Grov vs Propane: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Grov and Propane — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Grov
Grov
Collective AI memory for engineering teams that helps AI remember past learnings to accelerate shipping and reduce repeated exploration.
Key features
- Persistent Team Memory: Stores and indexes engineering knowledge and past AI interactions so solutions and context are retained across projects and time.
- Contextual Retrieval: Surfaces relevant past learnings and examples in response to developer queries to reduce repeated exploration and accelerate debugging.
- Shared Knowledge Base: Enables team-wide access to confirmed fixes, patterns, and decisions so individual learning becomes collective and reusable.
- Continuous Learning: Updates the collective memory as the team interacts, allowing AI responses to improve based on cumulative team experience.
- Workflow Integration: Designed to fit engineering workflows by making remembered context available where developers work (e.g., pull requests, issue threads).
- Reduced Investigation Time: Aggregates prior troubleshooting steps and solutions to shorten time-to-resolution for recurring technical problems.
- Persistent team memory for engineering knowledge
- Searchable knowledge base across code, PRs, and docs
- Contextual retrieval to provide relevant context to models
- Integrations with engineering workflows and tools
- Access controls and team management
- Persistent team memory that records learnings and decisions
- Queryable indexed knowledge retrieval to surface prior context
- Shared, team-scoped knowledge store for engineering organizations
- Integration points with engineering workflows and tools
- Reduces duplicated exploration by recalling past findings
- Supports faster onboarding by exposing historical context
- Facilitates incident retrospectives and postmortem knowledge capture
- Search and discovery across captured team knowledge
Best for
- Onboarding New Engineers: Quickly bring new team members up to speed by providing immediate access to historical decisions, fixes, and context stored in the collective memory.
- Recurring Bug Resolution: Retrieve past debugging steps and proven fixes for recurring issues so engineers can apply known solutions instead of re-exploring.
- Contextual Code Reviews: Surface relevant previous discussions, design rationale, or related code examples during code review to inform decision-making.
- Faster Incident Response: Use preserved incident runbooks and prior remediation actions to accelerate diagnosis and recovery during outages.
- Knowledge Consolidation: Convert individual learnings from experiments or investigations into team-accessible artifacts that improve future AI-assisted recommendations.
- Onboarding new engineers with historic decisions and context
- Faster ramp-up by surfacing relevant code and docs
- Preserving and reusing debugging and design learnings
- Providing contextual history to LLMs used by the team
- Centralizing tribal knowledge and engineering notes
- Onboarding new engineers by exposing past decisions and context
- Preventing repeated troubleshooting by recalling prior resolutions
- Capturing postmortem findings and retaining incident knowledge
Propane
Propane
A product-management system that connects all your customer data into shared context for product teams and their AI agents.
Key features
- Customer Data Connection: Connect every tool, interaction and competitor into one place so your whole team and agents see the same context.
- Context in Minutes: Link your stack and surface your first customer context within minutes of setup.
- Signal Extraction: Turn each data point into a real, actionable signal teams can build from rather than raw noise.
- Collaborative Canvases: Prioritize, sketch and shape roadmaps together in shared spaces with full context.
- Agent Hand-off: Hand the full context to AI agents so they work with more accuracy, higher quality and faster shipping.
- Stack Integrations: Unlimited integrations with HubSpot, Intercom, Slack and other product tools.
Best for
- Roadmap Prioritization: Decide what to build next using connected customer signals instead of guesswork.
- Customer Intelligence: Centralize fragmented customer and competitor data into one queryable context.
- Agent-Assisted Building: Give AI agents full product context so they ship higher-quality work faster.