# Trainyard, in full

Open-source (Apache-2.0) planning rooms for scaled agile and the classroom for anyone who trains it. This file carries the content of every public page so an assistant can answer from it. Canonical pages are linked.

## Pricing
Source: https://trainyard.app/pricing

- Practice (SOLO): free. Self-host the whole product, or run one small room on ours. Free up to 5 people; paid plans start from the sixth. Includes: Teach the five SAFe classes, free; Fifty ready-made templates, or bring your own; Every planning board, canvas and value stream tool; Self-hosted: unlimited people and trains; Hosted: one room, up to 5 people; Community support.
- Trainer (TRAINER): $19 / trainer / mo, or $16 / trainer / mo billed yearly. Everything a working coach or trainer needs, with unlimited people in the room. Includes: Free 30-day trial, then month by month; Money back on the first paid month; Unlimited participants, hosted; Class boards: Leading SAFe, SPC, Advanced SPC, POPM, RTE; Import your own Miro class boards; Jira and Azure DevOps sync; AI co-trainer with your own key; Email support.
- Academy (ACADEMY): $249 / train / mo, or $199 / train / mo billed yearly. For Agile Release Trains and the academies that launch them. Priced per train, not per person. Includes: Free 30-day trial, then month by month; Money back on the first paid month; Unlimited people per train; Jira app and Azure DevOps extension; SSO and an audit trail; Hosted by us or on your servers; Named success lead.

Every paid plan starts with a free 30-day trial, then month by month; yearly is a switch after the first paid month. The first paid month carries a 30-day money-back guarantee the customer claims from Plan & billing: the charge is refunded, the subscription ends, the workspace stays on the free plan with everything kept. Each customer gets a workspace of their own.

## Frequently asked

Q: Is it free for independent trainers?
A: Yes. Practice is free for one hosted room of up to 5 people, and the whole product is open source under Apache-2.0: self-host it with every feature and no limits. From the sixth person, Trainer or Academy.

Q: How do the trial and the guarantee work?
A: Trainer and Academy start with a free 30-day trial: you enter a card, nothing is charged, we email you when the trial starts and again 7 days before the first charge, and you can cancel before the trial ends from Plan & billing. After the trial it is month by month. If the first paid month does not convince you, claim the money back yourself from Plan & billing within 30 days of that charge: the charge is refunded, the subscription ends, and your workspace stays on the free plan with everything kept. Yearly billing is a switch you can make after the first month.

Q: Do participants need accounts?
A: They open the board in a browser from a link. On a free room anyone with the link can take part; once you turn authentication on, viewers read and planners move cards, each with a key you send. Nothing to install.

Q: Which frameworks are supported?
A: SAFe (Essential, Portfolio, Full), AI-Native SAFe, LeSS, Scrum@Scale, Nexus, Flight Levels and tribe models, as editable operating models. Change the model and the boards and dashboards follow.

Q: Can I use my own boards?
A: Yes. Upload iObeya exports, connect Miro, Mural, monday.com or Trello, or drop a CSV. Boards arrive at their positions and colours, become templates, and map onto the plan only when you say so.

Q: Can it run inside Jira or Azure DevOps?
A: Yes. The Jira app (Atlassian Forge) and the Azure DevOps extension put the boards, value streams and canvases in a project page or hub. Academy includes both.

Q: Why is Academy priced per train?
A: Because a PI planning room holds a whole train, and the tools priced per user or per room make the room the expensive part. One train is the smallest unit; portfolios with more than ten get a quote.

Q: Does it work online and in person?
A: Both. Project the board on the wall or have everyone open it on their laptops; the facilitator sees every move either way.

