Quick answer
Larridin is an AI impact intelligence platform. Larridin Developer Intelligence is designed for engineering teams as they move to an AI-native software development lifecycle and it covers telemetry and optimisation across the entire lifecycle. It measures velocity, quality, efficiency and the actual output of your engineers and your AI agents. It then helps you act: spread the good habits of your best AI users, reduce AI cost via the Larridin Router and be ready for a world where a huge amount of software is being written without a human at the keyboard.
Jellyfish is an engineering management platform that models where engineering effort goes and connects AI adoption and spend to delivery metrics.[2][3] Jellyfish answers the question “Where did our engineering effort and AI spend go?” Larridin answers “What did our engineers and AI agents build, how well are they working together, what did it cost and what should we change?”
Why teams choose Larridin Developer Intelligence
- We measure the work. Engineering Output scores each merged pull request on complexity, size and other factors, separates human, AI-assisted and agent-authored work, and builds code quality into the score.[9][14]
- Agent effectiveness from the sessions themselves. Larridin goes through the coding agent sessions and scores how well the engineers are working with the agents. It then takes the practices of your best AI users and turns them into guidance that the rest of the team can follow.[10][11]
- Agent cost and where the agents are spending their time. Sessions, work patterns, token usage and cost are all in one view so you can see how much of the agent spend is going towards feature work, bug fixing, research and testing.[10]
- Every AI dollar across the enterprise. Larridin pulls AI spend from coding tools, cloud models, LLM gateways, employee built agents, desktop apps and invoices and then attributes it to teams, agents, projects and workflows.[13]
- It lowers AI cost. The Larridin Router chooses the model for each coding request. Jellyfish has no router.[8][9]
What Jellyfish does well
Jellyfish’s allocation reporting and DevFinOps (including software capitalisation) are very much in use with engineering and finance leaders.[2][3] It has one of the largest engineering datasets in the space with over 100m pull requests from over 275k engineers and benchmarks across over 1300 companies.[4][7] It integrates with a wide range of planning tools, code hosts and AI coding tools and agents[2][5], runs developer experience surveys and provides enablement insights which are derived from the behaviour of power users.[2] It has SOC 1 Type 2 and SOC 2 Type 2 reports.[6] Its origins are actually in measuring where engineering effort goes rather than in measuring and optimising what AI agents actually produce.
The key difference: what was built, not just where time went
Output, not effort. Jellyfish starts from activity. Its patented Work Allocations Model turns signals from Git, Jira and similar tools into virtual time cards, with each engineer counted as one FTE. That shows where effort went, not what it produced.[3] Larridin’s Engineering Output scores each merged pull request from a semantic understanding of the code. When coding-agent transcripts are linked, AI spend is measured against that Output.[9][14] Larridin also has an effort view, Effort Allocation, which estimates where team time went.[12]
Agents, from the session. Jellyfish gets its AI signals from Git, planning systems and workflow data.[2] Larridin reads coding-agent sessions and scores each one on six dimensions, with the evidence for each score.[10] That shows how engineers work with agents, which tells you where to coach and what to change.[11]
Every AI dollar. Jellyfish tracks AI token spend across engineering teams by tool, team, model and initiative, with forecasting.[5] Larridin covers every AI source in the company, from code tools and cloud models to LLM gateways, employee-built agents and desktop apps.[13] The Larridin Router uses that spend to choose models. Jellyfish tracks spend by model but does not route.[8][9]
Example. Two teams each put two engineers on a payments initiative for a month, with the same AI spend. An effort view looks the same for both: 2 FTEs and the same spend. One team shipped a new checkout flow with tests. The other mostly patched bugs. Engineering Output shows the difference, and what each team got for its AI spend.
