Jellyfish vs. Span: Engineering Intelligence Comparison

Jellyfish and Span measure how engineering teams build software and whether AI tools improve the results. Each one collects and interprets that data differently, so they suit different needs.

Jellyfish puts engineering data in the context of business priorities. Teams can see where delivery slows down and whether AI investments pay off. The same platform supports planning for upcoming quarters and established R&D financial reporting.

Span has grown into a full engineering intelligence platform, with analytics, DevFinOps, and developer experience tools. It’s best known for agent observability, where traces connect each AI coding session to the code and outcomes it produces and show teams which workflows are inefficient.

• Choose Jellyfish if you want to improve engineering performance organization-wide, optimize workflows in your AI-native SDLC, and need trustworthy data for planning, investment, and delivery decisions.
• Choose Span if you want to see how coding agents operate and learn which prompts, workflows, or development environments lead to better results.

How do Jellyfish and Span Compare?

What are the Key Differences Between Jellyfish and Span?

Platform Focus

Both platforms connect engineering activity to outcomes. Jellyfish uses that data for company-wide delivery and investment decisions, while Span studies how individual AI coding workflows perform.

Jellyfish

Jellyfish measures the work of developers, AI assistants, and autonomous agents in one set of metrics and connects it to delivery, quality, and cost.

It also maps engineering effort to initiatives and investment categories. CTOs, product, and finance teams can then plan capacity and AI budget around company priorities.

Span

Span launched an AI code detection model in 2025 and has since grown into a wider engineering intelligence platform. Its analysis starts with agent traces, a record of each AI coding session.

Span links those sessions to the pull requests and production outcomes they produce, then points to where AI workflows break down and which fixes improve results.

Best fit → Jellyfish for decisions on engineering performance and investment across the organization. Span to study individual AI coding workflows and improve them.

AI Adoption and Agent Visibility

Span and Jellyfish measure AI coding tool use at different levels. Span studies what happens inside each agent session, while Jellyfish compares adoption and its effect on delivery across teams and tools.

Jellyfish

Jellyfish looks at AI adoption from three angles:

• Per-tool views, including dedicated Claude Code and Cursor dashboards, show adoption by team and role next to cycle time, throughput, and code quality.
• A vendor-neutral comparison measures Copilot, Devin, CodeRabbit, and other tools on the same metrics.
• AI Cohorts group developers by how they use each tool, so organizations can expand the habits of their strongest teams.

Span

Span estimates the share of AI-written code by team and repository. Its agent traces record the prompts, model choices, tool calls, and file edits from AI coding tools such as Claude Code, Cursor, and Codex.

Span also groups related sessions on the same task, including dead ends that never led to a pull request.

Best fit → Span offers more detail on individual agent sessions. Jellyfish’s team-level view gives organizations a clearer basis for decisions on which tools to fund and where to expand AI adoption.

AI Spending and ROI

Span shows what individual agent sessions cost and which workflow changes could lower that cost. Jellyfish looks at total AI spend and whether it improves delivery and frees up engineering capacity.

Jellyfish

Jellyfish brings token spend across AI tools, teams, and models into one view, reconciles API-reported costs with telemetry data, and tracks spend against PR throughput over time.

Spend-to-work attribution connects that spend to the initiatives, deliverables, and roadmap areas it supports. Capacity metrics (adjusted for headcount) then show how much engineering time AI frees up.

Span

Span’s traces put a cost on each agent session, and its dashboards follow spend by team, tool, and model to point out expensive defaults. It also maps token spend and engineering hours to the projects and work types they support.

Best fit → Span helps teams lower the cost of individual agent workflows. For engineering leaders who report AI returns to finance and the board, Jellyfish connects spend to delivery, capacity, and roadmap priorities.

Developer Experience

Jellyfish and Span collect developer feedback through research-backed surveys and compare it with engineering data. Jellyfish scores each team over time, while Span pairs surveys with pulse checks and AI readiness questions.

Jellyfish

Jellyfish DevEx shows survey results next to delivery and AI data, so teams can see whether the slowdowns developers report also appear in DORA, SPACE, or system metrics.

Each team gets a DevEx Index score with benchmarks against other teams, roles, and industry peers. AI recommendations then come with actions that teams can track from one survey round to the next.

