Tool profile
Jellyfish: cost, fit and limits
Jellyfish is an engineering management platform made by Orthogonal Networks, Inc. (d/b/a Jellyfish) that links engineering activity to business outcomes, with modules for AI impact, developer productivity, developer experience surveys and DevFinOps reporting such as software capitalization and R&D tax credits.
What to know first
Jellyfish publishes no prices. The pricing page lists three offers, AI Impact, Developer Productivity and DevFinOps, each marked quote only, with cost set by seat count and chosen modules. No free plan or trial is named. The DevFinOps plan mentions SOC 1 Type II compliance. The integrations page lists Jira, Linear, Azure Boards, GitHub, Azure DevOps and several AI coding tools.
Jellyfish at a glance
| Maker | Orthogonal Networks, Inc. (d/b/a Jellyfish), Not stated on the vendor pages |
|---|---|
| Free plan | No |
| Entry price | Quote only |
| API access | No |
| Category | Best AI Engineering Analytics Software in 2026, rank 5 of 13 |
| Website | jellyfish.co |
Who Jellyfish is best for
Engineering leaders presenting to finance
DevFinOps covers software capitalization, investment allocation and R&D tax credits with audit-ready reports, which suits leaders who explain engineering cost to a CFO.
Organizations running many AI assistants
AI Impact covers adoption trends, multi-tool evaluation and token spend, and the integrations list names Claude Code, Cursor, Copilot, Codex and Gemini Code Assist.
Large teams that want benchmarks
Developer Productivity adds industry benchmarking, DORA, SPACE and AI metrics, and developer experience surveys for comparing teams, which helps leaders judge how one team compares with peers.
Jellyfish plans and pricing
| Plan | Price | What it includes |
|---|---|---|
| AI Impact | Quote only (not published) | Adoption and usage trends, AI developer experience analysis, multi-tool evaluation, token spend metrics and AI productivity insights. |
| Developer Productivity | Quote only (not published) | Everything in AI Impact plus AI-driven insights, custom dashboards, AI-powered queries, investment allocation, DORA, SPACE and AI metrics, benchmarking and surveys. |
| DevFinOps | Quote only (not published) | R&D tax credits, software capitalization, investment allocations, SOC 1 Type II compliance and audit-ready reports. |
Checked November 2026.
What Jellyfish does
- AI Impact
- Adoption trends, AI developer experience analysis and token spend metrics are collected across tools. A leader can see which assistants are used and what they cost before renewing licenses.
- Jellyfish Assistant
- Leaders ask questions in plain language and receive root-cause answers drawn from engineering data. The homepage example reports AI tool spend, and the pages do not detail the model behind it.
- Operational effectiveness
- Team benchmarks are meant to expose bottlenecks, and a Business Alignment view shows how resources are allocated across engineering work for planning conversations, which supports planning talks with product and finance.
- DevEx surveys
- Developer surveys surface burnout and productivity problems, and results sit next to delivery data rather than in a separate survey tool, so leaders can read sentiment beside throughput and review time.
- DevFinOps
- Software capitalization and financial reporting are automated with audit-ready reports. The pricing page ties SOC 1 Type II compliance to this plan, which finance teams can hand to auditors.
How Jellyfish handles your data
The privacy policy says data is kept for as long as the customer uses the services, then deleted or de-identified, and backups stay isolated until deletion is possible. It names no hosting region, describing secure servers behind firewalls and providers that may be located in the United States and other countries. Third-party AI model providers will not receive personal information for training. Whether Jellyfish's own models train on customer data is not stated, and the research section mentions collective learnings. SOC 1 Type II is tied to DevFinOps, and SOC 2 is not mentioned on the pages read.
Where Jellyfish falls short in practice
- No printed prices. All three offers are quote only, and cost depends on seat count and selected modules. A buyer cannot compare Jellyfish with printed-price tools before talking to sales.
- No trial named. The pricing page names neither a free plan nor a trial. Proof of fit presumably runs through a sales-led evaluation, which lengthens the buying process.
- Model-training statement is partial. The no-training promise covers third-party model providers and personal information. It does not clearly cover Jellyfish's own models or non-personal data, so a privacy review should ask about both.
Works with
Alternatives to Jellyfish
Jellyfish: common questions
How much does Jellyfish cost?
Jellyfish does not publish prices. The pricing page lists AI Impact, Developer Productivity and DevFinOps as quote only, and says price depends on the number of seats and the modules you select. No free plan or trial is named, so a quote is the first step.
What is the DevFinOps module in Jellyfish?
DevFinOps is the finance-facing plan. It covers R&D tax credits, software capitalization and investment allocations, and produces audit-ready reports. The pricing page ties SOC 1 Type II compliance to it. Companies that must classify engineering work for accounting purposes are the intended buyers.
Which AI coding tools does Jellyfish connect to?
The integrations page lists Claude Code, Cursor, GitHub Copilot, Codex and Gemini Code Assist, alongside Jira, Linear, Azure Boards, GitHub and Azure DevOps. AI Impact uses these to show adoption, usage trends and token spend. Verify the current list with the vendor before purchase.
What is Jellyfish Assistant?
Jellyfish Assistant lets engineering leaders ask questions in plain language and get root-cause answers from engineering data. The homepage example reports AI tool spend. The pages read do not name the model or explain how conversation data is handled, so ask about both.
Does Jellyfish train AI models on customer data?
The privacy policy says third-party AI model providers will not receive personal information for training. It does not clearly state whether Jellyfish's own models train on customer data, and the research section mentions collective learnings. A buyer should ask for a written answer before connecting repositories.