Jellyfish vs. Faros AI: Which Platform is Right for Your Engineering Team?

Jellyfish and Faros AI both help engineering organizations understand what AI is doing to software development. They come at the problem from opposite ends.

Jellyfish takes the broader view. It brings data on developers, AI agents, and delivery workflows into one place, so teams can see what works, where they struggle, and whether AI investments improve business results.

Faros AI concentrates on AI coding efficiency. Its token engineering platform tracks what organizations spend on AI coding, tests which models perform best on their code, and cuts waste through model routing and governance. It also offers engineering analytics.

• Choose Jellyfish if you want to understand how AI affects engineering performance across the organization and use that insight to improve delivery, allocate people, and make better investment decisions.
• Choose Faros AI if your main goal is control over AI coding costs, with tools to optimize model selection and enforce policies on how coding agents use tokens.

We compare both platforms below, area by area, so you can see where each one fits.

How Do Jellyfish and Faros AI Compare?

Each offers engineering analytics and AI ROI measurement, but Jellyfish applies them to engineering operations as a whole, while Faros AI focuses them on AI coding workloads.

Here’s how the two compare at a glance.

What are the Key Differences between Jellyfish and Faros AI?

Platform Focus

Jellyfish helps engineering leaders understand how their organizations operate and decide where to invest and what to improve. Faros AI is now primarily a token engineering platform, focused on the efficiency of the models and coding agents developers use.

Jellyfish

Jellyfish is an intelligence platform for AI-integrated engineering, used to manage both engineering teams and the AI tools they work with. It brings AI adoption, delivery, resource allocation, developer experience, and R&D cost data together in one product, so no team has to reconcile numbers across several systems.

That shared view serves every group around engineering. Executives decide where to invest, managers find and remove team bottlenecks, and product and finance teams follow where engineering time and budget go.

Faros AI

Faros AI earlier offered a broad engineering analytics product, and developer productivity metrics are still available. Its current product is aimed at engineering leaders, platform teams, finance, and security at companies with large AI coding workloads.

Best fit → Jellyfish for organizations that want one view of engineering performance and AI adoption, and a clear sense of where to invest next. Faros AI for teams whose first goal is more efficient, better-controlled use of AI coding agents.

AI Adoption and Workflows

Both platforms track how developers use AI coding tools. Jellyfish looks at how that usage changes team workflows, while Faros AI follows individual agent sessions and their output.

Jellyfish

Jellyfish AI Impact detects AI usage from system signals, with no manual input from developers. It supports three groups of tools:

• Coding assistants and agents, from GitHub Copilot and Cursor to Claude Code, Codex, and Gemini Code Assist, with dedicated dashboards for Claude Code and Cursor.
• Code review agents like CodeRabbit, Graphite, and Greptile.
• Devin, Google Jules, and other autonomous agents.

Cohort Intelligence groups developers into power, active, and casual users.

Workflow Optimization then compares how teams and roles use AI to write, review, and test code, down to the share of human and agent work in each pull request.

Jellyfish ties those patterns to quality and delivery signals, so teams can see which practices produce better results and spread them across the organization.

Faros AI

Faros AI pulls usage data from the built-in telemetry of Claude Code, Codex, Cursor, GitHub Copilot, and about a dozen other tools. It links each agent session to commits, pull requests, and tickets.

This way, a team can see whether agent-written code merges smoothly or creates extra review work. Faros then uses that history to propose repository-specific context and rules that improve how agents carry out coding tasks.

Best fit → Jellyfish for organizations that want to spread effective AI habits across teams. Faros AI for teams that want to study and tune individual agent sessions.

AI ROI and Spending

Both platforms connect AI spend to engineering outcomes. Jellyfish shows what the full AI investment achieves across engineering, while Faros AI helps teams get more out of each dollar spent on tokens.

Jellyfish

AI Token Cost Management shows token spend for every tool, team, and model in one view, with year-to-date totals and projected spend. Jellyfish connects that spend to pull request throughput and delivery impact, so teams can see which tools return the most for their cost.

It also shows how AI changes the mix of roadmap, innovation, and maintenance work, which reveals whether AI frees up capacity for new features.

Faros AI

Faros AI breaks token spend down by team, tool, model, and repository, and tracks each team against its budget in real time.

A cost-per-outcome view compares that spend with merged pull requests, resolved tickets, and shipped features. Faros also points out tasks where a lower-cost model could do the same work at equal quality.

Best fit → Organizations that need to prove the value of AI across engineering will get more from Jellyfish. Teams focused on the cost of each AI coding task may find Faros AI the closer match.

Engineering Performance

Jellyfish looks for slowdowns across the whole software development lifecycle and checks whether faster coding leads to faster delivery. Faros AI focuses its delivery analysis on the work of AI coding agents.

