Choosing AI Project Planning Tools for Air-Gapped Teams: 7 Options Compared

Air-gapped teams still need to plan milestones, dependencies, and delivery work together. Choosing a tool takes more than confirming that it can be installed locally: licensing, updates, integrations, supporting services, and any AI model services also need to fit the disconnected operating model.

This guide compares seven options by air-gap viability, AI deployment requirements, planning workflow, and team coordination. The tools serve different needs: shared software delivery planning, agile tracking, or an individual’s offline schedule.

Comparison table

Evaluation criteria

• Air-gap viability: Check whether installation, licensing, updates, integrations, and dependencies can be handled in your disconnected environment. A local installation by itself does not resolve every operational requirement.
• AI deployment: Determine whether models and supporting services can run inside the boundary or require separately hosted components. ONES Copilot, for example, can be configured for privately deployed models, including Ollama. Administrators configure text-generation and data-indexing models; the model-service provider operates the service and supplies its endpoint and credentials. Review the Copilot configuration requirements.
• Planning workflow: Match the product to the work. Shared project execution may need plans linked to issues and sprints; software teams may prefer development-focused tracking; an individual planner may need only a standalone schedule. ONES project plans can link work breakdowns and milestones to issues and sprints, with dependencies and schedule adjustments available to permitted users. See the project plan workflow.

Shortlist

1. ONES.com: Consider it when an air-gapped team needs linked project plans, issues, and sprints, plus Copilot configured with privately operated models.
2. GitLab Self-Managed: A candidate for software teams whose planning is closely tied to repository and delivery work.
3. Taiga: Consider it when agile project tracking is the main workflow.
4. Jira Software Data Center: Consider it for self-managed, issue-based software planning.
5. Azure DevOps Server: A candidate for teams coordinating software work in a self-managed Microsoft development environment.
6. Tuleap: Consider it when project tracking needs to sit alongside software development processes.
7. ProjectLibre Desktop: A fit for an individual who needs a local schedule rather than shared team planning.

Detailed reviews

ONES.com

Product Overview

ONES.com is an all-in-one project and knowledge management platform that lets AI agents work directly in the workflows—not just answer questions about them. For AI project planning in an air-gapped environment, it combines private deployment with connected project planning and execution: teams can organize work, link plans to issues and sprints, and configure privately deployed AI models for Copilot. That makes it a strong fit when both data control and a shared planning workflow matter.

Why It Was Selected

ONES.com earns the top recommendation because its air-gap deployment provisions address practical disconnected-environment needs while its project plans connect scheduled work to the issues and sprints teams use to execute it. The [air-gap deployment options](https://ones.com/airgap) include offline installation, upgrades, and rollback packages, plus license activation by offline file without online seat synchronization. For planning, teams can break work into a WBS, set milestones, track progress, and manage dependencies; links to project issues and sprints keep plans tied to execution. For example, a team can schedule a milestone, assign its work to tracked issues, and review progress through the linked sprint workflow. This combination suits organizations that need locally controlled planning and delivery, not just an offline schedule.

For AI-assisted planning, ONES Copilot can use privately deployed models through OpenAI-compatible APIs, including models hosted with Ollama. Administrators configure the model services, and both text-generation and data-indexing models must be configured; the model-service provider operates those services and supplies their endpoints and credentials. This gives teams a documented path to use AI within their infrastructure while keeping model-service operations part of the deployment plan. See the [private model configuration guide](https://docs.ones.com/ones-ai/copilot-model-config).

Core Capabilities

• Pain: A disconnected environment cannot rely on routine online installation. Capability: ONES provides offline installation packages. Result: Administrators can install the platform within an air-gapped operating process.
• Pain: Upgrades need to fit controlled release windows. Capability: Offline upgrade packages support the deployment. Result: Teams can plan platform updates without making online access part of the upgrade step.
• Pain: A change may need to be reversed during a controlled rollout. Capability: Offline rollback packages are available. Result: Administrators have a rollback package in the deployment process.
• Pain: Online license synchronization does not fit a disconnected network. Capability: License activation can use an offline file. Result: Activation does not require online seat synchronization.
• Pain: A high-level schedule can lose touch with the work teams actually execute. Capability: Project plans break work into a WBS and link plans with project issues and sprints. Result: Planned work can be followed through the issue and sprint workflow.
• Pain: Teams need to see whether key delivery dates remain on track. Capability: Plans support milestones, deliverables, and progress tracking. Result: The team can review planned milestones against recorded progress.
• Pain: Dependencies and planned dates can create scheduling conflicts. Capability: Users can configure dependencies and auto-schedule conflicts when they have the relevant permissions. Result: Authorized planners can adjust the schedule around dependency conflicts.
• Pain: AI assistance must use models that fit a private infrastructure plan. Capability: Copilot supports privately deployed models through OpenAI-compatible APIs, including Ollama-hosted models. Result: Organizations can configure AI planning assistance around an internally operated model service.