How Larridin and Jellyfish compare
| What matters | Larridin Developer Intelligence | Jellyfish |
|---|---|---|
| Built for | AI-native engineering teams and every other department using AI, in one platform. | Engineering organizations, with views for product and finance leaders.[2] |
| Where the data comes from | For Engineering Output, a semantic understanding of the work in code and coding-agent sessions. For Effort Allocation, metadata from calendar, pull requests, tickets, docs, agent traces and commits.[12][14] | Activity signals from Git, planning systems such as Jira and workflow data, plus AI tool usage and spend.[2][3] |
| Engineering output | Engineering Output scores each merged pull request on complexity, size and other factors, with code quality built in, and separates human, AI-assisted and agent-authored work.[9][14] | Effort modeled in FTEs from activity signals; AI usage linked to throughput, cycle time and code quality.[2][3] |
| Where engineering time goes | Effort Allocation, from the WorkGraph. Estimates each team’s share of captured effort across seven work categories, classified by intent from metadata in code, tickets, calendar, docs and agent traces. Team-level only; no per-person view.[12] | Patented Work Allocations Model, splitting effort across initiatives and types of work.[3] |
| How agents are measured | Agent Effectiveness reads coding-agent sessions and scores how well engineers work with agents on six dimensions, with cited evidence for each score.[10] | AI signals derived from Git, planning systems and workflow data, with agent work shown beside human and AI-assisted work.[1][2] |
| Acting on it | Coaching suggestions and reviewable practice guidance, such as agent-guide changes and model comparisons, shared across teams.[10] | Enablement insights that learn from power users, with recommendations for AI rollout, and Jellyfish Assistant for questions.[1][2] |
| Agent cost and where agents spend time | Sessions, work patterns (feature development, bug fixing, research, testing and more), token usage and cost in one view.[10] | Spend and tokens by tool, team, model and initiative, and how AI shifts investment across roadmap, innovation and KTLO.[5] Cost by agent session and type of work is Not shown. |
| AI spend across the enterprise | Every AI source, from coding tools and cloud models to LLM gateways, employee-built agents, desktop apps and invoices, attributed to teams, agents, projects and workflows.[13] | AI token spend across engineering teams, with forecasting, plus DevFinOps and software capitalization.[2][5] Company-wide AI spend outside engineering is Not shown. |
| AI beyond engineering | Adoption, fluency and impact across Support, Sales, Marketing, Finance and Legal, alongside each department’s AI spend.[9][17] | Not shown. |
| Model choice and cost | The Larridin Router chooses the model for each coding request, and compares routed and pinned work on output, quality and cost.[9] | No router. Tracks spend by tool and model so teams can check a routing policy.[8] |
| Deployment | Cloud, hybrid or on-premises. | Cloud. An on-premises option is Not shown. |
| What to know | Not a developer survey tool. Agent scores depend on which agents and sessions are captured, and measuring spend against Engineering Output needs coding-agent transcripts. | Effort is modeled from activity signals, not read from the code, and AI spend is attributed to tools, teams and initiatives rather than to what each pull request delivered.[2][3][5] |
Similar features, different names
| What you want to measure | Larridin Developer Intelligence | Jellyfish | How they differ |
|---|---|---|---|
| Engineering output | Engineering Output | FTE effort and delivery metrics | Larridin scores what was built; Jellyfish models effort |
| Where time goes | Effort Allocation | Work Allocations | Both estimate effort from activity. Larridin reports team shares of captured attention across seven intent-based categories, with no per-person view. Jellyfish models each engineer as one FTE across initiatives and work types. |
| Agent practices | Agent Effectiveness | Enablement insights | Larridin scores each coding-agent session; Jellyfish works from system signals and adoption data |
| AI spend | Token Spend & Insights | AI Token Cost Management | Larridin covers every AI source across the company; Jellyfish covers engineering teams |
| Model choice | Larridin Router | None (spend tracking by model) | The Router acts on every request; Jellyfish reports on spend |
What data Larridin collects
Larridin Developer Intelligence looks at Git metadata, coding agent session data and usage pulled from AI tool APIs. For effort allocation it also pulls in metadata from calendars, tickets and docs (again, never the message bodies, never the document contents, never the diffs). It doesn’t record keystrokes or take screenshots. Our SOC 2 Type 2 and SOC 3 reports, our external penetration test and data flow diagram are all available via the Larridin Trust Center.[12][15]
Recommendations by role
- CTO
- Larridin Developer Intelligence, to see what engineers and AI agents actually build across the SDLC and to steer the move to AI-native development.
- VP of Engineering
- Larridin Developer Intelligence to identify the agent habits of your best engineers and then turn those into team practice.
- CFO
- Larridin to see the AI spend against what was actually built rather than where the effort went, have all the AI spend across the company in one place and the Router to cut cost.
- CIO / Head of AI
- Larridin, to measure AI adoption, fluency and spend across every department not just engineering.
- Security-constrained teams
- Larridin Developer Intelligence (hybrid or on-premises), with SOC 2 Type 2 and SOC 3 reports and a published data flow diagram.
Moving from Jellyfish
Larridin Developer Intelligence doesn’t need Jellyfish or its data to get going. It simply connects to your Git hosts, your coding agents and your AI tool APIs. If you’re currently running Jellyfish then run a pilot on two teams, compare what each product is showing you against what Larridin is recommending and then make your decision with real numbers in front of you. If your finance team is currently reliant on Jellyfish’s allocation and capitalisation reports you can absolutely keep those in place as you do the comparison. Larridin will cover all the stuff that effort data simply can’t.
Questions to test in a pilot
- Compare effort with output. Take two teams that have a similar headcount and see if there’s a difference in what they actually delivered. Then ask your engineering leads which of the two views is the one that matches reality.
- Open an agent session. Check whether each product shows how the engineer worked with the agent in that session, and what they could have done better.
- Follow the money. Take a month of AI spend and for each product ask what that spend produced (including spend outside of engineering) and what it would be to get more for the same amount of money.
- Ask what to change. After the 30 day period has elapsed, then ask each vendor what they’d recommend you change and what that would save.
Proof
For measured findings across Larridin customers, see the AI Impact in Engineering Report, Q3 2026.[16]
Larridin data AI Impact in Engineering Report, Q3 2026 Read the reportFAQ
What is Jellyfish?
