Lutra AI vs Make.com
Detailed side-by-side comparison to help you choose the right tool
Lutra AI
🟡Low CodeAI Automation
Lutra is an AI assistant that automates multi-step workflows across business apps by generating Python code on the fly — letting non-developers describe what they want in plain English and getting reliable, inspectable automations against Gmail, Slack, Notion, Salesforce, and dozens of other tools.
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CustomMake.com
🟢No CodeAutomation & Workflows
Make.com: Visual automation platform with AI integration and workflow orchestration
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FreeFeature Comparison
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💡 Our Take
Choose Lutra AI if non-technical users need to describe an outcome and let an agent build the automation across email, documents, spreadsheets, and CRMs. Choose Make if your team wants a visual scenario builder with granular control over each module, branch, and data transformation.
Lutra AI - Pros & Cons
Pros
- ✓Executes actual code for tasks rather than only producing chat responses, which helps make recurring automations more consistent and inspectable.
- ✓Supports a broad set of named work apps, including Google Docs, Sheets, Gmail, Calendar, Outlook, OneDrive, Slack, GitHub, HubSpot, Airtable, LinkedIn, and CRMs.
- ✓Lutra's MCP page describes it as an MCP-compatible client and says users can connect any MCP-compatible service or bring their own MCP server.
- ✓Connected-app actions such as reading or writing data in Slack, Google, Airtable, GitHub, and Outlook are listed as 0-credit actions.
- ✓Reusable Playbooks can be saved, scheduled, and shared, giving teams a way to standardize repeated processes instead of re-prompting from scratch.
- ✓The website states that Lutra is SOC 2 compliant and uses OAuth integration, with visibility into actions and code for security-sensitive automations.
Cons
- ✗Custom pricing still requires contacting Lutra, which makes enterprise budget comparison harder before sales review.
- ✗The credit model adds complexity because actions such as web search, website retrieval, person profiles, company profiles, and AI processing can consume different amounts.
- ✗AI function credit usage can vary based on text size, document length, and complexity, so large PDF or spreadsheet tasks may be less predictable.
- ✗Lutra’s public website emphasizes many common business apps, but it does not claim the thousands of prebuilt connectors offered by long-established automation platforms.
- ✗Teams that need full visual workflow control, self-hosting, or deterministic node-by-node automation may prefer a platform such as n8n or Make.
Make.com - Pros & Cons
Pros
- ✓Visual scenario builder supports routers, filters, scheduled runs, subscenarios, real-time execution monitoring, and reusable scenario templates.
- ✓Large connector ecosystem with 3,000+ standard apps, 350+ AI apps, and 300+ Make API endpoints for custom automation work.
- ✓Free plan includes 1,000 credits/month, 2 active scenarios, 3,000+ apps, routers and filters, and customer support with no time limit.
- ✓Paid plans support unlimited active scenarios, 1-minute scheduling, higher data transfer limits, and access to the Make API starting at $12/month for 10,000 credits.
- ✓Enterprise options include SSO, domain claim, on-prem agents for local systems such as SAP, advanced security features, 24/7 Enterprise support, and overage protection.
- ✓Make is used by 400,000+ customers across 200+ countries and territories, backed by a team of 350+ employees and parent company Celonis.
Cons
- ✗Credit-based billing can become hard to forecast because each module action in a scenario consumes credits.
- ✗The Free plan is limited to 1,000 credits/month, 2 active scenarios, 15-minute minimum scheduling, 5-minute maximum execution time, and 5 MB file processing.
- ✗Native AI features are useful for orchestration, but Make is not a full model development, vector database, or dedicated RAG platform.
- ✗Advanced governance and security features such as SSO, domain claim, audit logs, 24/7 support, and on-prem agents are Enterprise-oriented.
- ✗Complex scenarios still require careful technical design, especially when handling retries, data transformation, API limits, and high-volume AI calls.
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