Seat-Based Billing
Pricing.
Eliminate manual coordination waste and keep your team aligned. Pay strictly per seat, monthly, with zero long-term commitments.
Perfect for solo product managers, independent contractors, or consultants managing single-client specifications.
- 1 active product workspace
- Standard limits on living specifications
- Cloud agents
- Product Intelligence
- Bidirectional sync (Jira, Linear, Notion, ClickUp)
- 100% human-in-the-loop review
Engineered for product and development teams to completely eliminate status meetings and alignment drift.
- Unlimited active workspaces
- Extended limits on living specifications
- Custom agent instruction sets & context rules
- Product Intelligence
- Bidirectional sync (Jira, Linear, Notion, ClickUp)
- 100% human-in-the-loop review
- Premium Slack/Teams support channel
Enterprise Operations
Need custom integrations, dedicated agent clusters, SOC-2 verification, or custom MSA terms? Our Enterprise plan offers isolated instances with high security requirements.
Frequently Asked Questions
Straight answers regarding billing, usage boundaries, integrations, and data privacy.
How does seat-based billing work?
You are billed strictly for active seats in your workspace. You can add or remove team members at any point during your billing cycle. If you remove a member, your account is credited proportionally on your next invoice.
Is there a long-term commitment?
No. All subscriptions are strictly month-to-month. You can upgrade, downgrade, or cancel your workspace settings at any time from your admin panel with zero penalty fees.
How do secure integrations write back?
Productpanda communicates via secure OAuth tokens. Team plans utilize write permissions to sync with platforms like Jira or Linear. Critically, no code changes or ticket writes are committed without explicit confirmation from your operators.
Do you train models on our proprietary data?
Never. We adhere to enterprise-grade compliance. All customer signals, code repos, and specifications remain local and are never used to train public LLM models.