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Jellyfish

Engineering intelligence platform aligning software goals

Overview

Jellyfish.co provides a platform that aligns engineering work with business goals by delivering engineering intelligence from SDLC data. It surfaces where engineering effort is spent, improving planning, forecasting, and decision-making, and it streamlines engineering and product operations by removing bottlenecks and boosting predictability. It tracks progress on deliverables, reveals roadblocks, and helps teams make informed trade-offs to stay on schedule. It also monitors team health and performance using both quantitative data and sentiment to guide coaching and continuous improvement, typically via a subscription model.

Significant Headcount Growth

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About Jellyfish

Simplify's Rating
Why Jellyfish is rated
B-
Rated B on Competitive Edge
Rated B on Growth Potential
Rated C on Differentiation

Industries

Data & AnalyticsEnterprise Software

Company Size

201-500

Company Stage

Series C

Total Funding

$117.2M

Headquarters

Boston, Massachusetts

Founded

2017

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Simplify's Take

What believers are saying

  • Jellyfish says 700+ companies use it, including DraftKings, Keller Williams, and Blue Yonder.
  • June 2026 partnerships with Kiro and Codex expand AI-impact measurement across workflows.
  • March 2026 and May 2026 launches added Assistant, Data Hub, and AI Engineering Trends.

What critics are saying

  • GitHub, GitLab, and Atlassian can bundle similar engineering analytics into existing workflows.
  • Jellyfish’s AI metrics depend on customers adopting Copilot, Kiro, and other tools.
  • If AI productivity reporting commoditizes, Jellyfish becomes a dashboard vendor by 2027.

What makes Jellyfish unique

  • Jellyfish’s 2026 Gartner Leader status validates its developer productivity insight category leadership.
  • Its AI Impact platform tracks PRs, prompts, tokens, and code-review agents.
  • AWS Marketplace distribution and AWS Generative AI Competency simplify enterprise procurement.

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Funding

Total Funding

$117.2M

Above

Industry Average

Funded Over

4 Rounds

Series C funding is usually for startups that are doing well and are looking for more money to fuel major growth, such as acquiring other companies, expanding into global markets, or launching new product lines. Investors typically include larger venture capital firms and private equity.
Series C Funding Comparison
Above Average

Industry standards

$50M
$50M
$62M
$71M
$100M

Benefits

  • Professional Development Budget

Growth & Insights and Company News

Headcount

6 month growth

4%

1 year growth

4%

2 year growth

5%
SD Times
Oct 7th, 2026
Jellyfish advances developer and agent productivity insights for the ai-native SDLC.

Jellyfish advances developer and agent productivity insights for the ai-native SDLC. BOSTON, October 7, 2026 - Jellyfish, the leading Software Engineering Intelligence and AI Impact Platform, today announced a comprehensive suite of features to help organizations measure the impact of AI engineering tools. The features, launched during Jellyfish's inaugural AI Impact Week, empower engineering teams to go beyond measuring adoption to understand how their organizations are transforming with AI and whether those investments are delivering value for the business. "The software development process continues to be transformed by AI, and the industry's standard metrics, tools, and processes have struggled to keep pace," said Andrew Lau, CEO of Jellyfish. "Our platform makes it possible for engineering leaders to understand what's actually happening in the AI-native SDLC, from adoption to productivity to cost to ROI. Most importantly, it is designed to evolve with the industry and keep pace with every new tool, model release, and workflow." Where do I stand: new features to measure granular Usage. Jellyfish now ingest signals from across the engineering stack so leaders can see human work, AI-assisted human work, and fully autonomous agent activity side by side. * Lifecycle Explorer: A top-down view of where time actually goes across the AI-DLC. Helping engineering leaders avoid the most expensive mistake in AI investment right now - buying more code generation when review is the bottleneck. * AI Cohorts: Designed to segment and track how developers interact with generative AI coding tools including GitHub Copilot, Cursor, and Claude Code to help engineering leaders understand adoption and impact. * Metrics Explorer: Complete analysis across human contributors and fully autonomous agents, providing a clear, top-level picture of how AI is impacting overall velocity. Users can also create Custom Metrics by describing what they want in natural language and letting the platform build it. * Jellyfish Assistant and Agents: AI-powered chat interface that proactively surfaces insights, delivers trusted answers, and builds what you need to see based on each organization's engineering context. * Research Insights: In-context research insights within the Jellyfish platform allow users to compare AI use against more than 1,300 other companies on the Jellyfish platform. "Jellyfish's new AI features give us the ability to synthesize data that exists in Jira, Cursor, and GitLab repositories, combine all of that together and simply ask a question," said Bill Pawlikowski, VP of Engineering at Daxko. "The fact that we can ask a question and get an answer from that ecosystem has been mindblowing." Am I transforming: understanding how teams work with agents. Measuring throughput in an agentic world is now table stakes for engineering organizations. Jellyfish now helps leaders understand how their teams work with agents and optimize that activity. * Skill Adoption: Real-time tracking of which AI skills and practices are spreading across an engineering organization, which teams have them, and which teams are falling behind. * Behavioral Metrics: Designed to assess how well human engineers are working together with AI agents. Allows engineering leaders to understand which teams are compounding growth with AI and which are spinning their wheels. "We've rebuilt Jellyfish from the ground up," said Adam Ferrari, SVP Engineering at Jellyfish. "Rearchitected on an AI-native data foundation, our platform goes beyond engineering analytics to help R&D leaders deeply understand, measure, and transform their AI practice." What is it worth: tracking AI Cost and where investment is flowing. Many engineering organizations still struggle to track spending on AI tools due to differences in pricing models and spending reports. Jellyfish allows engineering organizations to view all AI spending in one place, including reconciliation between API-reported and telemetry-reported cost. * Token Usage and Spend: Track token use by tool and by model, allowing engineering leaders to see which teams are getting real leverage and scale accordingly. * Spend-to-work Attribution: Go beyond how much organizations are spending to track where AI spending is going - which initiatives, which deliverables, and which parts of the roadmap. * AI Cost Benchmarks: Allows organizations to compare spend, outcomes, and spend efficiency with hundreds of industry peers. * Total R&D Cost: See AI spend in the context of total R&D cost (People + AI), so the business can make informed decisions with the full scope of investment in mind. * AI Capacity: A measure of change in capacity using AI that normalizes output to headcount, feeding more accurate headcount planning in this new AI world. "More than two years into the AI revolution, R&D teams are still struggling to prove whether their AI investments are paying off," said Krishna Kannan, Head of Product at Jellyfish. "Engineering leaders don't need more reports, they need actionable insights. We built Jellyfish's suite of AI features to provide definitive answers on where and how teams are delivering value today."

