Akkio vs Make

Akkio is aimed at business users who want predictive analytics — forecasting future sales, predicting customer churn, scoring leads by likelihood to convert — without hiring a data science team or writing machine learning code. You connect a spreadsheet or common data source, pick a target outcome (e.g., "will this customer churn?"), and Akkio trains a predictive model automatically, which you can then apply to new data or connect to other tools via its API. A chat interface also allows plain-language questions about your connected data for quicker exploration alongside the predictive modeling. There's no free tier; plans start around $49/mo and scale with data volume and the number of models/users. Compared to chat-first analysis tools like Julius AI, Akkio is more purpose-built for repeatable predictive use cases than ad-hoc exploration, and like any predictive modeling tool, the quality of predictions depends heavily on having clean, sufficient historical data to train on.

Make (formerly Integromat) is a visual automation builder where you connect apps and services into "scenarios" using a drag-and-drop canvas, with branching logic, error handling, and data transformation tools that go beyond simple trigger-action automations. Built-in modules for OpenAI, Anthropic's Claude, Google Gemini, and other AI providers make it straightforward to add an AI step into a workflow — for example, summarizing incoming form submissions before routing them to a CRM. The free tier includes a meaningful number of monthly operations, generous enough for small personal automations; paid plans starting around $9/mo raise operation limits and unlock more frequent scenario execution. The visual builder is more powerful than simpler tools but has a steeper learning curve, and debugging complex multi-branch scenarios can take some practice.

• + Build predictive ML models from spreadsheets without coding
• + Plain-language chat interface for data Q&A
• + Pre-built use cases for common business problems
• + Integrates with common data sources and CRMs

• – No free tier
• – Less suited to ad-hoc exploration than chat-first tools
• – Prediction quality depends on input data quality

• + Powerful visual workflow builder
• + Deep customization with branching logic
• + Built-in AI modules (OpenAI, Claude, Gemini)
• + Generous free operation allowance

• – Steeper learning curve than simpler tools
• – Complex scenarios can be hard to debug
• – Operations-based pricing scales with usage