Jellyfish alternative
A Jellyfish alternative for one question: does your AI-generated code survive?
Software engineering intelligence platforms measure delivery and investment across your whole organization. Codelitics measures something narrower and harder: per AI coding tool, how much generated code actually ships, lasts, and earns back its cost. If that is the question you came to answer, this page is for you.
First, a fair word about SEI platforms
Jellyfish positions itself as a software engineering intelligence platform: it aggregates data from tools like Jira and GitHub to show engineering leaders how time is spent, how delivery is trending against DORA metrics, and what share of capacity goes to features, bugs, tech debt, and infrastructure. For large organizations that need to report engineering investment to a board, that is a strong, well-defined job.
Jellyfish has also built an AI Impact product that tracks AI tool adoption, usage, spend, and delivery outcomes, and analyzes pull requests to detect AI's footprint in code changes. So this is not a story about a platform that ignores AI. It is a story about a different unit of measurement.
The gap
Delivery dashboards answer "how fast." The AI-code question is "did it last."
A general SEI platform is organized around delivery and investment: throughput, cycle time, deployment frequency, allocation. AI Impact features extend that frame by correlating AI activity with the same delivery signals, usually derived server-side from pull requests and connected tool data. That tells you whether teams using AI are moving faster and where the spend goes.
It does not, by design, center on the durability of the AI-authored code itself. The question a lot of leaders actually have is downstream of throughput: of everything an AI tool generated this quarter, how much reached main, how much was still there 90 days later, and what each surviving, useful change cost. That is a survival-and-cost question measured over a window, not a point-in-time speed reading.
There is a reason it is rarely the headline. Measuring it well means watching AI-authored lines from the moment they are generated, through edits and reviews, into commits, and then across weeks of churn, attributed to the specific tool that produced them. That is what Codelitics is built around.
How Codelitics is built
Repo-local capture, vendor-neutral, organized around survival.
Four design choices separate the AI-code ROI layer from a broad delivery platform. None of them is a criticism of SEI tooling; they are simply what it takes to answer the survival question well.
On the developer's machine
A per-seat agent (Go CLI, AI-tool plugins, git hooks, local SQLite) records AI sessions, tokens, edit checkpoints, and commit attribution at the source. That is a finer grain than reconstructing AI's footprint from pull request metadata alone. Codelitics does not run in your CI pipeline.
Survival, not throughput
The core metric is Code Yield, a rolled product of Ship times Last times Matter, backed by survival rate and Code Half-Life. These track durability over weeks, not speed at a point in time.
Per AI tool, vendor-neutral
Yield is broken out by tool, so Claude Code, Cursor, Copilot, and the rest are compared on the same outcome basis rather than each vendor's own activity counter. See tool yield for the per-tool definition.
Cost per realized change
Spend (including tokens) is divided by changes that actually shipped and survived, not by raw output, giving cost per realized change. The verification tax of reviewing AI output is part of the denominator, not hidden.
On data handling: the dashboard connects through a GitHub App or GitLab OAuth and clones the repositories you put in scope to derive metrics. You control which repositories and tools are included, and every figure is exportable and traceable to how it was computed.
Side by side
Where a general SEI platform ends and the AI-code layer begins.
This compares Codelitics with a general software engineering intelligence platform on the dimensions that matter for the AI-code ROI question. It is not a scorecard; the two are built for different primary jobs.
| Dimension | General SEI platform | Codelitics |
|---|---|---|
| Primary question answered | Where does engineering time and investment go, and how is delivery trending? | Per AI tool, how much generated code ships, survives, and earns its cost? |
| AI-tool-level yield | AI Impact tracks adoption, usage, spend, and delivery outcomes by tool, derived from PR and connected-tool data. | Code Yield computed and attributed per tool from repo-local capture, organized around survival. |
| Survival and Code Half-Life | Not the central unit of measurement. | The central unit: survival rate and Code Half-Life over a time window. |
| Cross-tool neutrality | Vendor-agnostic across many AI tools and agents. | Vendor-neutral, with every tool scored on the same outcome basis. |
| Cost framing | AI spend tied to delivery outcomes for ROI reporting. | Cost per realized change: spend over code that actually shipped and survived. |
| Capture and install model | Server-side integrations with source control, issue tracking, and AI tools. | Per-seat agent on each dev machine (git hooks plus AI-tool plugins); dashboard clones in-scope repos. Not a CI integration. |
| Breadth | Broad: DORA, allocation, investment, and AI impact across the organization. | Deliberately narrow: the AI-code ROI layer, designed to sit alongside. |
Competitor capabilities above are drawn from Jellyfish's own product pages, including its AI Impact dashboard. If a detail there changes, treat the vendor's site as the source of truth.
The TCO angle
Total cost of ownership, but of the measurement itself.
There is a version of total cost of ownership that is often overlooked: the cost of the measurement layer, not the engineering it measures. A broad SEI platform is a significant, organization-wide commitment, often sold through sales with multi-system integration and a wide surface area. That is appropriate when you need delivery, allocation, and investment reporting for the whole R&D function.
