I had been using the original tool for a while and wanted to see what else was out there. These are the alternatives that stood out after weeks of testing.
tensorflow is a strong Bricks alternative in the data category. Best for: data scientists, ML engineers, and analysts. Visit tensorflow →
| Category | data |
|---|---|
| Stars / adoption | 195,729 |
| Best for | data scientists, ML engineers, and analysts |
llama.cpp is a strong Bricks alternative in the data category. Best for: data scientists, ML engineers, and analysts. Visit llama.cpp →
| Category | data |
|---|---|
| Stars / adoption | 117,132 |
| Best for | data scientists, ML engineers, and analysts |
DeepSeek-V3 is a strong Bricks alternative in the data category. Best for: data scientists, ML engineers, and analysts. Visit DeepSeek-V3 →
| Category | data |
|---|---|
| Stars / adoption | 103,765 |
| Best for | data scientists, ML engineers, and analysts |
pytorch is a strong Bricks alternative in the data category. Best for: data scientists, ML engineers, and analysts. Visit pytorch →
| Category | data |
|---|---|
| Stars / adoption | 100,805 |
| Best for | data scientists, ML engineers, and analysts |
DeepSeek-R1 is a strong Bricks alternative in the data category. Best for: data scientists, ML engineers, and analysts. Visit DeepSeek-R1 →
| Category | data |
|---|---|
| Stars / adoption | 91,985 |
| Best for | data scientists, ML engineers, and analysts |
Bricks is a strong choice when you're already in the data ecosystem, or when its specific strengths (Generate spreadsheets from natural language prompts and Create dashboards and visualizations automatically) match your needs. If you're hitting limits, the alternatives above are the next best options.