Gemini Batch API vs Standard API: The Cost & Speed Difference
Learn how to use Google Gemini's Batch API to process large volumes of requests at 50% lower cost than the standard API, no rate-limit stress, no waiting on synchronous calls. In this hands-on masterclass, we start with a single API call for sentiment analysis, move to a small inline batch, then build a full JSONL batch pipeline that scales to tens of thousands of requests. By the end, you'll know how to format, submit, monitor, and extract results from both batch methods in Python using Google Colab.
📁 Dataset used in this video: https://github.com/mirzarahim2197/bai...
Chapters:
0:00 - Intro: what you'll learn
0:52 - Colab setup & loading the dataset
6:52 - Single API call: sentiment analysis with Gemini 2.5 Flash
9:29 - Formatting an inline batch request
20:07 - Submitting the batch & checking job status
26:01 - Extracting inline batch results
29:05 - Why use full JSONL batches for large datasets
37:50 - Creating & uploading the JSONL file
42:16 - Submitting the full batch job
48:35 - Downloading & analyzing the final results
👨🏫 About the Instructor
✔ 16+ years in Data Science
✔ Author of 3 books on AI and Machine Learning
✔ Startup mentor
✔ Corporate Trainer and Educator
✔ Teaching AI, Machine Learning & Data Science for 10+ years
📌 Connect & Learn More
👤 Rahim Baig on LinkedIn:
👉 / rahim-baig
🎓 Coursera Instructor Profile:
👉 https://www.coursera.org/instructor/~...
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