Decision Node vs Continue
Detailed side-by-side comparison to help you choose the right tool
Decision Node
🔴DeveloperDeveloper Tools
MCP server that records development decisions as structured JSON, embeds them as vectors, and enables semantic search over past decisions.
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Starting Price
$0/monthContinue
🔴DeveloperDeveloper Tools
Open-source AI coding assistant for VS Code and JetBrains — bring any model, any MCP server, and share reusable rules and agents across your team via Continue Hub.
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Starting Price
FreeFeature Comparison
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Decision Node - Pros & Cons
Pros
- ✓Semantic search finds relevant decisions even with different terminology
- ✓Works across all major AI coding tools via MCP
- ✓Local storage keeps sensitive decisions on-premises
- ✓Visual UI helps teams explore decision relationships
- ✓Structured format prevents decisions from becoming unstructured brain dumps
Cons
- ✗Requires a Gemini API key for vector embeddings (adds dependency and cost)
- ✗Only useful if the team consistently records decisions — needs adoption discipline
- ✗Local-only storage means no built-in team sync or cloud collaboration
- ✗Vector embeddings are Gemini-specific — no choice of embedding provider
- ✗No integration with existing decision documentation tools (ADR tools, Notion, etc.)
Continue - Pros & Cons
Pros
- ✓Open-source assistant for VS Code and JetBrains. This directly supports the primary workflow.
- ✓Bring-your-own-model: Anthropic, OpenAI, Ollama, local, any provider.
- ✓First-class MCP client — any MCP server becomes callable tools.
- ✓Continue Hub for sharing assistants, rules, prompts across a team.
Cons
- ✗At least one tier requires a sales quote, preventing a complete public budget calculation.
- ✗Features and limits can change by plan, billing interval, region, and release; verify current documentation.
- ✗MCP connectivity expands the permission surface and requires narrow scopes, approvals, logs, and credential rotation.
- ✗A representative pilot is necessary because a feature list does not establish reliability or operational fit.
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