Span

Span’s surveys measure developer sentiment and AI readiness, with pulse surveys for periods of change and anonymous responses when appropriate. It connects that feedback to engineering performance data and industry benchmarks to help leaders identify areas for improvement.

Its agent trace evaluations also score how clearly developers prompt their agents.

Best fit → Span works well for pulse checks during change and a read on AI readiness. Jellyfish suits organizations that want a consistent DevEx score per team and recommendations they can track.

Industry Benchmarking

Jellyfish compares organizations with a broad set of engineering peers, while Span’s benchmarks focus more on AI-assisted development and agent practices.

Jellyfish

Jellyfish benchmarks every metric it tracks, from a single team up to the whole company. The peer set includes more than 1,000 engineering organizations and over 100 million pull requests.

Our October 2026 release expanded AI benchmarking. Teams can now compare AI adoption with those peers and see how their AI spend and efficiency measure up against the industry.

Span

Span compares teams with similar-size peers on stages such as time to first review, rework, and idle time, against an industry median.

Its AI benchmarks come from research on 103 teams over 12 weeks in 2026. The study ties clearer prompts to a lower cost per merged AI line, and better-prepared repositories to more merged AI code per developer turn.

Best fit → The choice depends on the goal. Span’s research helps teams improve how developers work with agents, while Jellyfish shows how delivery and AI spend compare across a broader peer group.

Financial Reporting and R&D Capitalization

Jellyfish and Span both calculate capitalizable R&D costs from engineering activity. Jellyfish’s reports come with a SOC 1 Type II attestation, while Span extends the calculation to AI agent spend for R&D credits.

Jellyfish

Jellyfish DevFinOps calculates labor cost per project from tickets, commits, and reviews, with salary data from HR and roster sources. Finance can sort costs into capitalizable and non-capitalizable work, pull reports for any period, and document activity for R&D tax credits.

The package also carries a SOC 1 Type II attestation, an independent audit of the controls that affect customers’ financial reporting.

Span

Span attributes engineering hours and metered agent spend to projects, each with session records and a confidence tier. One record supports ROI, capitalization, and Form 6765 Section G for R&D credits.

Teams set capitalization rules, approve each cycle, and export reports for accounting. Span lists SOC 2 Type II, so finance teams should confirm which controls cover its capitalization numbers and how the calculations are documented.

Best fit → Span suits companies that want agent spend in their R&D credit claims. Jellyfish gives finance an established workflow with SOC 1 Type II assurance.

Data Collection and Security

Jellyfish works mainly from metadata in development tools, while Span’s agent traces record what happens inside each coding session.

Jellyfish

Jellyfish uses metadata from existing engineering tools to measure performance without needing access to source code. Organizations with stricter rules can run the Jellyfish agent on-premise and scrub sensitive fields before anything is sent.

Security controls include SOC 1 Type II and SOC 2 Type II attestations, AES-256 encryption at rest, and TLS 1.2 or higher in transit.

Span

Span’s agent traces can include the prompts developers write, along with the commands and file edits their agents make in Cursor, Claude Code, and Codex. Its AI code detection also reads the code in merged pull requests.

To protect that data, Span removes secrets such as API keys on the developer’s machine and again when traces reach its servers. Full prompts are visible only to the engineer who wrote them, and admins can switch off prompt or file capture entirely.

Best fit → Organizations that want prompts and code kept out of third-party systems will find Jellyfish easier to clear with security. Span suits teams that accept session-level capture for deeper AI workflow detail.

How do Jellyfish and Span Compare in Terms of Pricing?

Both platforms use custom quotes, though their pricing models differ. Jellyfish charges by seat and package, and Span charges per code contributor, with dashboard-only viewers free.

Jellyfish Pricing

Jellyfish offers custom pricing based on the number of seats a company needs and the packages it selects:

• AI Impact: Helps teams understand how developers use AI coding tools and whether those tools are improving productivity. It also provides visibility into AI spending so companies can evaluate the return on their investments.
• Developer Productivity: Includes everything in AI Impact and adds broader engineering intelligence capabilities. Teams can measure engineering performance, compare results against industry benchmarks, and identify opportunities to improve how they work.
• DevFinOps: Helps engineering and finance teams work together on R&D financial reporting. It automates software capitalization and supports tax credit reporting, with SOC 1 Type II controls and audit-ready documentation.