PR Newswire
Oct 7th, 2026
Jellyfish launches AI impact tools to measure developer productivity and agent work in AI-native SDLC

Jellyfish, a software engineering intelligence platform, has launched new features to help organisations measure the impact of AI engineering tools. The updates, announced during the company's inaugural AI Impact Week, include lifecycle explorer capabilities, AI skill adoption measurements, and AI spend-to-work attribution. The platform now ingests signals from across the engineering stack, allowing leaders to view human work, AI-assisted work, and autonomous agent activity together. It helps engineering teams understand how they work with agents and optimise that activity. Jellyfish addresses the challenge of tracking AI tool spending by consolidating all AI expenditure in one place. The platform reconciles API-reported and telemetry-reported costs across different pricing models. The three-day AI Impact Week event features live demonstrations and discussions with leaders from AWS, Linear, IBM, Slalom, and CFGI. Jellyfish serves clients including DraftKings, Box, and Blue Yonder.

PR Newswire
Sep 24th, 2026
Jellyfish surpasses 100M pull requests and 90M prompts analysed from 275,000 engineers

Jellyfish, a software engineering intelligence platform, has analysed over 100 million pull requests from more than 275,000 engineers, alongside 90 million prompts and 150 trillion tokens. The company helps organisations measure the return on investment from AI coding tools and engineering spend. The platform's recent data shows open-weight model usage doubled over six weeks, with adoption concentrated amongst AI-mature companies. Jellyfish has achieved AWS Generative AI Competency and appointed Julie Neumann as chief marketing officer. Neumann previously served as CMO at Honeycomb and has experience across AI and developer tools. Companies including Box and LastPass use Jellyfish to monitor and optimise AI usage in their engineering operations. The platform runs entirely on AWS and is available through AWS Marketplace.

Jellyfish
Jun 4th, 2026
Jellyfish partners with Kiro to measure AI engineering impact.

Jellyfish partners with Kiro to measure AI engineering impact. Billy Robins Jun 04, 2026 AI-assisted software development is rapidly reshaping how engineering teams build, ship, and scale software. But as organizations accelerate adoption, many engineering leaders are still asking the same question: How do Jellyfish measure the real impact of AI on engineering performance? That's why Jellyfish is excited to announce its partnership with Kiro, an agentic development environment designed to help teams move faster and deliver high-quality software more efficiently. Together, Jellyfish and Kiro give engineering organizations the visibility they need to understand how AI-assisted development is influencing productivity, delivery velocity, and overall engineering effectiveness. Kiro customers want to understand the impact of AI in their development process. Jellyfish brings the core engineering insights that, combined with the Kiro usage analytics, enable its customers to quickly get to insight. - Joe Cudby, Principal Product Manager, Agentic AI Kiro supports developers across the software development lifecycle, helping teams streamline workflows, accelerate development cycles, and improve the developer experience. Through a deep integration with Jellyfish AI Impact, organizations can now connect AI adoption directly to measurable engineering and business outcomes. Moving beyond AI experimentation. Engineering teams are investing heavily in AI tooling, but adoption alone doesn't guarantee results. Leaders need to understand where AI is driving meaningful improvements - and where there's opportunity to optimize further. By combining Kiro's AI-powered development capabilities with Jellyfish's engineering intelligence platform, organizations can move beyond anecdotal feedback and gain a clearer picture of how AI impacts software delivery across teams. With Jellyfish and Kiro, engineering leaders can: * Understand AI adoption across the organization: Jellyfish provides near real-time visibility into how teams are using AI, helping leaders track adoption trends, usage patterns, and engagement over time. * Connect AI usage to engineering outcomes: Organizations can measure how Kiro influences key engineering metrics, including delivery speed, cycle time, code quality, and developer productivity. * Scale successful AI practices across teams: Jellyfish helps identify where AI-assisted workflows are driving the strongest results, enabling organizations to replicate effective behaviors and accelerate AI maturity across engineering. Building the future of ai-integrated engineering. As AI becomes embedded in modern software development, engineering leaders need more than isolated productivity gains - they need a system of intelligence that helps them evaluate, optimize, and scale AI-driven engineering practices over time. Together, Kiro and Jellyfish provide the visibility and insights organizations need to confidently lead in the next era of software engineering. To learn more about Kiro and Jellyfish AI Impact, or to see the integration in action, request a demo today. You can also explore Jellyfish's growing ecosystem of AI integration partners here. Billy Robins is Head of Partnerships at Jellyfish.