Jellyfish

Jellyfish tracks DORA metrics, cycle time, review speed, and throughput. Impact Insights compares PR cycle time and delivery speed with and without AI, down to the team, repository, and code area.

A jump in pull requests then counts as progress only when software also ships faster. When delivery slows, Life Cycle Explorer shows each stage of development, with idle issues, stale work, and project overlap.

Faros AI

Faros AI also supports DORA metrics, with cycle time broken down by task, pull request, and deployment. Its platform connects delivery and quality data with AI coding activity.

Faros’s own research found that developers with AI tools merge 98% more pull requests while review time grows 91%, and the platform looks for bottlenecks like these in the AI engineering environment.

Best fit → Jellyfish, when the goal is to confirm that faster coding leads to faster delivery across the whole lifecycle. Faros AI, when the priority is to see how AI coding agents affect delivery and quality.

Capacity Planning

Jellyfish helps organizations test staffing and scope changes before they commit to a roadmap. Faros AI’s newer planning features allocate and forecast AI coding spend across teams.

Jellyfish

Jellyfish connects three planning tools to the same delivery data:

• Resource Allocations calculates where engineering effort goes across initiatives, product lines, and deliverables
• Capacity Planner forecasts capacity for upcoming quarters from allocation history, team size, and work in flight
• Scenario Planner shows how changes to scope, priorities, or resources affect delivery before anyone commits to them

Jellyfish also sends alerts when a project is at risk of a delay, so a VP of Engineering can raise staffing or scope tradeoffs with the business early.

Faros AI

Faros AI earlier offered initiative tracking and resource allocation views. Its current platform maps each team by efficiency and strategic importance, sized by AI spend, and forecasts AI coding spend separately for developers and agents.

Best fit → Organizations that make headcount and roadmap decisions will find more support in Jellyfish. Faros AI is more suited to teams that plan AI coding budgets across developers and agents.

Developer Experience

Jellyfish measures developer experience with validated surveys and a single DevEx score, then ties the results to delivery metrics. Faros AI also supports developer surveys, from general templates to questions about AI coding tools.

Jellyfish

Jellyfish DevEx lets teams launch research-backed surveys in minutes and find the blockers that slow developers down. Results feed the DevEx Index, a single score that tracks developer experience over time.

Jellyfish correlates that feedback with DORA, SPACE, and system metrics, so teams can see whether a better working environment leads to faster, higher-quality delivery. AI also interprets the results and recommends next steps.

Faros AI

Faros AI has offered research-backed survey templates and can import survey data from other tools. Its documentation also describes surveys about AI coding assistants, with questions on time savings and satisfaction.

On the current platform, developer experience data is kept in the same graph as delivery metrics and token use.

Best fit → For a continuous read on developer experience tied to delivery, Jellyfish is the stronger option. Faros AI works well for teams that want survey data next to their AI coding activity.

Industry Benchmarking

Jellyfish compares an organization’s engineering performance with industry peers. Faros AI tests which AI coding tools and models work best on a company’s own code.

Jellyfish

Jellyfish Benchmarks compare the engineering metrics tracked in Jellyfish against data from more than 1,000 engineering organizations, at the team, group, or company level.

That includes delivery measures such as issue cycle time, deployment frequency, and planning accuracy, so each team can see where it stands against peers.

Faros AI

Faros AI offers three kinds of benchmark data:

• Research on AI’s impact on engineering, published in its AI Engineering Report.
• Build Your Own Bench, which reruns a company’s past pull requests with different AI tools and models and compares the cost, quality, and speed of each.
• A public Route Index that compares coding tools like Claude Code and Codex, paired with different models, on Faros’s own engineering tasks.

Best fit → Jellyfish is the broader choice for peer comparisons across engineering. Faros AI suits teams that want to know which AI tool and model work best on their own code.

AI Model Optimization and Governance

Faros AI has the stronger offering for direct control over which models coding agents use and what they spend. Jellyfish focuses on which AI tools and workflows create the most value.

Faros AI

Faros AI acts on the results of its benchmarks in three ways:

• Model routing sends each coding task to the model with the best results for its cost
• Context engineering gives agents the skills and rules specific to each repository
• Team policies set which models and coding tools each team may use, along with its usage quota

As a team nears its quota, Faros can throttle lower-priority work, send it to a cheaper model, or escalate it for approval.

Jellyfish

Jellyfish Vendor Comparison measures every AI tool in one framework. It shows which tools work best for new code, refactors, bug fixes, and reviews, and where each one speeds up or slows down delivery.

The AI token dashboard breaks spend down by tool, team, and initiative to help prevent overruns. Teams can use those views to decide where to scale, cut, or move AI tool spend.

