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Inserting 2 million records per second into Postgres is achievable. The analysis explores five methods for inserting data into Postgres using Python, focusing on trade-offs in abstraction, safety, convenience, and performance rather than just speed. High-volume insert workloads are common in scenarios like loading records, syncing data, backfilling analytics tables, and ingesting events. Minor inefficiencies can lead to significant performance impacts. To interact with Postgres, the psycopg3 driver is used alongside SQLAlchemy, which provides two layers: Core and ORM. Psycopg3 is a low-level driver requiring manual SQL management, while SQLAlchemy Core offers a SQL abstraction, and the ORM maps Python classes to database tables, enhancing productivity but introducing overhead. Benchmarking involves measuring only the time spent transferring data from Python to Postgres, ensuring a fair comparison among methods. The fastest method may not always be the best due to maintenance costs, correctness guarantees, and cognitive load. The right insertion strategy depends on the existing data structure rather than just row count. The ORM is suited for CRUD-heavy applications, Core for data ingestion and analytics, and the Driver for maximum throughput in extensive writes. Performance issues can arise from mismatching abstractions, and reverting to a lower level may enhance performance. A guideline for choosing methods is: - Use ORM for applications prioritizing correctness and productivity. - Use Core for data movement or transformation balancing safety and speed. - Use Driver for pushing performance limits with raw power and full responsibility.