← AI Recommendation IndexOpen Data · Rolling 4-week window

Payload vs PostgreSQL

A fact-by-fact comparison of Payload and PostgreSQL — sourced and dated — plus the part no generic comparison shows: which one AI assistants actually recommend more, engine by engine.

Measured by Orbatormethodology owner Henrik Tellewindow ends Sep 6, 2026

What AI recommends — by engine

Recommendation share = the % of sampled AI answers, over a 4-week rolling window, that recommend each product. Higher is better.

CLAUDE

Payload

0.8%

PostgreSQL

2.6%

GEMINI

Payload

0.8%

PostgreSQL

3.9%

GROK

Payload

1.5%

PostgreSQL

7.2%

CHATGPT

Payload

1%

PostgreSQL

2.8%

PERPLEXITY

Payload

0.8%

PostgreSQL

1.8%

The facts — sourced & dated

payloadcms.com

Features

  • Static A/B testingSource · Verified Sep 5, 2026
  • Publishing WorkflowsSource · Verified Sep 5, 2026
  • Multi-TenancySource · Verified Sep 5, 2026
  • White LabelSource · Verified Sep 5, 2026
  • LocalizationSource · Verified Sep 5, 2026
  • Access ControlSource · Verified Sep 5, 2026
  • AuthSource · Verified Sep 5, 2026
  • Visual EditingSource · Verified Sep 5, 2026
  • RAG + Vector EmbeddingSource · Verified Sep 5, 2026
  • SSOSource · Verified Sep 5, 2026
  • AI Auto-EmbeddingSource · Verified Sep 5, 2026

Platforms

  • Next.jsSource · Verified Sep 5, 2026
postgresql.org

Features

  • performanceSource · Verified Sep 5, 2026
  • feature robustnessSource · Verified Sep 5, 2026
  • object-relational database systemSource · Verified Sep 5, 2026
  • reliabilitySource · Verified Sep 5, 2026
  • reliability, feature robustness, and performanceSource · Verified Sep 5, 2026

Payload vs PostgreSQL — FAQ

Does AI recommend Payload or PostgreSQL more?

PostgreSQL is recommended more often: it appears in 3.2% of sampled AI answers versus 0.9% for Payload, measured across the live AI engines over a rolling 4-week window.

Which AI engines were used to compare Payload and PostgreSQL?

Recommendation share is measured on CLAUDE, GEMINI, GROK, CHATGPT, PERPLEXITY using neutral, buyer-shaped prompts (no product names), aggregated over a 4-week rolling window. See the methodology for prompt design and sample sizes.

Own one of these products? Claim your profile to keep its facts accurate.

Recommendation share and entity resolution follow the Index methodology. Facts are sourced from each vendor's own pages and re-verified periodically. Customer status does not affect measurement. Free to use with attribution to orbator.io.

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