Jellyfish vs. Weave: What Sets These Platforms Apart?

Jellyfish and Weave are both software engineering intelligence platforms that track developer productivity and the impact of AI coding tools. They differ in how they measure engineering work and in what organizations can do with the results.

Jellyfish looks at engineering operations as a whole. It connects the work of developers and AI agents to delivery and business priorities, so organizations can decide where capacity and budget should go.

Weave uses AI models to score the work engineers and coding agents produce, links those scores to quality and AI spend, and offers a model router to reduce AI coding costs.

• Pick Jellyfish if you need a clear read on how your engineering organization performs. It gives you the data to understand your AI-native SDLC, fix delivery problems, and maximize AI ROI.
• Pick Weave if you mainly want to know how much work developers and coding agents produce, down to the individual pull request, and want help to keep AI coding costs down via model routing.

Here’s how they compare across eight areas, plus pricing and customer feedback.

How do Jellyfish and Weave Compare?

What are the Key Differences Between Jellyfish and Weave?

Platform Focus and Measurement

Both platforms measure the work of engineers and AI agents, but from different starting points. Weave estimates the effort behind each code change, while Jellyfish connects that work to delivery and business priorities.

Jellyfish

Jellyfish collects data from the tools teams already use, like Git and Jira, and shows the work of developers and AI agents in one place.

From there, it shows how that work affects delivery and cost, and whether engineering time matches company priorities.

Weave

Weave gives every merged pull request a score in “expert hours.” The score estimates how long a skilled engineer would need to make that change, based on how complex and risky it is.

Weave’s AI model, Silk 1, scores every engineer and team the same way. Work outside the code, such as planning or mentorship, needs extra context to measure.

Best fit → Weave offers a consistent score for every code change, whether a person or an agent wrote it. Jellyfish gives organizations the context to connect that work with delivery, capacity, and business priorities.

AI Tool Adoption and ROI

Each platform pulls data from Claude Code, Cursor, GitHub Copilot, and other AI coding tools, with Jellyfish focused on delivery and Weave on AI cost per expert hour.

Jellyfish

Jellyfish AI Impact detects adoption automatically across coding assistants, review agents, and autonomous agents. Several views connect that usage to results:

• Dedicated Claude Code and Cursor dashboards that link each tool’s usage to cycle time, throughput, and PR velocity
• Drive Adoption, which follows how usage grows or drops off by team and role
• Impact Insights, with before-and-after comparisons for each tool and for AI as a whole
• AI Token Cost Management, which sets spend by tool and model against PR throughput

Together, these views point to the tools and workflows that justify more investment.

In October 2026, Jellyfish launched AI Cohorts, Skill Adoption, and Spend-to-work Attribution, which track how developers use each AI tool, which practices spread across teams, and which initiatives AI spend supports.

Weave

Weave’s Token Intelligence attributes AI spend to engineers and teams from provider billing, tool telemetry, and configured subscription costs. It also reports what share of output is AI-assisted.

For ROI, Weave divides attributed spend by expert hours of output to calculate a cost per output hour, which teams can compare across groups and over time.

Best fit → Weave gives a more granular view of AI cost against code output. Jellyfish suits organizations that want to link AI tools to team delivery and decide where adoption should expand.

Code Output and Quality

Weave scores each pull request and grades the quality and reviews around it. Jellyfish measures output and quality across the development lifecycle and connects both to what ships.

Jellyfish

Jellyfish tracks throughput, DORA metrics such as change failure rate, and quality data from integrations like SonarQube and PagerDuty. Workflow Optimization also measures the balance of AI- and human-written code and how agent PRs fare at merge.

Weave

Silk 1 scores each change by its downstream impact, so a three-line config edit that affects 40 files is scored on its full reach. Line counts and PR totals don’t feed the score.

Weave also tracks quality signals:

• Production incidents linked to related PRs and deploys
• Revert rates and code churn by repository and engineer
• Scores for review depth, plus the number of review rounds before merge

Best fit → Weave has the edge for code-level analysis. Jellyfish connects that work to what reaches production, for teams that judge output by delivery.

Delivery Metrics and Bottlenecks

Both track DORA and SPACE metrics. Weave pairs them with code-level analysis, while Jellyfish links delays to team capacity and priorities.

Jellyfish

Life Cycle Explorer shows how long work spends at each development stage and where issues wait idle. Teams can open any outlier to see where it went off track.