But if the live question is narrower, say "is our AI coding spend producing code that survives, and which tool is best," then the relevant TCO is the cost and effort of answering that one question reliably. Buying a broad platform to extract a narrow answer is a different value calculation than adopting a focused layer aimed at exactly that answer.
This is why we frame Codelitics as complementary. Keep the platform that runs your delivery and investment reporting. Add the AI-code ROI layer where the survival-and-cost question lives. The honest TCO comparison is not platform-versus-platform; it is "broad analytics you already need" against "the specific AI durability number, traceable and exportable, that broad analytics was not built to center on."
Worked example
What the AI-code ROI question looks like with numbers.
The figures below are illustrative, not a measured benchmark or a real customer result. They show how the survival-and-cost view differs from a throughput view for the same team.
Example. Say a 30-engineer team runs two AI coding tools and merged 6,000 AI-authored changes over a quarter. A delivery dashboard might report throughput up and cycle time down, which looks like a clear win.
Now apply the survival view. Suppose 70% of those changes were still in main after 90 days (survival rate), and of those, 80% touched code that mattered to a shipped goal. Rolling Ship times Last times Matter gives a Code Yield well below the raw merge count: roughly 6,000 × 0.70 × 0.80, about 3,360 realized changes, not 6,000.
Put cost against the realized figure, not the raw one. If the two tools cost a combined 90,000 over the quarter, dividing by 6,000 implies 15 per change, while dividing by the 3,360 that survived and mattered implies about 27 per realized change. Same spend, very different unit economics, and the gap between the two tools is exactly what tool yield is built to surface.
Currency omitted on purpose; substitute your own. Percentages and counts are chosen to illustrate the method, not to assert a typical result.
The same method, applied per tool, is what the Claude Code, GitHub Copilot, and Cursor ROI pages walk through in tool-specific terms.
Why the cost side matters now
Metered AI pricing put the realized-cost question on the agenda.
The reason "cost per realized change" stopped being academic is that AI coding spend became variable and visible. GitHub Copilot moved to usage-based, token-metered billing on June 1, 2026, and power users were projected to see agentic bills jump 10x to 50x. When spend swings that much, dividing it by raw output is misleading; you need the denominator to be code that actually survived.
Budgets are feeling it. Fortune reported that Uber drained its full-year 2026 AI budget in four months. And the speed assumption that justifies the spend is itself contested: a METR controlled study found experienced developers were measured about 19% slower on real tasks while believing they were faster. That gap between perceived and realized value is precisely what a survival-based number is meant to close.
For the governance side of that spend, see AI spend governance, and for why a token dashboard is not the same as a yield number, see token dashboards versus yield.
Jellyfish alternative FAQ
What evaluators ask before they choose a layer.
- Is Codelitics a Jellyfish competitor?
- Not really. They do different primary jobs. A software engineering intelligence platform answers broad questions: where engineering time goes, how delivery is trending, how investment maps to the roadmap. Codelitics answers one narrow question that those platforms were not built around: per AI coding tool, how much AI-generated code actually ships, survives, and is worth what you paid for it. For many teams the two are complementary, not mutually exclusive.
- Can Jellyfish measure AI code ROI?
- Jellyfish has an AI Impact product that tracks AI tool adoption, usage, spend, and delivery outcomes by analyzing pull requests and correlating AI activity with PR metadata, reviews, and throughput. That is real AI measurement at the delivery layer. Codelitics is built around a different unit: the survival of AI-authored code over time, captured repo-locally at edit-checkpoint and commit granularity, expressed as survival rate, Code Half-Life, and Code Yield per tool. If your question is 'which AI tool produced code that is still in main 90 days later, and at what cost per realized change,' that is the gap Codelitics fills.
- Do I have to replace my SEI platform to use Codelitics?
- No. Codelitics is the AI-code ROI layer. If you already run an SEI platform for DORA, cycle time, and investment allocation, you keep it. Codelitics adds the durability and per-tool yield view those platforms are not centered on, and every figure is exportable and traceable to how it was computed, so it can sit alongside whatever reporting you already do.
- How is the install model different?
- Codelitics installs a per-seat agent on each developer's machine: a small Go CLI runtime, plugins for AI tools like Claude Code, Cursor, Codex, VS Code, and OpenCode, git hooks, and a local SQLite database. Capture happens on the developer's machine. A dashboard then connects via a GitHub App or GitLab OAuth and clones the repositories you put in scope to derive metrics. You control which repositories and tools are in scope. Codelitics does not run in your CI pipeline.
- Does Codelitics rank individual developers?
- The public, conformant Return on Code score is team-level and tool-level, and never ranks individuals. Individual-level views do exist, but they are opt-in, sit outside the conformant score, and are governed by your own policy (for example GDPR or works-council agreements). The headline number you would put in front of finance is about teams and tools, not people.
- What does Codelitics actually compute that a delivery dashboard does not?
- Code Yield, the rolled product of Ship times Last times Matter; survival rate and Code Half-Life for AI-authored code; cost per realized change; and tool yield, all attributed per AI tool. These are durability-and-cost measures over a time window, not point-in-time throughput or adoption counts. They answer whether the AI code you paid for is still doing work, which is a different question from how fast a team is shipping.
Comparing other tooling? See the Copilot analytics alternative page, or start from how to measure AI coding ROI.