## Comparisons
Source: https://trainyard.app/compare

- Trainyard: hosting: Self-hosted, open source; hosted; Jira app and Azure DevOps extension. PI planning: Integrated: PI planning, team breakouts, PI objectives, confidence vote. ART and team boards: ART and team boards, PI planning board with milestones and dependency strings, ROAM, WSJF, retrospectives. Beyond planning: Portfolio Kanban, roadmaps, SAFe canvases, value stream identification and mapping, AI portfolio, dashboards. Integration: Jira and Azure DevOps, two ways; imports from iObeya, Miro, Mural, monday.com. Priced by: Trainer or train; people in the room are free.
- iObeya: hosting: On premises or separate server. PI planning: Integrated web interface. ART and team boards: ART board, ROAM, WSJF, retrospectives. Beyond planning: Portfolio and more. Integration: Full integration through API and iFrame. Priced by: Room, with a minimum number of rooms.
- Easy Agile: hosting: Jira plugin. PI planning: Within Jira. ART and team boards: ART and team boards. Beyond planning: None. Integration: Jira only. Priced by: Every Jira user on the site.
- Agile Hive: hosting: Jira plugin. PI planning: Within Jira. ART and team boards: ART board, ROAM, WSJF, team execution. Beyond planning: Portfolio Kanban, roadmap, reporting. Integration: Jira only. Priced by: User, by quote.
- BigPicture: hosting: Jira plugin. PI planning: Within Jira. ART and team boards: ART board, ROAM, WSJF, team execution. Beyond planning: Project planning and reporting, OKR and risk widgets. Integration: Jira only. Priced by: Every Jira user on the site.
- PIPlanning.io: hosting: On premises or separate server. PI planning: Dedicated tool. ART and team boards: ART and ROAM boards. Beyond planning: None. Integration: Jira. Priced by: Team.
- Miro: hosting: Public cloud. PI planning: Not integrated; templates. ART and team boards: Limited ART board. Beyond planning: None. Integration: None for planning data. Priced by: Every member.
- When iObeya is the better pick: iObeya is the better pick when your organisation already runs its visual management on it beyond PI planning (obeya rooms, lean boards across plants) and wants one vendor for all of it. (https://trainyard.app/compare/iobeya)
- When Easy Agile is the better pick: Easy Agile is the better pick when every participant already lives in Jira, planning never leaves Jira, and you want nothing to host. (https://trainyard.app/compare/easy-agile)
- When Agile Hive is the better pick: Agile Hive is the better pick when you want SAFe modelled strictly inside Jira with reporting by the same vendor. (https://trainyard.app/compare/agile-hive)
- When BigPicture is the better pick: BigPicture is the better pick when project portfolio reporting inside Jira (Gantt, resources) matters more than the planning room itself. (https://trainyard.app/compare/bigpicture)
- When PIPlanning.io is the better pick: PIPlanning.io is the better pick when you want a dedicated PI planning board with little else around it and a per-team price. (https://trainyard.app/compare/piplanning-io)
- When Miro is the better pick: Miro is the better pick when the whiteboard is used for everything and PI planning is a template among many, with no need for planning data to flow back to a tracker. (https://trainyard.app/compare/miro)

## Class boards for trainers
Source: https://trainyard.app/classes

### Leading SAFe (2 days) · https://trainyard.app/classes/leading-safe
Two days: business agility, the mindset and principles, team and technical agility, Agile Product Delivery with a PI planning simulation, Lean Portfolio Management, leading the change.

- Lesson 1: Thriving in the Digital Age with Business Agility. Exercises: What brought you here; Potential for improvement × ease of implementation.
- Lesson 2: Building a Foundation with Mindset, Values, and Principles. Exercises: Agile Manifesto principles in your context; Your value stream and its delays; Centralize or decentralize? Score each decision.
- Lesson 3: Establishing Team and Technical Agility. Exercises: Built-in quality in your teams.
- Lesson 4: Building Solutions with Agile Product Delivery. Exercises: Empathy map; Empathy map canvas; Features: benefit hypothesis and acceptance criteria; WSJF: Cost of Delay ÷ job size; PI Planning simulation; PI Planning simulation · autonomous delivery vehicle; Run it on the real board.
- Lesson 5: Exploring Lean Portfolio Management. Exercises: Toward Lean Portfolio Management; WSJF worked example (Fibonacci); Portfolio Kanban and canvases.
- Lesson 6: Leading the Change. Exercises: The leader's call to action (one page); Who do we need? Guiding coalition.

