something better than a gut feeling to answer it.
If the question is “what did we spend engineering on,” Jellyfish is built for that question and has the payroll join to answer it in dollars.
If the question is “did our throughput actually change after we adopted AI tooling,” then that requires a scored history of what shipped before, which is what the baseline is meant to provide.
If the question you keep getting asked is the second one, connect a repository and the pre-AI window scores in the first pass.
Which one fits your org
Jellyfish fits an org that capitalizes R&D, has a finance partner who needs engineering expressed in dollars, already runs a mature planning system with reliable epic hygiene, and is comfortable with a tool that can report per person. The planning-data dependency matters: allocation accuracy tracks how disciplined your Jira is.
Navigara fits an org of roughly 20 to 100 engineers that adopted AI tooling in the last three years, needs a defensible number on whether it worked, and would rather not introduce individual reporting into a team already nervous about measurement. The repository history is the only required input, so the setup does not depend on anyone’s ticket discipline.
Both, occasionally. A 300-engineer org running capitalization through Jellyfish and a baseline comparison through Navigara is a reasonable configuration. They answer to different people.
What neither one does
Neither tool tells you whether the work should have been done. Navigara’s own research page states the limit directly: ETV measures what shipped, not whether what shipped was planned. Jellyfish’s allocation view has a mirror-image limitation: it reports where investment went without assessing whether the mix was correct.
That judgment stays with the engineering leader, which is roughly where it belongs. The instrument’s job is to make the work legible enough that the judgment can be argued in a room where nobody reads commit logs.
Talk to us if you want to see the baseline comparison run against your own history.
Frequently asked questions
- Is Navigara a Jellyfish alternative?
- For the AI ROI and throughput-baseline question, yes. For R&D capitalization, no, because Navigara does not publish a capitalization feature and Jellyfish’s payroll join is built for exactly that reporting.
- Does Jellyfish measure the ROI of AI coding tools?
- Jellyfish publishes AI spend attribution at the token level, reported by tool, team, or initiative. The cost side is well covered. The return side depends on having a pre-AI baseline to compare against, which is a separate measurement problem.
- How much does Jellyfish cost?
- Jellyfish does not publish pricing, so a number here would be invented. Expect an enterprise sales cycle. Navigara publishes its tiers at navigara.com/pricing.
- Does Navigara report on individual developers?
- No. Throughput measurement runs at team and repository level. The change-level score makes a large refactor visible as a large piece of work without ranking the people who did it.
- Can Navigara use our Jira data?
- Navigara integrates with Jira and Linear, as well as Git. The baseline calculation itself runs on repository history, so a team with inconsistent ticket hygiene still gets a usable comparison.
- Which one is faster to get a number out of?
- Navigara scores the historical window in the first pass after a repository connects, because the data is already in Git. Jellyfish’s allocation reporting depends on the planning system and the payroll join, which takes longer to configure and is worth more once configured.
