Linear vs Teamwork
Updated 2026 — Side-by-side comparison of features, pricing, and scores.
Quick Verdict
Linear is for engineering teams. Teamwork is for client-facing agencies. No meaningful overlap — choose based on your team type.
Linear
7.7/10
Great
Ease of Use9
Value for Money8.5
Support7
Features7
Scalability7.5
Integrations7
Teamwork
7.6/10
Great
Ease of Use8
Value for Money7.5
Support8
Features7.5
Scalability7.5
Integrations7
Linear and Teamwork serve completely different markets. Linear is a fast issue tracker for engineering teams. Teamwork is built for agencies with time tracking, billing, and client management. Linear: $0-16/user; Teamwork: $0-25.99/user.
Feature Comparison
| Feature | Linear | Teamwork |
|---|---|---|
| Task Management | ✓ Fast issue tracking | ✓ Tasks, subtasks, dependencies |
| Kanban Boards | ✓ Board view | ✓ Board view |
| Gantt Charts | ✗ No Gantt charts | ✓ Gantt chart view |
| Time Tracking | ✗ Not available | ✓ Built-in time tracking |
| Resource Management | ✗ Not available | ✓ Resource scheduling |
| Document Collaboration | ✓ Project documents | ✓ Teamwork Docs |
| Integrations | ✓ GitHub, Slack, Figma | ✓ Integrations marketplace |
| Custom Workflows | ✓ Opinionated workflows | ✓ Custom workflows |
| Reporting & Dashboards | ✓ Analytics on Business | ✓ Profitability reports |
| Guest Access | ✗ No guest access | ✓ Unlimited client users |
| Automations | ✓ Automation rules | ✓ 100/mo on Free |
| Templates | ✓ Issue templates | ✓ Project templates |
| Goals & OKRs | ✗ Not available | ✗ Not available |
| Sprints & Agile | ✓ Cycles (sprints) | ~ Basic sprints |
| Forms & Intake | ✗ Not available | ✓ Intake forms |
✓ Full support~ Limited✗ Not available
Pricing Comparison
Linear Pricing
FreeFree
BasicPopular$10/mo
Business$16/mo
Teamwork Pricing
FreeFree
DeliverPopular$13.99/mo
$11/mo billed annually
Grow$25.99/mo
$20/mo billed annually
FAQ
Is Linear or Teamwork better?
It depends on your needs. Linear scores 7.7/10 overall while Teamwork scores 7.6/10. Compare the detailed feature matrix above to find the best fit.