### SAFe Practice Consultant (SPC) (4 days) · https://trainyard.app/classes/spc
Four days: the Leading SAFe lessons first (business agility, principles, team agility, Agile Product Delivery with the PI planning simulation, LPM), then designing the implementation, launching an ART with the readiness checklist, coaching ART execution, enhancing the portfolio and accelerating business agility.

- Lesson 1: Thriving in the Digital Age with Business Agility. Exercises: What brought you here.
- Lesson 2: Building a Foundation with Mindset, Values, and Principles. Exercises: Agile Manifesto principles in your context; Your value stream and its delays; Centralize or decentralize? Score each decision.
- Lesson 3: Establishing Team and Technical Agility. Exercises: Built-in quality in your teams.
- Lesson 4: Building Solutions with Agile Product Delivery. Exercises: Empathy map; Empathy map canvas; Features: benefit hypothesis and acceptance criteria; WSJF: Cost of Delay ÷ job size; PI Planning simulation; PI Planning simulation · autonomous delivery vehicle; Run it on the real board.
- Lesson 5: Exploring Lean Portfolio Management. Exercises: Toward Lean Portfolio Management; WSJF worked example (Fibonacci); Portfolio Kanban and canvases.
- Lesson 6: Leading the Change. Exercises: Who do we need? Guiding coalition.
- Lesson 7: Getting Certified and Starting Training (SAFe for Teams). Exercises: Lesson links.
- Lesson 8: Reaching the SAFe Tipping Point. Exercises: Toward the tipping point; The leader's call to action.
- Lesson 9: Designing the Implementation. Exercises: Identify the value streams; Value stream canvas; Operational steps → systems → people → ARTs; Run it live.
- Lesson 10: Launching an Agile Release Train. Exercises: ART readiness checklist: must it be in place to launch? Y · N · D(epends); Score it live; Launch planning.
- Lesson 11: Coaching ART Execution. Exercises: Fishbone: why does the ART miss its PI objectives?; Coaching plan for the first PI.
- Lesson 12: Enhancing the Portfolio. Exercises: WSJF worked example (Fibonacci); Portfolio Kanban, canvases and guardrails.
- Lesson 13: Accelerating Business Agility. Exercises: Measure and grow: where are we?.
- Lesson 14: Get Certified and Start Training. Exercises: Your first 90 days as an SPC.

### Advanced SPC (3 days) · https://trainyard.app/classes/aspc
Three days for experienced SPCs: the role, embracing AI, engaging executives, developing a transformation strategy, unlocking portfolio flow, customizing SAFe to your context, with the CARS case studies and a design-sprint learning lab.

- Lesson 1: Introducing the Advanced SPC Role. Exercises: What brought you here; CARS scenario: a company on the move; CARS: SAFe transformation goals; CARS: the problems to overcome.
- Lesson 2: Embracing AI. Exercises: Where does AI change the work?; AI in our transformation.
- Lesson 3: Engaging Executives. Exercises: Case study 1: charting the road ahead; Case study 2: communicating the transformation strategy.
- Lesson 4: Developing a Transformation Strategy. Exercises: Case study 3: do we have the metrics we need?; OKRs for the transformation.
- Lesson 5: Unlocking Portfolio Flow. Exercises: Case study 4: using facts as our guide; WSJF worked example (Fibonacci); WSJF: Cost of Delay ÷ job size; Portfolio flow, live.
- Lesson 6: Customizing SAFe to Your Context. Exercises: Keep · adapt · drop; Operating models as data.
- Lesson 7: Learning Lab: design sprint. Exercises: Expert talks & HMWs; Independent work area · HMWs; HMW board: categorise and vote (2 dots each, decider 4); Drawing the map; Map; Long term goal; Long term goals; Sprint questions; Sprint questions; Lightning demos; Lightning demos; Concept sketching (offline); Sketches · vote with dots, questions on stickies; Favourite concept and reasons; User test flow; User test flow matrix; Storyboarding; Storyboard.
- Lesson 8: Get Certified and Start Training. Exercises: Your next transformation move.

