How Writer Keeps Its AI Infrastructure Running with Snowflake Hybrid Tables

TL;DR

Writer reduced LLM tracing write failures from 45% to near-zero by migrating to Snowflake Hybrid Tables, improving P50 latency by 15x.

Key facts

• Peak write volume: 600
• Daily trace volume: 1.3 million
• Initial P50 insertion latency: 2,362
• Post-migration P50 latency: 154
• Initial write failure rate: 45

Why it matters

The migration enabled reliable data ingestion for QA and observability without adding external infrastructure or complex ETL.

Key points

• Writer's LLM tracing pipeline, generating 1.3 million rows daily, experienced 45% write failure rates due to lock contention on standard Snowflake tables.
• Writer implemented a dual-table architecture: Hybrid Tables handle high-volume API writes, while a Snowflake Task incrementally moves data to standard tables for analytical queries.
• The migration allowed Write to downsize its ingest pipeline warehouse from medium to extra small while maintaining performance.

Notable quotes

INSERT latency dropped ~15x: P50 went from 2,362ms to 154ms.

— Aeril Xie

Write failures went from ~45% to essentially zero

— Aeril Xie

Structured claims — 12

1. 1
   Standard Snowflake tables experienced 45% failure rates for insertions due to lock contention.

   “Failure rates climbed to 45% of all insertions.”
2. 2
   P50 insertion latency dropped from 2,362ms to 154ms after switching to Hybrid Tables.

   “INSERT latency dropped ~15x: P50 went from 2,362ms to 154ms.”
3. 3
   Write failures dropped from 45% to near zero after switching to Hybrid Tables.

   “Write failures went from ~45% to essentially zero”
4. 4
   Writer downsized the warehouse powering its ingest pipeline from medium to extra small.

   “We were able to downsize the warehouse powering our ingest pipeline from medium to extra small, while handling the same or higher write traffic without contention.”
5. 5
   Writer uses a Snowflake Task to incrementally copy rows from the hybrid table to a standard table.

   “A Snowflake Task runs on a short cadence, incrementally copying new rows into a standard Snowflake table clustered by timestamp.”
6. 6
   Writer routes every customer request through LLM inference providers.

   “As an enterprise AI platform, we route every customer request through LLM inference providers.”
7. 7
   Writer's LLM tracing pipeline generates up to 600 rows per minute at peak.

   “Our LLM tracing pipeline writes to Snowflake at high velocity: up to 600 rows per minute at peak”
8. 8
   Writer's primary traces table receives roughly 1.3 million rows per day.

   “and roughly 1.3 million rows per day for our primary traces table.”
9. 9
   P50 insertion latency on standard Snowflake tables reached 2,362 milliseconds.

   “INSERT latency at P50 was reaching 2,362 milliseconds.”
10. 10
    Writer evaluated writing tracing data to Postgres as an alternative to Snowflake.

    “We also briefly considered two alternatives: writing the tracing data directly from our API to a Postgres table (the traditional transactional approach)”
11. 11
    Snowflake Hybrid Tables use row-oriented storage with row-level locking.

    “They're designed for high-concurrency write workloads, use row-oriented storage with row-level locking, and coexist natively with standard Snowflake tables.”
12. 12
    Writer is considering using Hybrid Tables for high-volume API logging data.

    “We have high-volume logging data from our API that currently routes to a third-party streaming service. If Hybrid Tables can serve as a stable destination for that traffic”

Source

Source

snowflake-medium-rss

Record title

How Writer Keeps Its AI Infrastructure Running with Snowflake Hybrid Tables

Author

Aeril Xie

Published

Oct 26, 2026

URL

https://medium.com/snowflake/how-writer-keeps-its-ai-infrastructure-running-with-snowflake-hybrid-tables-80f55dd652fa

Manifest ID

1793024889149137424

Source evidence

Full Signal Claims and Brief (JSON) · Markdown