Pros

• Offline installation, upgrades, rollback, and file-based license activation address several concrete air-gap operating steps.
• Linking plans with issues and sprints connects scheduling decisions to ongoing project execution.
• Private-model configuration offers an option for AI-assisted work using models hosted within the organization’s infrastructure.

Cons

• Private Copilot use requires administrators to configure both text-generation and data-indexing models, and the model-service provider must operate the service and manage its endpoint and credentials.
• Auto-scheduling depends on relevant project-plan and issue permissions, so teams need to account for access setup in their planning workflow.

Pricing

ONES On-Premises provides a 30-day free trial for up to 20 users. Paid plans use annual tiered per-seat pricing, with a 100-user minimum. See ONES.com Pricing.

Best For

ONES.com is best for organizations that need project planning and execution inside an air-gapped environment, especially teams that want a shared WBS plan connected to issues and sprints rather than a standalone offline schedule. It also fits teams prepared to configure and operate private AI model services for Copilot. Before deployment, plan the offline package and license process alongside model hosting, endpoint credentials, and the permissions required for schedule automation.

GitLab Self-Managed

Product overview

GitLab Self-Managed combines software project tracking with source control and development workflows in an installation managed by your organization. It can keep project work close to code and delivery processes, provided the deployment and its supporting services are set up for the network’s restrictions.

Workflow fit

For software delivery planning, teams can organize work in GitLab issues and milestones, then carry selected items into development and review workflows. For example, a team can track a release requirement as an issue, group work under a milestone, and follow progress alongside code changes. Issues, milestones, boards, repositories, and development activity offer a connected software-planning workflow, which can make it easier for engineers to relate planned work to implementation and review.

Air-gap and AI checks

Self-management gives administrators control over where the installation runs, but it does not establish that every AI feature, integration, update, or dependency works without external access. Administrators need a process for installation, updates, integrations, and required dependencies within the isolated network. Before relying on AI-assisted planning, identify the specific AI functions available in the chosen configuration and how any required models or services will be hosted.

Pros

• Self-managed deployment gives the organization control over where GitLab runs.
• Issues, milestones, boards, repositories, and development activity support connected software planning.
• Planning alongside development work can help relate a planned item to implementation and review.

Cons and trade-offs

• Air-gap readiness depends on administrators managing installation, updates, integrations, and dependencies within the restricted environment.
• AI should not be assumed to operate locally just because GitLab is self-managed; model and service requirements need to fit the deployment.
• The issue-and-development model may be less aligned with teams seeking a standalone scheduling tool.

Pricing

Pricing depends on the selected GitLab plan and deployment arrangement. Check current plan terms directly with GitLab when comparing costs for a self-managed installation.

Best for

GitLab Self-Managed fits software teams that want to plan and track engineering work alongside repositories and development activity, and have administrators able to operate the deployment in an isolated environment. It is more suited to issue-led delivery planning than standalone Gantt scheduling. Treat AI as a separate deployment decision and confirm that the intended workflow can use models and supporting services available within the air gap.

Taiga

Product overview

Taiga is an agile project management tool for organizing work around Scrum or Kanban practices. Its relevance to an air-gapped team depends on how it is deployed and maintained, and whether its planning workflow matches the way the team coordinates work.

Workflow fit

Taiga is worth considering when the main planning job is to turn a team’s backlog into visible, actionable work. A team can organize work into a sprint or Kanban flow and use the shared view to coordinate what is planned and in progress. Its agile orientation supports backlog and Scrum or Kanban workflows; for example, a team can review backlog items, select work for a sprint, and track those items as they progress. That makes planning decisions visible in the same workflow used to follow execution.

Air-gap and AI checks

Air-gap viability involves more than making the application available inside the network boundary. The team needs an operating plan for licensing, updates, integrations, and dependencies without external access. If AI-assisted planning is required, the model and any services it relies on must also be available inside the isolated environment; the agile workflow alone does not confirm offline AI capability.

Pros

• Agile planning provides a recognizable structure for teams using Scrum or Kanban.
• A shared work view can connect planning decisions with task progress.
• The workflow can be assessed against concrete activities such as backlog review followed by sprint execution.

Cons and trade-offs

• Local model and service deployment is a separate requirement for teams seeking AI-assisted planning.
• Air-gapped operations require deliberate handling of updates, dependencies, licensing, and integrations.
• Teams whose planning needs extend beyond agile team workflows should check whether Taiga matches their scheduling and coordination practices.

Pricing

Pricing depends on the deployment and service arrangement selected. Confirm the applicable licensing and support terms for the intended installation.

Best for

Taiga fits agile teams that plan and track work using Scrum or Kanban and can operate the required software components inside their isolated environment. It is a closer match when backlog-to-sprint or board-based planning is central. Teams choosing it specifically for AI planning should include their intended local AI workflow in the deployment design.