### Product Owner / Product Manager (POPM) (2 days) · https://trainyard.app/classes/popm
Two days for POs and PMs, from the trainer's own POPM board: the roles and AI's disruption of product work, preparing for PI Planning (market rhythms, just-enough pre-planning, AI-supported customer research, writing features), leading PI Planning (dependencies, AI-empowered planning, the vision, the simulation), executing iterations (continuous delivery pipeline, story map, AI-assisted iteration planning) and executing the PI (sync events, responsible AI).

- Lesson 1: Becoming a Product Owner / Product Manager. Exercises: Lesson 1 activity: product role experience; L1 discussion: AI disruption in Product Management; AI disruption: what we think; AI opportunities: sort them; AI prompting template (use in AI activities).
- Lesson 2: Preparing for PI Planning. Exercises: Lesson 2 activity: consider market rhythms for optimum release timing; Market rhythms: demand by quarter; Lesson 2 discussion: just enough pre-planning; What is the problem? Who has the problem? Why is it important?; Lesson 2 discussion: exploring customer needs with AI-supported research; Lesson 2 activity: writing features · example; Feature storming; Feature refinement; Features: benefit hypothesis and acceptance criteria; WSJF: Cost of Delay ÷ job size; Feature and enabler canvases, live.
- Lesson 3: Leading PI Planning. Exercises: Lesson 3 activity: identify dependency issues and resolve them; ART planning board; L3 discussion: AI-empowered PI Planning; Lesson 3 activity: communicate the vision; PI Planning simulation · autonomous delivery vehicle; PI Outcome Planning (AI-Native SAFe).
- Lesson 4: Executing Iterations. Exercises: Lesson 4 discussion: building the Continuous Delivery Pipeline; Lesson 4 activity: develop a story map · example feature; Story map; Story writing: INVEST; Lesson 4 activity: AI-assisted iteration planning.
- Lesson 5: Executing the PI. Exercises: Lesson 5 discussion: enabling alignment across the ART with sync events; Sync events: what I bring; Lesson 5 discussion: responsible AI and product decisions; Responsible AI: trustworthy, explainable, human-centric; Iteration review and system demo.

### Release Train Engineer (RTE) (3 days) · https://trainyard.app/classes/rte
Three days for RTEs: the role, applying SAFe principles, organizing and launching the ART with the readiness checklist, planning a PI, executing a PI, relentless improvement, serving the ART.

- Lesson 1: Exploring the RTE Role and Responsibilities. Exercises: The RTE's week.
- Lesson 2: Applying SAFe Principles. Exercises: Which principle helps here?.
- Lesson 3: Organizing the Agile Release Train. Exercises: Value stream and ART design; ART readiness checklist: must it be in place to launch? Y · N · D(epends); Score it live; Launch planning; ART launch timeline and PI planning readiness.
- Lesson 4: Planning a Program Increment. Exercises: PI Planning simulation; PI Planning simulation · autonomous delivery vehicle; PI Outcome Planning (AI-Native SAFe).
- Lesson 5: Executing a Program Increment. Exercises: ART sync agenda; Program board and flow dashboards.
- Lesson 6: Fostering Relentless Improvement. Exercises: Problem-solving workshop: fishbone; Improvement backlog items.
- Lesson 7: Serving the ART. Exercises: Servant leadership: what changes on Monday.
- Lesson 8: Continuing Your Learning Journey. Exercises: Your first PI as RTE.