Jellyfish
May 26th, 2026
16 quarters at the top: Jellyfish continues to lead G2's Software Development Analytics Tools grid.

16 quarters at the top: Jellyfish continues to lead G2's Software Development Analytics Tools grid. Adlana Estephanian May 26, 2026 Jellyfish has once again been recognized as a Leader in G2's Summer 2026 Software Development Analytics Tools grid, marking its 16th consecutive quarter at the top. Beyond this incredible streak, Jellyfish is once again a Top 50 Development Product for 2026. While industry awards are a fantastic validation of its work, this sustained leadership position is really a story about its customers. It reflects their trust in Jellyfish to provide the visibility and actionable insights needed to align engineering efforts with overarching business goals, especially as AI rapidly reshapes the modern software development landscape. The numbers speak for themselves. Its priority is delivering measurable impact for engineering teams, and customer feedback is its ultimate barometer for success. Based on recent G2 reviews, the Jellyfish community is seeing real value: * 98% gave Jellyfish a 4- or 5-star rating * 98% believe Jellyfish is moving in the right direction * 93% said they would recommend Jellyfish * 93% are satisfied with Jellyfish support Hear directly from engineering leaders. Seeing its platform simplify workflows and drive confident decision-making is what motivates its team every single day. Here is a look at how its users are experiencing Jellyfish in the real world: Jellyfish continues to engage with customers even after onboarding and consistently makes improvements to the application without causing any friction. Over the time Jellyfish has been using Jellyfish, they've added multiple features I find useful and are proactive in gathering feedback to drive these improvements. It automates tasks that were previously manual, like pulling together actionable metrics and dashboarding for AI usage, simplifying my workflow. The incorporation of an AI chat feature and custom query mechanism helps me overcome dashboarding limitations and easily get the metrics I need. I also use Jellyfish to identify engineering bottlenecks, measure productivity, track DORA metrics, and automate data collection from GitHub, Jira, and AI tools, replacing its hard-to-maintain custom solution. - Daniel Crowley Rolling out AI tooling without measurement is just hope. Jellyfish gave Jellyfish the data to see what was actually working, where adoption was real, and how it translated into output across the org. On top of that, its accounting team relies on it to track and capitalize software development projects. It's hard to imagine running engineering at its scale without it. - Jim Turpin The DevFinOps capabilities and DORA metrics have changed the way Jellyfish operate as an engineering organization. Jellyfish solves its toughest challenges from labor capitalization and AI impact to synthesizing data across Jira, GitLab, and Cursor -delivering metrics that truly transformed its operations. - David Adiutori How G2 scores are calculated. G2's rankings are highly respected because they are rooted in authentic, real-time, and unbiased peer reviews. Products are categorized using a standardized methodology, and scores are determined by a mix of public online signals and verified user review data, specifically looking at: * G2's Satisfaction algorithms * User satisfaction * Review quality * Product growth (employee, web, and social) * Year-over-year trends Join the conversation. More than 700 organizations are currently using Jellyfish to transform how they operate. If you are one of them, Jellyfish want to hear from you! Your insights shape the future of its product. Head over to its G2 page to share your experience.f About G2. G2 is the world's largest and most trusted software marketplace. More than a million people annually - including employees at all Fortune 500 companies - use G2 to make smarter software decisions based on authentic peer reviews. Thousands of software and services companies of all sizes partner with G2 to build their reputation and grow their business - including Salesforce, HubSpot, Zoom, and Adobe. To learn more about where you go for software, visit www.g2.com and follow G2 on LinkedIn. Ready to try Jellyfish for yourself? Request a demo today to see why Jellyfish has been a G2 Leader for 17 quarters in a row. Adlana Estephanian is the Senior Customer Marketing Manager at Jellyfish.

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