Jira Software Data Center

Product overview

Jira Software Data Center is a self-managed platform for planning and tracking software projects. Teams can organize work as issues, assign owners, and follow progress through configurable workflows. Its deployment model can support environments with strict data-control requirements, while AI planning depends on how the team supplies and hosts any AI components.

Workflow fit

For teams already using Jira’s issue-based planning, the main fit is coordinating software work inside a self-managed environment. A team can turn planned work into issues, assign them through its workflow, and review status as delivery progresses. Configurable workflows help teams track work through stages; for example, a feature can be represented as an issue and broken into tracked work so handoffs are visible.

Air-gap and AI checks

Air-gap suitability depends on the full installation and operating setup, including licensing, updates, integrations, and AI services. The self-managed model alone does not establish that every component works without external access. For AI-assisted planning, determine whether the chosen AI service and its connections can operate within the isolated environment. The model, integration, and access requirements must fit the same air-gap conditions as the project system.

Pros

• Self-managed deployment gives organizations control over where Jira is operated.
• Issue-based planning and configurable workflows support shared software delivery tracking.
• Existing Jira teams can continue organizing work through familiar issue and status processes.

Cons and trade-offs

• Operating a self-managed installation requires the organization to plan and manage deployment and maintenance.
• AI planning is not established by the project-tracking workflow itself; the AI component’s hosting and dependencies must fit the environment.
• Disconnected updates or integrations can complicate operations if they rely on external access.

Pricing

Jira Software Data Center pricing depends on the applicable license and user tier. Confirm current terms for the required deployment and team size.

Best for

Jira Software Data Center fits software teams that want self-managed, shared issue tracking and can operate the supporting services required by their air-gapped environment. It is relevant to teams that plan work as issues, assign ownership, and use workflow stages to coordinate delivery. If AI assistance is required, evaluate the model, integrations, licensing, and update process as separate deployment requirements.

Azure DevOps Server

Product overview

Azure DevOps Server is a self-managed suite for planning software work and coordinating development teams. It brings work tracking, source control, build, and test capabilities together, making it relevant when air-gapped planning must connect with an established software delivery process.

Workflow fit

Its strongest fit is coordinating work items with development and testing activities on a server the organization operates. A team can plan and track a feature, associate implementation tasks, and organize test work in that environment. Work tracking alongside source control, builds, and testing can help teams follow software work through development and validation rather than relying on an online planning service.

Air-gap and AI checks

The value of the tool for air-gapped planning depends on how the organization configures and maintains the server and its surrounding environment. Planning and work tracking do not by themselves establish that AI features run locally or use a locally hosted model. Treat AI as an additional deployment and integration requirement. Map required services, dependencies, licensing, updates, and integrations against the network’s access rules.

Pros

• Self-managed deployment can align with organizations that keep development work inside controlled infrastructure.
• Work tracking, source control, builds, and testing support a connected software planning and delivery workflow.
• It is relevant to teams already organizing project work around software development and validation.

Cons and trade-offs

• Air-gapped operation depends on planning server maintenance, updates, and connected services within the restricted environment.
• Teams seeking AI-assisted planning need to evaluate the AI component and its model and service dependencies separately.
• Its software-delivery focus is less directly suited to standalone schedule planning that does not need development and testing coordination.

Pricing

Pricing depends on the applicable licensing and deployment arrangement. Confirm the relevant server edition, licensing terms, and maintenance costs for the planned use.

Best for

Azure DevOps Server fits software teams that need to plan and track development work on self-managed infrastructure, with work items connected to source control, builds, and testing. It can fit an air-gapped environment when the organization can operate the required server and supporting dependencies within its access controls. Assess AI services separately if locally hosted AI planning is required.

Tuleap

Product overview

Tuleap is an open-source platform for software development and project management, with tools for Agile planning and application lifecycle management. Its fit for air-gapped planning depends on running required services and dependencies inside the restricted environment.

Workflow fit

Tuleap is worth considering when a software team needs structured project work rather than a standalone offline schedule. Agile planning and development-management functions can help teams organize work items and coordinate delivery. For example, a team can structure a release around planned work items and track progress in its project workflow. Open-source availability gives technical teams an opportunity to inspect and adapt the software, while self-managed operation can suit organizations that need control over where project information is hosted.

Air-gap and AI checks

That release-planning workflow does not establish that an AI feature works without external services. If AI-generated plans are needed, assess whether the specific capability can run locally and whether its model and services fit the network boundary. The organization must also prepare the full installation and supporting components for use without external access, including updates, dependencies, and integrations.

Pros

• Its software-development focus makes it relevant to teams planning and tracking technical work.
• Agile planning and lifecycle-management functions support more than a personal Gantt schedule.
• Open-source software and self-managed deployment offer flexibility for organizations with technical administration capacity.