Jellyfish doesn’t publish prices, so the final number comes from a sales conversation.

Span Pricing

Span charges per code contributor. Here’s how its pricing works:

• A contributor is anyone who commits code or is tracked in connected tools like GitHub and GitLab.
• People who only view dashboards, such as product managers and executives, aren’t charged.
• Every module on Span’s pricing page shows “request pricing,” so the exact cost comes from a quote.
• Span’s AWS Marketplace listing gives a reference point, $540 per active contributor for a 12-month contract. That equals $45 per contributor per month, the same rate Span lists on its Jellyfish comparison page.

Note → Compare quotes on scope as well as price. Span’s per-contributor rate and Jellyfish’s per-seat packages don’t necessarily count the same people. A request limited to AI agent traces will also produce a very different quote from one that includes planning, DevEx, and financial reporting.

User Reviews and Customer Feedback

On G2, Jellyfish has a 4.5 out of 5 rating from 430 reviews. Span’s 4.6 comes from only 13, which is an encouraging score but a much smaller sample.

Jellyfish

For a G2 user with time on the platform, Jellyfish’s main benefit is support for planning and leadership discussions:

“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.”

Loadsmart, a freight technology company, uses Jellyfish to decide which AI tools to scale. Its data showed flat cycle times in a GitHub Copilot pilot and faster cycle times and reviews with Cursor, so Loadsmart chose Cursor company-wide. Jellyfish also pointed out engineers using top-tier models for simple tasks, and new guidelines cut token spend without slowing delivery.

Ron Ben Yosef, Loadsmart’s CTO, puts it this way: “I don’t see Jellyfish as a reporting tool. Jellyfish is a decision-making tool.”

Report filters could be a little smoother, though, according to a G2 user who often adjusts several at a time:

“The filters start to load reports as soon as I change them, but sometimes I need to adjust multiple filters, and there is an unnecessary loading that slows down me getting to the final report I want.”

Span

Span’s early G2 feedback mentions ease of use and a responsive team, and this user relies on its data across the organization:

“The tool is easy to use, the team is incredibly responsive, and the data we get is invaluable. We use it across our organization to inform allocation, velocity, and performance.”

Setup took more effort than expected for another G2 user, mainly when connecting service integrations:

“The service integrations can be somewhat difficult to configure, and the setup process isn’t always as straightforward as I’d expect.”

Which Platform Fits Your Engineering Priorities?

Span and Jellyfish look at AI-assisted development from different distances. Span studies individual agent sessions and the environments around them, while Jellyfish connects AI activity to delivery, capacity, and investment across the organization.

• Span fits organizations that want to see how AI agents complete coding tasks, session by session. Its traces and environment scores help platform and AI teams improve the prompts, setups, and workflows their agents depend on.
• Jellyfish is the stronger option when AI results have to inform planning, delivery, and investment decisions across engineering. It ties adoption and spend to cycle time, capacity, and R&D reporting, so engineering and finance can agree on where the next budget goes.

For organizations that need to prove AI’s business value while improving how their teams operate, Jellyfish is a good place to start.

Request a personalized demo to see it with your own data, or click through the interactive product tour.

FAQs

Can Jellyfish and Span work with existing Jira and GitHub workflows?

Yes. Both platforms connect to Jira and GitHub without changing how teams plan or ship work. Before setup, confirm that every active repository is connected and that sandbox or archived repos are left out.

Both also classify work from code and ticket activity when links are incomplete, though cleaner Jira fields still improve accuracy.

Can Jellyfish and Span automatically generate reports for engineering leaders?

Yes. Jellyfish Assistant gives engineering leaders plain-language answers across delivery, planning, allocation, and AI data. Report Builder produces editable executive reports on AI adoption and impact.

Span’s Src agent answers questions in Slack or the app and sends scheduled updates through playbooks, such as a Monday brief and board summaries.

What should organizations expect when implementing Jellyfish or Span?

Both start with connections to tools such as Jira and GitHub, followed by an import of historical data. Jellyfish can also connect through an on-premise agent and comes with dedicated onboarding and professional services.

Span asks for read access to repository code, and agent trace collection also involves a security review and a hooks package installed on developer Macs.