Jellyfish is an engineering management platform that models where engineering effort goes and connects AI adoption and spend to delivery metrics. It offers allocation reporting, DevFinOps, developer experience surveys and an AI Impact product.[2][3]
What are alternatives to Jellyfish?
Teams commonly evaluate DX, LinearB, Swarmia, Faros AI and Weave alongside Jellyfish. Larridin Developer Intelligence is another option for teams moving to an AI-native software development lifecycle.
Is Larridin a better fit than Jellyfish for AI-native engineering teams?
Yes. If you’re moving to AI-native development then Larridin is the better option. It measures what the engineers and AI agents actually build rather than where the effort went, it scores how the engineers work with the agents, it cuts cost via the Router and it covers AI across the whole organisation. Jellyfish is all about effort allocation and engineering reporting.[2][3][9][10]
How is Engineering Output different from Jellyfish’s effort model?
Jellyfish models FTEs from activity in Git, Jira and the other tools. Larridin’s Engineering Output scores each merged PR on complexity, size and all the other factors from the work itself. One tells you where the time went, the other tells you what was built.[3][14]
Does Larridin show where engineering time goes?
Yes. Effort Allocation from the WorkGraph works out each team’s share of captured effort across the seven work categories (Product Development, Production Incidents, Admin etc). It uses metadata from the calendar, pull requests, tickets, docs, agent traces and commits and is at the team level only (no individual level). It’s an estimate of the work that has actually been observed (just like Jellyfish’s allocation) rather than a time sheet.[12]
Does Larridin read the code, or just metadata?
Both of them for different jobs. Engineering Output scores each PR from a semantic understanding of the code itself. Effort Allocation uses metadata only (no message bodies, no document content, no diffs).[12][14]
Does Jellyfish have a router?
No. Jellyfish tracks AI spend by tool and by model so that teams can verify that a routing policy is in place. The Larridin Router selects the model for each coding request in order to save cost.[8][9]
Does Jellyfish track AI spend?
Yes. Jellyfish monitors AI token usage and spend across engineering teams by tool, by team, by model and by initiative with forecasting. It doesn’t measure spend against how much work each PR delivered and company wide AI spend outside of engineering is not shown.[5]
What data does Larridin Developer Intelligence collect?
Git metadata, coding agent session data and usage via AI tool APIs, plus calendar, ticket and document metadata for Effort Allocation (nothing at all on message bodies, document contents or diffs). No keystroke logging, no screenshots. Security reports and a data flow diagram are available at trust.larridin.com.[12][15]
Can we switch from Jellyfish to Larridin?
Yes. Larridin connects directly to your Git hosts, your coding agents and your AI tool APIs so you can run a pilot across two teams with absolutely zero Jellyfish data.
Sources and method
Written by Larridin using Jellyfish’s public website, press releases and documentation and Larridin’s own product pages. This is a documentation review rather than a hands on benchmark of Jellyfish. Where Jellyfish’s pages don’t describe something we’ll simply state “Not shown” and that won’t be treated as missing. The Jellyfish facts were reviewed on 10th November 2026. If you spot an error please email support@larridin.com and we’ll fix the page within 3 business days.
- Jellyfish, “Jellyfish Advances Developer and Agent Productivity Insights for the AI-Native SDLC” (October 7, 2026): prnewswire.com/news-releases/jellyfish-advances-developer-and-agent-productivity-insights-for-the-ai-native-sdlc-302901000.html
- Jellyfish AI Impact: jellyfish.co/platform/jellyfish-ai-impact
- Jellyfish, Work Allocations Model: jellyfish.co/blog/work-allocations-model-for-efficient-engineering-teams
- Jellyfish, 100 million pull requests milestone (September 24, 2026): jellyfish.co/newsroom/jellyfish-surpasses-100-million-pull-requests
- Jellyfish AI Token Cost Management: jellyfish.co/platform/jellyfish-ai-impact/ai-token-usage-monitoring
- Jellyfish Trust Center: trust.jellyfish.co
- Jellyfish AI Engineering Trends: jellyfish.co/ai-engineering-trends
- Jellyfish, AI token cost management guide: jellyfish.co/library/ai-token-cost-management
- Larridin Developer Intelligence: larridin.com/developer-intelligence
- Larridin Agent Effectiveness: larridin.com/developer-intelligence/agent-effectiveness
- Larridin, Agent Effectiveness Score: larridin.com/measurement-guide/agent-effectiveness/agent-effectiveness-score
- Larridin Effort Allocation: larridin.com/developer-intelligence/effort-allocation
- Larridin Token Spend & Insights: larridin.com/tokens-spend-insights
- Larridin Engineering Output methodology: larridin.com/developer-intelligence/methodology
- Larridin Trust Center: trust.larridin.com
- Larridin, AI Impact in Engineering Report, Q3 2026: larridin.com/data/engineering-ai-impact-q3-2026
- Larridin platform overview: larridin.com
Not just engineering
Larridin is not limited to engineering. The same platform measures AI adoption, fluency, impact, and spend across Finance, Support, Marketing, Sales, and Legal, so leadership gets one view of what AI costs and what it produces across the enterprise.[9][17]