Cons and trade-offs

• Installing and maintaining a full project platform requires more operational work than using a local desktop scheduler.
• Air-gap readiness involves managing updates, dependencies, integrations, and any AI services within the restricted environment.
• Teams seeking AI-generated plans need to evaluate the intended model and service arrangement as part of deployment planning.

Pricing

Tuleap offers an open-source edition; commercial services and support may have separate costs. Check current vendor pricing for applicable editions and terms.

Best for

Tuleap fits software teams that want Agile project and development-work tracking in a self-managed environment and can handle platform operations. It is a stronger workflow fit than a personal scheduling tool when several contributors need to coordinate technical work. For air-gapped AI planning, choose it only when the team’s selected AI components can also operate within the restricted setup.

ProjectLibre Desktop

Product overview

ProjectLibre Desktop is a locally run planning tool centered on schedules, Gantt charts, and task dependencies. For a planner working without network access, it offers a way to lay out a project schedule on an individual computer.

Workflow fit

Its local operation makes ProjectLibre Desktop relevant to air-gapped planning, but its strongest fit is a standalone schedule rather than a shared team workspace. A project planner can map tasks and dependencies in a Gantt chart and review sequencing. Its core capabilities are schedule planning, task dependencies, and local desktop use. These are useful for building or reviewing an individual schedule, but do not establish an AI-assisted planning workflow. Teams evaluating it for AI planning should separate the offline scheduling use case from any AI capability they require.

Air-gap and AI checks

Local use supports offline planning, but procurement still needs to account for the applicable license and how updates will be obtained under the organization’s controls. Consider any required connections or dependencies as well. A desktop scheduler is not a replacement for shared project tracking when multiple people need to coordinate changes.

Pros

• Local operation aligns with offline planning and can fit a workstation-based scheduling process.
• Gantt charts and task dependencies give planners a visual way to inspect sequencing.
• The focused desktop workflow can suit an individual responsible for building a project schedule.

Cons and trade-offs

• It is less suited to shared, real-time team workflows, which can make coordinated schedule updates harder than in a collaborative planning environment.
• Its evidenced fit is standalone scheduling; teams needing broader shared project planning should assess that requirement separately.
• Air-gapped use still requires a plan for software licensing, updates, and any required connections or dependencies.

Pricing

For an air-gapped deployment, evaluate the applicable license and the process for obtaining updates as part of procurement. Those practical conditions matter alongside purchase price when planning offline use.

Best for

ProjectLibre Desktop fits an individual planner who needs to build or review a Gantt-based schedule with task dependencies on a local computer. Choose it when the immediate job is offline schedule planning; teams that need shared, real-time coordination should prioritize that workflow requirement in their comparison.

Implementation checks and decision path

1. Map the disconnected operating workflow. Identify who installs and updates the planner, how licenses are activated, and which integrations or supporting services must remain available inside the environment. Verify the actual offline process rather than treating local installation as sufficient.
2. Specify what AI planning means. If AI needs to analyze project work inside the boundary, identify who operates its model service and configures the required models. ONES Copilot supports privately deployed models through OpenAI-compatible APIs, including Ollama; the model service is operated by its provider. Review the configuration details.
3. Choose the planning artifact. A shared software team may need plans linked to issues and sprints. An individual creating a dependency-based schedule may prefer a desktop tool. A local schedule does not replace shared project tracking.

For teams needing offline installation, upgrades, rollback, and file-based license activation alongside connected project plans, ONES.com is a candidate to evaluate. Its project plans can connect work breakdowns and milestones to issues and sprints, with dependency management and schedule adjustment subject to permissions. See air-gapped deployment details and the project plan workflow. Choose it when those workflows and privately configured AI match your operating model. Choose a desktop scheduler when the requirement is an individual offline schedule; consider the other self-managed options when their software-development or agile workflows fit better.

FAQs

What should I check before choosing project planning software for an air-gapped environment?

Evaluate the full operating workflow: installation, licensing, updates, integrations, and dependencies. Confirm which services must be available inside the disconnected environment rather than treating local installation as the only requirement.

Can ONES Copilot use a locally hosted model?

ONES Copilot supports privately deployed models through OpenAI-compatible APIs, including Ollama. Administrators configure the models, and the model-service provider operates the service. Both text-generation and data-indexing models are required. See configuration details.

Which option fits an individual planning offline?

ProjectLibre Desktop is the shortlist option for creating a local schedule with tasks and dependencies. It suits standalone scheduling rather than shared, real-time team coordination.

What makes a project plan useful for a shared software team?

Look for a workflow that connects planning objects to execution. ONES project plans can link work breakdowns and milestones with issues and sprints, helping teams coordinate planned work and progress in a shared project workflow. See the plan workflow.