Jellyfish vs. DevStats
Jellyfish is enterprise engineering management built for the CFO. DevStats is focused engineering intelligence built for the engineering leader — set up in two minutes, priced per developer.
Enterprise engineering management vs. engineering intelligence that sets up in 2 minutes
Jellyfish is built for the CFO. It layers Git and Jira data on top of HR systems and financials, then produces audit-ready allocation reports for capex/opex, R&D tax credits, and board reviews. Real problem, well solved at large companies.
DevStats is built for the engineering leader. It plugs into the Git, issue tracker, incident, and AI coding tools your team already uses, runs on the SPACE framework and DORA research, and gets out of the way — a diagnostic instrument, not an allocation platform.
The catch on Jellyfish is the buying motion: no public pricing, no trial, a multi-call sales process, and contracts reported between $30,000 and $120,000 a year.
Where DevStats and Jellyfish really differ
Four axes that decide the call: report breadth, AI-native analytics, pricing, and AI-agent auto-configuration. DORA and delivery metrics are table stakes on both — see the feature table below.
24 reports, 6 categories — everything in one product for the engineering leader
DevStats ships 24 reports across six engineering-focused categories — Flow, Planning & Sprints, Investment & Allocation, Quality & Reliability, Visibility & Insights, and AI Impact — in one product at one price. Jellyfish organizes its surface around allocation and capitalization reporting for the CFO, so PR-level, sprint-level, and code-review views for the engineering leader are secondary to the portfolio rollup.
- 24 reports across 6 categories, not a CFO-first portfolio view
- Player Dashboard, AI Impact, and Investment Profile in one product
- Built for engineering leaders, not R&D finance teams
An MCP server and an in-product AI Chat — query your data in natural language
DevStats ships a first-party MCP server so Claude, Cursor, or Copilot can drill into your PRs, issues, and reports through natural-language queries. The in-product AI Chat answers the same questions without leaving DevStats. Jellyfish has no official MCP server — only a third-party community fork on GitHub — and no in-product AI assistant.
- First-party MCP server, not a community fork
- In-product AI Chat with squad-scoped context
- AI Impact reports across Copilot, Claude Code, and Windsurf
Public per-developer pricing vs. a multi-call enterprise sales process
DevStats publishes pricing: $15 per contributor on Starter, $27 on Pro, billed annually. A 40-developer team on Pro runs about $13,000 a year. Jellyfish does not publish pricing. Reported contracts fall between $30,000 and $120,000 a year depending on seat count, modules, and term, after a sales process that commonly takes four to six calls.
- Public pricing page — say yes or no without a three-stakeholder sign-off loop
- 14-day self-serve trial, no credit card required
- Bootstrapped and profitable vendor, not running on a 2027 exit plan
An AI agent configures your team on setup — no taxonomy workshop, no Jira labeling kickoff
DevStats connects in under two minutes with no code changes. An AI agent then inspects your Git and issue-tracker history to auto-detect how your team actually works — squads, services, sprint cadence, branch conventions — so you skip the taxonomy workshop and the working-agreements kickoff entirely. Historical backfill runs in the background within normal API rate limits. Jellyfish is a multi-week rollout that needs a Jira audit and a labeling workshop before the allocation reports are useful.
- AI agent auto-detects squads, services, sprint cadence, and branch conventions
- No taxonomy workshop, no working-agreements kickoff
- Historical data pulled on connect (1 year Starter · 3 years Pro)
Who each platform fits
Jellyfish is the right answer for some buyers. DevStats is the right answer for others. Pick the one whose ideal customer looks most like you.
- Engineering orgs with 200+ developers where R&D cost capitalization and audit-ready finance reporting are non-negotiable procurement requirements
- Companies where a CFO, controller, or R&D finance team owns the evaluation and the engineering leader is a stakeholder, not the buyer
- Leaders who need portfolio-level reporting that ties engineering activity to business objectives, OKRs, and capitalizable work for a board deck
- Organizations with mature Jira hygiene across every team (Jellyfish relies on consistent ticket discipline to calculate allocation)
- Buyers with procurement runway for a multi-month evaluation that includes security review, legal redlines, and multi-stakeholder sign-off
- Growth-stage SaaS, software, and VC/PE-backed companies with 15+ developers (sweet spot 30 to 50) that need real engineering visibility without enterprise pricing or timeline
- VP Engineering and CTO buyers who need to answer the CEO's "where is engineering time going" question before the next board meeting, not after the next fiscal year
- Engineering managers who want to see exactly which stage of the PR cycle is stalling delivery, which reviewers are overloaded, and which sprint commitments are about to slip
- Teams that have rolled out GitHub Copilot, Cursor, Claude Code, Amazon Q, Cody, or Windsurf and need to prove to leadership whether the AI spend is actually landing
- Organizations committed to the SPACE framework and to non-toxic metrics: no individual stack ranking, no surveillance framing, no leaderboards
- Procurement teams that require SOC 2 Type II, public pricing, and the ability to say yes or no without a three-stakeholder sign-off loop
- Leaders who would rather connect a tool to one repo in two minutes and see their own numbers than sit through a scripted demo with someone else’s data
- Buyers who want the vendor behind their engineering intelligence stack to be profitable and sustainable, not running on a 2027 exit plan
Full feature comparison
Side by side across the capabilities engineering leaders actually use. Pricing sits at the top because it is the row most readers scan first. Cells where DevStats has a clear edge are highlighted.