## Guides
Source: https://trainyard.app/guides

### ART readiness checklist: the 15 questions before you launch an Agile Release Train · https://trainyard.app/guides/art-readiness-checklist
SAFe's ART readiness checklist as a working tool: nine needed items and six desirable ones, each with the consideration question behind it, mapped to the launch timeline. Score it before the date-certain quick-start.

#### What the checklist is for
Launching an Agile Release Train on a date certain works when the few things that must be true on day one are true. SAFe's readiness checklist names them. Nine are needed: without them the first PI Planning does not happen or does not hold. Six are desirable: they make the first PI smoother but can be finished during it.
Trainyard keeps the checklist on the ART launch canvas next to the launch timeline (content, cadence, teams, leadership), so each item sits on the lane that prepares it and the readiness score is the share of needed items done.

#### Needed before PI Planning
| Item | The question to answer |
| --- | --- |
| Planning scope and context | Is the scope (product, system, technology domains) of the planning process understood? Have we identified our value stream(s) and Agile Release Train(s)? |
| Release Train Engineer | Have we identified the Release Train Engineer? Do they understand the scope of the role in preparing the organization and preparing for the PI Planning event? |
| Planning timeframe, iteration and PI cadence | Have we identified the PI Planning dates, the iteration cadence, and the PI cadence? |
| Agile Teams (SM/TCs, POs) | Does each team have an identified Scrum Master/Team Coach and Product Owner? |
| Team makeup/commitment | Have we identified cross-functional Agile Teams? Are there dedicated team members on every team? |
| Agile Team attendance | Are all team members attending PI Planning, whether in person or involved remotely? |
| Executive, Business Owner | Do we know who will set and present the business context and strategy? Do we know who will assign business value and accept the plans? |
| Business alignment | Is there reasonable agreement on priorities amongst the Business Owners and Product Management? |
| Architecture vision and development practices | Do we know who will present the architecture vision and required development practices? |

#### Desirable
| Item | The question to answer |
| --- | --- |
| System Team | Has the system team been identified and formed? |
| Shared Services | Have the shared services (User Experience, Architecture, etc.) been identified? |
| Other Attendees | Do we know what other key business and technology stakeholders should attend? |
| Agile Lifecycle Management (ALM) tooling | Do we know how and where iterations, PIs, features, stories, measures, etc., will be maintained? |
| Development infrastructure | Do we have a strategy for using and improving the Continuous Delivery Pipeline? |
| Quality practices | Is there a strategy for applying built-in quality practices? |

#### How trainers use it
- In the SPC class (Launching an Agile Release Train) and the RTE class (Organizing the ART), the checklist is the exercise: each team scores a case study Y, N or Depends and defends the calls.
- In a real launch, the RTE owns it and reviews it at each weekly launch sync; items move to done only with a named owner.
- The score is honest by design: desirable items do not count towards readiness, so a train cannot look ready by finishing the easy things.

### PI Outcome Planning: the one-day, outcome-first alternative to PI Planning (AI-Native SAFe) · https://trainyard.app/guides/pi-outcome-planning
What PI Outcome Planning is, the outcome tree from portfolio to team, the PI Outcome Planning Board, team boards, the management review board, the one-day agenda and the confidence vote, as run in Trainyard.

#### What changes
Classic PI Planning plans features into iterations. PI Outcome Planning plans outcomes: measurable changes in customer or business behaviour, each with two to five key results, and lets teams choose the experiments, prototypes, enablers and features that move them. It fits in one day because the unit of agreement is the outcome and its milestones, not every story.
The spine is an outcome tree: portfolio outcomes above the ART, ART outcomes, PI outcomes for this increment, and team outcomes that contribute to them. Ambition is explicit: moonshot or roofshot.

