Elasticsearch

The memo spends few words on the cut and many on a brighter future, blaming AI and a leaner reorg while pledging to keep adding customer-facing sales roles.

Ex-staff and readers call the math a tell: a 7% trim that an ordinary hiring freeze would have reached, dressed as a sign of confidence.

The contrast with Elastic's 2022 layoff is the sharper point. Then it was framed as ZIRP ending; now, in good times, the cut is 6 points deeper.

The gist sets a deliberately high bar: pick a separate system only after you have pushed Postgres to its limits, documented why it fell short, and accepted the operational cost. PGMQ stands in for Kafka, pgvector and ParadeDB for Elasticsearch, Apache AGE for Neo4j, TimescaleDB for time-series.

Then a Google SRE flips it: you only reach for Kafka or Snowflake at the point Postgres stops being viable, so the comparison runs backwards. And a 20-year ops veteran argues one big database is harder to run than several small ones.