Workflow Analysis then puts those delays next to goals, allocations, and work in flight. This helps teams spot causes outside code and review, such as:

• Too many projects in flight for one team
• Maintenance work that takes capacity from roadmap priorities
• Overloaded engineers and gaps in support

Weave

Weave monitors deployment frequency, PR deploy lead time, deployment success rate, and mean time to recovery. It also measures review load per engineer and compares teams to find technical debt or process problems that slow delivery.

Best fit → When delays start in planning, staffing, or scope, Jellyfish helps trace the cause. Weave gives teams solid deployment and review metrics for slowdowns inside the code pipeline.

Developer Experience and Team Health

Weave guides coaching with each engineer’s output, quality, and AI usage, while Jellyfish pairs developer surveys with system data.

Jellyfish

Jellyfish DevEx combines validated surveys with DORA, SPACE, and system metrics. Each survey cycle produces:

• A DevEx Index score for every team
• Benchmarks against teams, roles, and industry peers
• AI recommendations for each team

Managers can then trace slowdowns to tooling or working practices and track whether fixes improve the score.

Weave

Weave’s engineer-level views show each person’s output, quality, and AI usage within their team’s context. The platform presents these views as a way to spot coaching opportunities and recognize top contributors. It also brings developer feedback into its view of where work gets stuck.

Best fit → Weave works well for managers who coach from individual output data. Jellyfish gives a fuller view of team health, since it combines what developers report with how work moves through the system.

Model Routing and Automation

Model routing is a Weave capability that Jellyfish doesn’t offer, so the two approach AI cost from different levels.

Jellyfish

Jellyfish works one level up, at the choice of AI tools. Teams can compare AI vendors side by side on cycle time, code quality, and developer feedback, then put more budget behind the tools that improve delivery.

Weave

Weave Router sends each coding agent turn to the cheapest model it expects to succeed, based on:

• Complexity of the turn
• Savings from a switch, against the cost of a cache rebuild
• Task progress, with escalation to a frontier model when needed
• Remaining quota on each subscription

Router works with Claude Code, Codex, and Cursor, so developers keep their current tools.

Best fit → For direct control over which model handles each coding task, Weave Router is the specialist option. Jellyfish informs the broader decisions about which AI tools to adopt and fund.

R&D Capitalization and Reporting

Both platforms produce software capitalization reports, but Jellyfish’s finance workflow has more depth.

Jellyfish

Jellyfish DevFinOps combines engineering efforts from Jira, Azure DevOps, and GitHub with roster, HR, and payroll data. From that data, finance teams get:

• Automated or self-service categorization of capitalizable and non-capitalizable costs
• On-demand reports with the cost and FTEs behind each unit of work
• R&D activity records that support tax credit claims

The DevFinOps package is also SOC 1 Type II compliant, which gives auditors a tested set of controls over the numbers.

Weave

Weave’s R&D Capitalization report identifies capitalizable work from PR output and project data, with a year-to-date view by team, repository, or project.

Best fit → Weave gives teams a code-based view of capitalizable work. Jellyfish is the more complete option when finance needs capitalization, tax credits, and audit-ready controls in one workflow.

How do Jellyfish and Weave Compare In Terms of Pricing?

Weave publishes prices for most of its plans, including a free tier, while Jellyfish prices every deal through a custom quote based on seats and modules.

Jellyfish Pricing

Jellyfish prices by seat, based on which of its three packages a team selects:

• AI Impact for AI adoption, token spend, and tool comparisons
• Developer Productivity, which expands AI Impact with DORA and SPACE metrics, industry benchmarks, DevEx surveys, and custom dashboards
• DevFinOps for capitalization, tax credit, and audit reports, with SOC 1 Type II compliance

Prices aren’t listed publicly, so buyers request a quote or speak with sales.

Weave Pricing

Weave’s platform has three plans:

• Starter, which is free and includes the output score, AI ROI, and org-level metrics
• Pro, at $50 per engineer per month, with individual and team stats, PR drill-downs, and the Wooly AI agent
• Enterprise, with custom pricing, security and compliance features, and custom contract terms

Router plans cost $10 per month for open-source models (Code Max), $100 per month for OpenAI, Anthropic, and open-source models (Code Boost), or a custom Enterprise price.

Note → Weave’s platform fee and Router plan are billed separately, so teams that want both should budget for each. Jellyfish quotes cover the packages a team selects, so the fairest comparison puts the same scope on both sides.

