Jellyfish
Engineering intelligence platform that turns developer tool data into AI ROI, delivery and developer experience insights.
By Jellyfish · 4.5/5 Value-Position score (estimate)
Positioning guardrails
Best for
- Engineering and R&D leaders measuring the adoption, spend and ROI of AI coding tools such as GitHub Copilot, Cursor and Claude Code
- CTOs and VPs of Engineering who need delivery, throughput and cycle-time visibility across hundreds of developers
- Finance and R&D operations teams automating software capitalization and R&D tax credit reporting
- Enterprises wanting vendor-neutral benchmarks against a customer base of 1,000+ engineering organizations
- Platform engineering teams comparing AI assistants and closing enablement gaps
Ideal size: 200-2,000+ engineers people · Enterprise or late-stage scale-up with structured SDLC tooling and clear engineering metrics ownership
Not for
- Teams of fewer than roughly 100 engineers without a platform or data function to run the tooling
- Buyers who want transparent self-serve pricing or a free tier - Jellyfish is quote-only
- Companies with no Git, Jira or CI/CD data to analyse
- Developers shopping for an AI coding assistant rather than measurement and analytics
Value metrics scorecard
Time-to-Value
Days to first AI insights; one sprint for trends
~7 days to first production value
Total Cost of Ownership
On request
Quote-based; priced by number of seats and the specific modules selected (AI Impact, Developer Productivity, DevFinOps)
Implementation Friction
2/5
Engineering + admin effort required
Value-Position score
out of 5 · model estimate
Full cost breakdown
Mandatory implementation fee
None
Seat tiers
No published seat tiers; seat count and modules are agreed with sales
Add-on costs
- Capability is selected a la carte as modules (AI Impact, Developer Productivity, DevFinOps), so anything beyond the base selection is paid for separately
Company & support
Who is behind Jellyfish, and how your team gets help once it is live.
Market position
Where Jellyfish sits against its closest alternatives. Pick any two of cost, speed, friction and buyer score, and up to 9 companies to compare.
Quadrant view
Typical annual cost × Time-to-value
The lines cross at the median of the solutions shown, so about half sit on each side of each line.
Companies on the chart 6 / 10
- Jellyfish
- HiBob
- Coda
- Tines
- Humaans
- LawVu
Add or change companies
Up to 10 companies including Jellyfish. Listed closest first.
Stack fit signal
Compatibility with standard B2B ecosystems.
No supported MCP path today, so it cannot be driven from an AI client.
AI & MCP readiness
What Jellyfish ships in AI, and what it asks of your ecosystem.
AI features shipped
Jellyfish applies AI to its own analytics: AI-native decision support turns engineering data into immediate answers, automated reports and proactive recommendations, and AI-powered queries drive custom dashboards. Its main AI story, though, is measuring customers' AI tool adoption, token spend and delivery ROI across assistants such as Copilot, Cursor and Claude Code.
Your data & models
- Trains on your data
- Never trains on your data
- Runs on
- Not recorded
- AI pricing
- Paid add-on
In your ecosystem
- AI connection
- Not supported
- Model key
- Vendor's key
- AI usage audit
- Not recorded
Compliance attestations
* IAPP AIGP certifies individuals, not products. It means named staff hold the credential — not that the platform does.
Bottom line
Jellyfish is a software engineering intelligence platform that unifies data from Git, Jira, CI/CD and AI coding tools to measure delivery performance, developer experience and AI ROI. Engineering and finance teams use it to prove the impact of AI assistants, automate software capitalization and R&D tax credit reporting, and benchmark against more than 1,000 peer organizations. Pricing is quote-based by seats and modules, with first insights in days and trend data after a sprint.
Frequently asked questions
How is Jellyfish priced?
Jellyfish does not publish list prices. Pricing is based on the number of seats plus the specific modules selected - AI Impact, Developer Productivity and DevFinOps - and is quoted by sales. Budget for a seat-based subscription plus whichever modules you adopt, rather than a credit-card self-serve plan.
How quickly will we see value?
Jellyfish states that most teams see AI adoption and spend insights within days, with delivery-impact trends emerging in the first sprint. Because it analyses existing Git and work data rather than asking teams to change tools, setup is fast and does not require heavy integrations, tagging or manual time tracking.
Do we have to integrate every AI coding tool to get insights?
No. Jellyfish derives AI signals directly from Git, planning systems and workflow data, so adoption and impact insights do not depend on integrating each assistant. It also publishes direct integrations for tools such as GitHub Copilot, Cursor, Claude Code, Amazon Q, Gemini Code Assist, Windsurf and CodeRabbit.
What compliance attestations does Jellyfish hold?
The Jellyfish Trust Center states the company maintains audited SOC 1 Type II and SOC 2 Type II attestations, runs an annual SOC 2 Type II audit with an accredited auditor, performs regular third-party vulnerability and penetration tests, hosts entirely in AWS, and encrypts data in transit with TLS 1.2+ and at rest with AES-256. No ISO 27001, HIPAA or FedRAMP claim is published.
Can Jellyfish support R&D tax credits and software capitalization?
Yes. The DevFinOps module automates software capitalization and R&D tax credit reporting with audit-ready reports, replacing manual time tracking, and the vendor cites a SOC 1 Type II compliant reporting path. It also connects engineering investment data to finance so both sides work from the same allocation numbers.
Is Jellyfish a fit for a mid-sized engineering org?
It is best suited to organisations with hundreds of engineers, structured SDLC tooling and someone who owns engineering metrics, since value comes from cross-team benchmarks and finance-grade reporting. Smaller teams without that data foundation, or buyers wanting transparent self-serve pricing, are usually better served by lighter reporting tools.