#### Milestones on the board
| Kind | Meaning |
| --- | --- |
| Fixed-date milestone | Events and contractual obligations on or before a date set outside the ART. |
| Learning milestone | Where the ART validates a technical or business hypothesis and decides whether the work is still worth pursuing. |
| Synchronization milestone | Where two or more teams' work must converge; missing it stalls the dependent work. |
| Release | A planned point where value reaches customers; mostly on demand, sometimes tied to a fixed date. |

#### What teams put on their boards
- Experiment: a cheap test of an assumption with a clear learning goal
- Prototype: something customers can touch that answers a riskier question
- Enabler: capability, data or infrastructure the outcome needs
- Feature: value delivered to customers, with a hypothesis and acceptance criteria

#### The one-day agenda
| Half | Minutes | Slot | What happens |
| --- | --- | --- | --- |
| morning | 30 | Business context | ART vision, ART outcomes, progress on key results, the draft PI outcomes, and what changed since last PI. |
| morning | 60 | Outcome breakout | Per PI outcome, across teams: how each team contributes, and the milestones that keep them aligned. |
| morning | 60 | Team breakout | Per team: all its outcomes on one board, capacity, constraints, the critical experiments, prototypes, enablers and features. |
| morning | 30 | Outcome breakout | Draft the PI Outcome Planning Board; surface decisions needed and risks. |
| morning | 30 | Draft PI Outcome Planning Board presentation | Representatives walk the board: team outcomes, milestones, decisions needed, risks. |
| morning | 60 | Lunch & Management Review and Problem-Solving | Leadership and stakeholders turn decisions needed and risks into confirmed decisions and actions, each with an owner. |
| afternoon | 30 | Communicate planning adjustments | Each decision and action, with the owner who helps teams adjust. |
| afternoon | 30 | Outcome breakout | Fold the adjustments in; deepen the cross-team conversations. |
| afternoon | 60 | Team breakout | Adjust intentions and sequencing; re-examine the team outcomes and their key results. |
| afternoon | 30 | Outcome breakout | Finalize the PI Outcome Planning Board, including other commitments. |
| afternoon | 60 | Final plan review | Each PI outcome, the other commitments, open decisions and risks, then the fist-of-five confidence vote. |

#### Management review board
- Decisions needed: raised by teams during the breakouts, with the options
- Risks: ROAMed at the review
- Decisions confirmed: the decision as made, with who made it
- Actions: an action with an owner and a date

#### The vote
The final plan review ends with a fist-of-five vote per PI outcome and for the plan as a whole. Anything under three gets a voice before the vote closes. Trainyard records the votes against the outcome so the next Sense and Respond knows where confidence was thin.

### Running an AI-Native Agile Release Train: pipeline, gates, intent records and the twelve success factors · https://trainyard.app/guides/ai-native-art
How an AI-Native ART works in practice: the five-stage pipeline with gates, learning versus value outputs, intent, specification and context records with change propagation, curated data, team capability coverage, Sense and Respond, and the twelve-factor assessment.

#### Outputs, not just features
An AI-Native ART produces four kinds of output. Experiments and prototypes exist to learn: they conclude with the uncertainty they reduced. Features and enablers exist to deliver value: they release with verification evidence. Every output hangs off an outcome and a metric, and stopping one is as easy as advancing it: discard from any stage, with a reason.

#### The pipeline and its gates
| Stage | Gate to leave it |
| --- | --- |
| discover | an outcome and a metric are linked |
| specify | a specification exists, the intent record is clean (no pending review) and the metric is set |
| build | a verification owner is named |
| validate | value items need verification evidence; learning items record the uncertainty reduced |
| release | released, or concluded for learning items |

#### Intent, specification, context
Three records carry what an AI system is meant to do. The intent record states the current and future state and the measure. Specifications and context records derive from it. When an intent changes, every derived record is marked for review and the item is held at Specify until each review is cleared; the evidence log keeps the trail.

#### Curated data
- Complete: People and agents can reach all the knowledge a task requires.
- Accurate: Verified, trustworthy, lineage traceable to source.
- Securely accessed: The right people and agents reach the right data, and no more.
- Maintained: The data stays current.