Best fit → Teams whose main goal is direct model optimization will find more in Faros AI. Jellyfish fits organizations that want to know which AI tools and workflows deserve investment.

How Do Jellyfish and Faros AI Compare in Terms of Pricing?

Pricing for both platforms comes through a quote, though Jellyfish prices by seats and modules while Faros AI prices by usage.

Jellyfish Pricing

Jellyfish pricing depends on the number of seats and the modules a company selects. The pricing page groups those modules into three packages:

1. AI Impact covers AI adoption, token spend, and multi-tool evaluation.
2. Developer Productivity includes everything in AI Impact, plus DORA and SPACE metrics, benchmarks, investment allocation, and DevEx surveys.
3. DevFinOps handles software capitalization, R&D tax credits, and audit-ready reports.

Quotes come through the sales team.

Faros AI Pricing

Paid Faros AI plans go through sales, while Faros Desktop and a self-service playground are free to try. Its AWS Marketplace listing uses usage-based pricing at $1 per Token Intelligence unit, with the first month free for monthly subscribers.

The unit count rises with the volume of AI token activity Faros monitors and classifies, but the listing gives no exact conversion, so total cost is hard to estimate without a quote.

Note → Compare each quote with the job it does. Organizations that mainly want to cut AI coding costs will look at Faros’s fees next to their token budget. Those investing in broader engineering management will look at what Jellyfish’s view of delivery, capacity, and AI impact helps them decide.

User Reviews and Customer Feedback

Jellyfish has a 4.5 out of 5 rating on G2 across more than 350 reviews. Faros AI’s 4.8 rating comes from 13 reviews, which gives a narrower view of customer experience.

Jellyfish

One G2 user described how much of the analysis the platform handles on its own:

“The AI insights built within the product take the heavy lifting of evaluating the data off of the user. Pulling engineering data from our disparate systems and presenting a full picture across our engineering organization in one place is a big win.”

That single view also makes it possible to measure AI’s effect on delivery. When LastPass standardized on Claude, it compared engineering results in Jellyfish against its pre-AI baseline. According to CTO Jason Rasmussen, the time from opening an issue to closing it has dropped by 30–40%, and the strongest teams now work two to three times faster.

One customer would like custom dashboards to be easier to set up, and sometimes relies on support to fix GitHub and Jira data pulls:

“I find custom dashboarding challenging, though they’re moving in that direction. Sometimes I need to engage support to fix data pulls from GitHub and Jira, but they resolve those quickly.”

Faros AI

Feedback on Faros AI is positive so far, and customers point to how much manual work the platform saves:

“While the learning curve can be a little steep at first, a power user is able to build views, reports, dashboards, etc. that provide substantial value in hours when it would take a team of technical resources months to do the same in other tools.”

The platform’s customization options could be easier to use, according to a G2 review that asks for simpler widget management:

“We would like to have a more intuitive UI for customization, including the ability to easily move widgets from one dashboard to another.”

Jellyfish vs. Faros AI: Which Platform Should You Choose?

Faros AI and Jellyfish work on different parts of the move to AI-assisted engineering. Faros improves how coding agents use models and tokens, while Jellyfish measures what AI adoption means for delivery, capacity, and investment across engineering.

• Faros AI makes the most sense for organizations that already have a clear read on engineering performance and mainly want cheaper, better-controlled AI coding. Model routing, team budgets, and approved-model policies help keep agent spend in check.
• Jellyfish is a better match for organizations that need to know how AI changes engineering performance as a whole. It ties AI adoption to cycle time and throughput, points to where work slows down, and supports decisions on headcount, roadmaps, and AI budgets.

For CTOs and VPs of Engineering who need to show the business what AI investment achieves, that broader view makes Jellyfish the stronger choice.

Request a demo to see how Jellyfish measures AI’s impact on your engineering organization, or take a product tour first.

FAQs

Do Jellyfish and Faros AI replace existing project management tools?

No. Both are SaaS platforms that connect to the tools teams already use, including Jira, Azure DevOps, GitHub, GitLab, and Bitbucket.

Work still gets planned and tracked in those systems, and the platforms analyze that data to produce metrics, scorecards, and forecasts. Jellyfish also supports roadmapping and team management, with predictive analytics that warn when a delivery date is at risk.

Can Jellyfish and Faros AI help teams understand production incidents?

Yes, through incident management integrations. Jellyfish pulls PagerDuty and Opsgenie data to calculate change failure rate and mean time to resolve, so teams can see whether faster AI-assisted delivery affects reliability.

Faros AI also connects to PagerDuty and includes incident data in its delivery and quality metrics. Neither replaces monitoring tools like Datadog, which track system health separately.