Decision Support · Side-by-side
Compare pricing, strengths, and use cases so it is easier to pick the right fit.
Change tools
For everyday users who need quick, beautiful charts from spreadsheets or Google Sheets, Graphy wins hands-down with its free tier and intuitive AI assistant. Sisense is far more powerful for embedding analytics into apps or handling complex data modeling, but its cost and learning curve make it overkill for most individuals. The single biggest difference: Graphy is a personal visualization tool you can use in minutes, while Sisense is enterprise-grade analytics for developers and teams.
Graphy
Sisense
Scores at a glance
Choose Graphy if
Choose Sisense if
Key differences
Facts side by side
| Graphy | Sisense | |
|---|---|---|
| Free plan | ||
| Mobile app | ||
| API access |
Common questions
Yes, absolutely. Graphy is designed for exactly that — upload your Excel file, describe what you want in plain English, and get a beautiful chart in seconds. Sisense would require connecting a database and learning its modeling tools first.
No. Neither tool has a mobile app, so you'll need a laptop or desktop to use them.
Graphy's free tier is the clear winner — you can create and export charts without paying anything. Sisense has no public pricing and is designed for companies, not individuals.
Yes, Graphy supports iframe embeds and interactive URLs, so you can embed live charts into a website or blog. Sisense also supports embedding, but it's far more complex to set up.
Yes, Graphy can connect via SQL queries, but it's not its primary strength. Sisense is built for SQL databases and cloud warehouses, making it better for live database connections.
Graphy is the easy, affordable choice for everyday chart-making; Sisense is powerful but only worth it if you're embedding analytics into a product.
If you're a regular person who just wants to turn a spreadsheet into a clean chart without headaches, start with Graphy's free tier — you'll be done in minutes. Leave Sisense to the developers and companies with big budgets and complex data needs.
