How to scale Gen AI to billions of rows in BigQuery at a fraction of the cost

Key Concepts

• BigQuery AI Functions: Managed functions (e.g., ML.GENERATE_TEXT, ML.CLASSIFY) that integrate LLM reasoning directly into SQL queries.
• Optimized Mode: A feature that scales AI analysis by distilling LLM intelligence into a lightweight, local model.
• Model Distillation: The process of training a smaller, faster model using the outputs (labels) of a larger, more complex LLM.
• Embeddings: Vector representations of data that capture semantic meaning, used here to train the distilled model.
• Token Consumption: The primary cost driver in LLM usage; optimized mode significantly reduces this by minimizing API calls.
• Inference Latency: The time taken to process data; optimized mode reduces this by shifting computation from external LLM calls to internal BigQuery compute.

1. The Challenge: Scaling AI Analysis

Traditionally, using LLMs for large-scale data analysis (millions of rows) involves a trade-off: either accept high costs and slow processing times by sending every row to an LLM, or settle for less powerful, traditional machine learning techniques. Sending every row to an LLM for tasks like classification or filtering is inefficient when the data volume reaches millions of records.

2. The Solution: Optimized Mode for BigQuery AI Functions

Optimized mode allows enterprises to achieve "LLM-quality" results at a fraction of the cost and time. Instead of performing per-row inference for every single record, the system uses a distillation methodology.

The Step-by-Step Process:

1. Sampling: BigQuery takes a small, representative sample of the dataset.
2. LLM Labeling: The LLM labels this sample, providing the "ground truth" or high-quality reasoning.
3. Distillation: BigQuery trains a lightweight, local model using the data embeddings and the LLM-generated labels.
4. Internal Execution: The distilled model runs directly within BigQuery’s compute environment, using semantic embeddings to process the remaining rows without needing further LLM API calls.

3. Real-World Application: "Let’s Drive" Case Study

The video demonstrates the efficacy of optimized mode using a fictitious self-driving car company, "Let’s Drive."

• Scenario A: Obstacle Classification
  • Dataset: 34,000 images from front-facing cameras.
  • Standard Query: Sent all 34,000 rows to the LLM.
    • Result: 55 million tokens consumed; ~16 minutes execution time.
  • Optimized Query (with embeddings parameter):
    • Result: ~3 million tokens consumed (a 94% reduction); ~2 minutes execution time.
• Scenario B: Voice Command Filtering
  • Dataset: 50,000 driver voice commands.
  • Methodology: Used autonomous embedding generation. BigQuery auto-detected the embedding columns, triggering optimized mode without additional code.
  • Outcome: Successful filtering of "slow down" commands with the majority of rows processed via the optimized distilled model.

4. Key Arguments and Benefits

• Cost Efficiency: By reducing token consumption by over 90%, organizations can scale AI analysis to billions of rows without prohibitive costs.
• Performance: Moving the heavy lifting from external LLM inference to internal BigQuery compute significantly reduces query latency.
• Scalability: The system is designed to handle massive datasets; the more rows processed, the greater the relative savings in time and tokens.
• Ease of Use: The feature is either triggered by a simple parameter change or automatically detected if autonomous embeddings are present, requiring minimal developer intervention.

5. Synthesis and Conclusion

Optimized mode for BigQuery AI functions effectively removes the "speed vs. cost" barrier for enterprise AI. By leveraging model distillation and semantic embeddings, BigQuery allows users to maintain the high-quality reasoning of LLMs while achieving the performance characteristics of traditional database operations. This capability is particularly transformative for high-volume tasks like classification, rating, and filtering, enabling data-driven organizations to extract insights from massive datasets that were previously too expensive or slow to analyze.