#### Team capability coverage
- Product: Defines the why, anchors to customer needs, synthesises feedback.
- Builder: Creates the solution.
- Domain expert: Deep subject knowledge.
- AI: Named as a team capability, not a tool; humans review its output for quality and bias.

#### The twelve success factors
| Shift | Factors |
| --- | --- |
| Outcome-driven flow | Outcome-driven product development; Portfolio outcomes and investment strategy; Curated data |
| Cross-functional, AI-augmented teams | AI-Native ARTs; AI-Native Teams; AI Value Architect |
| Iterative learning and rapid experimentation | Product vision and roadmap; PI Outcome Planning; Sense and Respond |
| Development and innovation at scale | Intent, specifications and context; AI Innovation Pipeline; AI governance and ethics |

#### Sense and Respond
Every iteration the ART answers three questions from data, not slides: what did we learn, what moved, what should we stop. Trainyard pulls the agenda from the records: key result deltas, what released, discard candidates (no metric linked, no movement since release), the review queue and AI cost variance by workflow.

### PI planning tools compared: Jira plugins, dedicated boards, whiteboards and open source · https://trainyard.app/guides/pi-planning-tools
A capability comparison of PI planning tools: iObeya, Easy Agile, Agile Hive, BigPicture, PIPlanning.io, Miro and Trainyard, by hosting, PI planning support, ART boards, what sits beyond planning, integration and how each is priced.

#### Four families
PI planning tools come in four families. Jira plugins (Easy Agile, Agile Hive, BigPicture) keep planning inside Jira and price by every Jira user on the site. Dedicated planning servers (iObeya, PIPlanning.io) give a purpose-built room and integrate with the tracker. Whiteboards (Miro) give freedom and templates with no planning data model. Open source (Trainyard) gives the purpose-built room, the integrations and the choice of hosting it yourself.
Prices change and most vendors quote; this page compares what each does and what the price is counted by, not amounts.

#### Side by side
| Tool | Hosting | PI planning | ART and team boards | Beyond planning | Integration | Priced by |
| --- | --- | --- | --- | --- | --- | --- |
| Trainyard | Self-hosted, open source; hosted; Jira app and Azure DevOps extension | Integrated: PI planning, team breakouts, PI objectives, confidence vote | ART and team boards, PI planning board with milestones and dependency strings, ROAM, WSJF, retrospectives | Portfolio Kanban, roadmaps, SAFe canvases, value stream identification and mapping, AI portfolio, dashboards | Jira and Azure DevOps, two ways; imports from iObeya, Miro, Mural, monday.com | Trainer or train; people in the room are free |
| iObeya | On premises or separate server | Integrated web interface | ART board, ROAM, WSJF, retrospectives | Portfolio and more | Full integration through API and iFrame | Room, with a minimum number of rooms |
| Easy Agile | Jira plugin | Within Jira | ART and team boards | None | Jira only | Every Jira user on the site |
| Agile Hive | Jira plugin | Within Jira | ART board, ROAM, WSJF, team execution | Portfolio Kanban, roadmap, reporting | Jira only | User, by quote |
| BigPicture | Jira plugin | Within Jira | ART board, ROAM, WSJF, team execution | Project planning and reporting, OKR and risk widgets | Jira only | Every Jira user on the site |
| PIPlanning.io | On premises or separate server | Dedicated tool | ART and ROAM boards | None | Jira | Team |
| Miro | Public cloud | Not integrated; templates | Limited ART board | None | None for planning data | Every member |

#### Questions to ask before choosing
- Who is in the room? If it is a whole train including business owners who are not Jira users, per-Jira-user pricing counts them too.
- Where must the plan end up? If features and dependencies have to flow back into Jira or Azure DevOps, a whiteboard means retyping.
- Can you host it? Regulated organisations often cannot put planning data in a vendor's cloud; on-premises or open source are the options.
- What else do you need? Portfolio Kanban, value streams and canvases, or just the board.