| Feature | DevStats | Jellyfish |
|---|---|---|
| Pricing | ||
| Starting price (per contributor, billed annually) | $15/mo (Starter) | Not public |
| Mid-tier price (per contributor, billed annually) | $27/mo (Pro) | Not public |
| Typical annual contract | ~$13K/yr for 40 devs on Pro | $30K–$120K/yr reported |
| Public pricing page | ||
| Free trial | 14 days, self-serve, no credit card | Demo required |
| Time to first insight | Under 2 minutes | Typical multi-month rollout |
| Business model | Bootstrapped and profitable | VC-funded |
| DORA metrics | ||
| All four DORA metrics | ||
| DORA thresholds from research (Accelerate) | Partial | |
| Elite / High / Medium / Low performer rating | ||
| Delivery metrics | ||
| PR Cycle Time with 5-stage breakdown (Coding, Pickup, Review, Merge, Deploy) | Aggregate cycle time; stage breakdown less granular | |
| Throughput | ||
| Planning accuracy / sprint tracking | ||
| Aging PRs and WIP overload alerts | Partial | |
| Code review metrics | ||
| Review turnaround time | ||
| Reviewer load balancing | Partial | |
| Flags oversized PRs (e.g., over 300 lines) | Partial | |
| Engineering investment and allocation | ||
| Four-bucket work categorization (New Things, Improving, KTLO, Productivity) | Yes (Investment Balance) | Allocation across projects, themes, business objectives |
| Software cost capitalization reporting | Not a core focus | Yes, audit-ready |
| Board-level portfolio reporting | Investment Profile report | Core strength |
| Custom metrics | Enterprise tier | Enterprise tier |
| AI coding tool coverage | ||
| AI Impact reports (AI-assisted PR %, velocity, quality, review time) | Yes (velocity, quality, and review time deltas) | AI adoption and impact reporting |
| GitHub Copilot | ||
| Cursor | ||
| Claude Code | Coverage varies | |
| Amazon Q, Cody, Windsurf | Coverage varies | |
| Developer experience | ||
| SPACE framework foundation | Yes (product built on SPACE) | SPACE and DORA referenced |
| Surveys and DevEx signals | ||
| Activity heatmap | Yes (squad-level and player-level) | Partial |
| Positioning on individual developer metrics | Processes, not people. Non-toxic by design. | Resource allocation framing; individual tracking available |
| Integrations | ||
| Git (GitHub, GitLab, Bitbucket, Azure DevOps) | ||
| Issue tracker (Jira, Linear, Asana, ClickUp, Azure DevOps) | Jira primarily | |
| Incident management (PagerDuty, OpsGenie, Datadog, incident.io) | Partial | |
| HR and finance tool integrations | Not core | Yes (core to allocation model) |
| Historical data retention | 1 year (Starter) · 3 years (Pro) | From connected tools |
| Setup and support | ||
| Setup time | Under 2 minutes, no code changes | Typical multi-month |
| AI agent auto-detects squads, services, sprint cadence, and branch conventions | ||
| Taxonomy workshop or Jira labeling kickoff required | ||
| Self-serve onboarding | No, guided enterprise onboarding | |
| Dedicated Slack channel for support | Yes (Pro plan) | Enterprise customer success team |
| Security and compliance | ||
| SOC 2 Type II | ||
| SAML SSO | Enterprise tier | |
| Self-hosted option | Contact sales | Enterprise options available |
Common questions about comparing Jellyfish and DevStats
DevStats connects in under two minutes with no code changes. An AI agent inspects your Git and issue-tracker history to auto-detect squads, services, sprint cadence, and branch conventions — so you skip the taxonomy workshop and the working-agreements kickoff entirely. Historical data (1 year on Starter, 3 years on Pro) backfills in the background within normal API rate limits. Jellyfish is a multi-week rollout that needs a Jira audit and a labeling workshop before the allocation reports are useful.
No. Both platforms read from the same source systems (GitHub, GitLab, Bitbucket, Jira, Linear), so the metrics live in your tools, not the vendor. DevStats pulls your historical data on connect and rebuilds your cycle times, DORA scores, and throughput from scratch. The only thing to rebuild is any custom Jira labeling tied to capitalizable work.
Yes, and it is the cleanest way to evaluate. Both tools read from your source systems with no code changes, so you can point them at the same GitHub org and Jira project for two to four weeks. Note that Jellyfish does not offer a free trial, while DevStats runs on a self-serve 14-day trial that extends twice for enterprise security review.
All four DORA outcomes (deployment frequency, lead time for changes, change failure rate, time to restore) are first-class on both platforms. DevStats applies the DORA/SPACE research thresholds from Accelerate automatically, so every metric arrives with an Elite/High/Medium/Low read, and breaks Cycle Time into five stages (Coding, Pickup, Review, Merge, Deploy). Jellyfish reports DORA inside its allocation surface, with less stage-level granularity.
This is the use case Jellyfish was built around. If a CFO needs audit-ready R&D cost capitalization, capex/opex splits, or tax credit documentation, Jellyfish wins on that requirement. DevStats does not sell capitalization reporting, but its Investment Balance view splits every sprint into features, improvements, KTLO, and productivity work for board-level conversations.
No. DevStats is a cloud product with OAuth and API-key connections, no agents, and no infrastructure to run. New commits, PRs, issues, and deployments flow in automatically. Jellyfish carries higher ongoing overhead because its allocation reports depend on Jira hygiene, ticket labeling, and epic discipline maintained by an eng ops or TPM owner.
DevStats publishes pricing: $15 per contributor per month on Starter, $27 on Pro, billed annually. A 40-developer team on Pro runs about $13,000 a year. Jellyfish does not publish pricing. Reported contracts fall between $30,000 and $120,000 a year depending on seat count, modules, and term, after a sales process that commonly takes four to six calls.
DevStats vs. the others
Jellyfish is one of the comparisons engineering leaders run, but it is rarely the only one. If your shortlist also includes any of the platforms below, the side-by-side is one click away.