User Reviews and Customer Feedback

Jellyfish has a 4.5 out of 5 rating on G2 from more than 350 reviews and a 4.5 rating on Gartner Peer Insights. Weave doesn’t have independent reviews on either site yet, so its feedback below comes from customer stories it has published.

Jellyfish

For this G2 user, Jellyfish’s value grew over time, from visibility to planning and trade-off discussions across teams:

“As I have used Jellyfish more, I have found its biggest value is not just the visibility it provides, but how it supports better planning and leadership conversations.

It brings engineering delivery, team allocation, capacity, and investment priorities into one place. It’s especially useful for creating a shared view between engineering, product, PMO, and leadership teams.”

Precisely saw the same effect after it moved from legacy software to SaaS. Its engineering teams spent about 80% of their time on maintenance, and the company wanted to reverse that ratio.

With Jellyfish, teams used velocity data to forecast large deliverables and share capacity with product management. Sprint predictability rose from 55% to 76.5% within a year, and the team saved 32 hours a month that had gone into manual reports.

One G2 review says the navigation took some getting used to, but built-in shortcuts helped once the user settled in:

“There was a bit of a learning curve at first as I got used to the navigation, but once I understood where to find the information I needed, I could use favorites, bookmarks, and stars to quickly access the information I rely on regularly.”

Weave

Weave doesn’t have user reviews on G2 or Gartner Peer Insights yet. Its website does feature several customer stories, and three of them share specific results:

• Bytes AI, a five-engineer team, saw output rise about 30% six weeks after it opened Weave profiles to all engineers.
• OpenZeppelin estimates Weave saved about four weeks of engineering time on one underperforming project.
• Intellistack replaced manual monthly board reports with Weave, Jira, and Slack data.

All three results rely on Weave’s expert-hour score, which its documentation describes as a model estimate that needs “inspection and context.” Teams will want to check scores against the underlying pull requests before they use them in performance reviews or budget talks.

Engineering Output Matters, but What Happens Next?

Knowing how much work engineers and agents produce is a good first step, and Weave measures it in detail with an expert-hour score for every pull request. But the next step is to understand what that work achieves.

Jellyfish research on 12,000 developers found that the heaviest token users spend nearly ten times more tokens for about twice the throughput, so higher output can hide a poor return on AI spend.

Which platform fits depends on which of those steps an organization needs most:

• Weave works well for teams that want granular, code-level measurement. Its expert-hour scores, quality signals, and Router help them compare output across people and agents and keep AI coding costs under control.
• Jellyfish gives organizations engineering intelligence for decisions across the business. It connects AI adoption and spend to delivery, traces slowdowns to their causes, and helps plan capacity and future R&D investment from the same data.

Delivery dates and AI budgets eventually reach the board, and CTOs and VPs of Engineering need more than an output score to explain them. Jellyfish gives them that wider context in one platform.

Book a demo to see Jellyfish with your own engineering data, or start with the self-guided product tour.

FAQs

What are some alternatives to Weave?

Weave combines LLMs with its own machine learning models to score code output, so its measurement doesn’t depend on lines of code (LOC) or story points. Other engineering analytics platforms measure engineering work from different angles:

• Jellyfish connects engineering work to delivery, resource allocation, and AI spend, with planning, DevEx, and R&D capitalization in one platform.
• LinearB focuses on workflow automation, with policy-based PR routing and AI code review before merge.
• Swarmia combines DORA metrics and investment balance with developer experience surveys and AI impact measurement.
• Waydev measures AI adoption and its return, along with DORA metrics, code quality at merge, and cost capitalization.

The best option depends on whether a team wants to measure developer output, improve delivery workflows, or guide broader engineering decisions.

Can Jellyfish and Weave work with different development tools across teams?

Jellyfish integrates with GitHub, GitLab, Bitbucket, Jira, and Azure DevOps, making it suitable for organizations with mixed development environments. Weave also connects to engineering tools, including GitHub, Jira, and Linear. Buyers should confirm whether each platform supports their entire toolchain and can provide consistent reporting across teams.

How do Jellyfish and Weave protect sensitive engineering data?

Both platforms hold SOC 2 Type II attestations and offer access controls to protect engineering data. Jellyfish also holds SOC 1 Type II. Before choosing either platform, organizations should review what data it collects, who can see individual developer metrics, and whether its data storage policies meet their security requirements.