Pendium
ParadeDB
ParadeDB
Visibility46
Vibe98
Businesses/Database Software/ParadeDB
ParadeDB
AI Visibility & Sentiment

ParadeDB

ParadeDB is a modern Elasticsearch alternative built as a Postgres extension, delivering simple, elastic-quality search capabilities directly within PostgreSQL. The company enables developers to implement full-text, hybrid, and faceted search without the complexity of managing separate search infrastructure.

Active Monitoring
paradedb.com
Database SoftwareStartups
AI Visibility Score
46/100

Moderate

Sentiment Score
98/100
AI Perception

Summary

ParadeDB has achieved a remarkable stronghold within Google's AI ecosystem, securing a 100% mention rate in Gemini and AI Overviews as the go-to solution for Elasticsearch replacement. However, this dominance is sharply undermined by a total visibility blackout in ChatGPT and Claude, creating a critical risk where the brand is invisible during pure conversational research. The data reveals a polarized landscape where ParadeDB is a search-engine darling but remains a ghost in the training sets of the most popular large language models.

Value Proposition

Elastic-quality search directly in Postgres with zero ETL, eliminating the need for complex data synchronization and external search infrastructure while maintaining full SQL compatibility.

Overview

ParadeDB is a modern Elasticsearch alternative built as a Postgres extension, delivering simple, elastic-quality search capabilities directly within PostgreSQL. The company enables developers to implement full-text, hybrid, and faceted search without the complexity of managing separate search infrastructure.

Mission

To build a modern Elasticsearch alternative on Postgres, giving developers elastic-quality search without the burden of managing separate search infrastructure.

Products & Services
BM25 full-text search extension for PostgresHybrid search combining text and vector searchFaceted search and aggregationsManaged ParadeDB Cloud (private beta)Enterprise self-hosted deployment with support
Agent Breakdown

AI Platforms

How often do different AI platforms reference ParadeDB?

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Conversation Analysis

Topics

What conversations is ParadeDB included in — or excluded from?

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Buyer Personas

Personas

Who does each AI platform recommend ParadeDB to, and when?

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Programmatic Testing

Sample Conversations

We programmatically analyze questions that real customers are asking to AI agents and chatbots, extract brand mentions and sentiment, analyze every response, and synthesize the data into an action plan to increase AI visibility.

ChatGPTChatGPTClaudeClaudeGeminiGeminiAI OverviewsAI Overviews
Simplifying Search Infrastructure(2 queries)

“i'm tired of managing an elasticsearch cluster just for basic search on my postgres data, any better ways to do this?”

2/4 platforms mentioned

ChatGPTChatGPT
1.Elasticsearch
2.Postgres
3.pg_trgm
4.Typesense
5.MeiliSearch

+13 more

ClaudeClaude
1.Elasticsearch
2.PostgreSQL
3.Amazon OpenSearch
4.Elastic Cloud
5.Typesense

+4 more

GeminiGemini
1.Elasticsearch
2.PostgreSQL
3.ParadeDB
4.Tantivy
5.pg_trgm

+7 more

AI OverviewsAI Overviews
1.Elasticsearch
2.blog.blockost.com
3.Postgres
4.ParadeDB
5.pg_search

+8 more

“how can i do full-text search directly in postgres without using a separate search engine, what extensions should i look at?”

2/4 platforms mentioned

ChatGPTChatGPT
1.PostgreSQL
2.PGroonga
3.ZomboDB
4.Elasticsearch
5.Prometheus

+7 more

ClaudeClaude
1.PostgreSQL
2.Elasticsearch
3.pg_bigm
4.RUM
5.pgroonga

+3 more

GeminiGemini
1.Elasticsearch
2.Postgres
3.Lucene
4.ParadeDB
5.Tantivy

+8 more

AI OverviewsAI Overviews
1.PostgreSQL
2.Elasticsearch
3.ParadeDB
4.pg_trgm
5.unaccent

+3 more

Implementing Advanced Search Features(1 query)

“help me implement hybrid search with vectors and keyword matching in my postgres database, what are the best tools?”

2/4 platforms mentioned

ChatGPTChatGPT
1.Postgres
2.pgvector
3.pg_trgm
4.Qdrant
5.Milvus

+11 more

ClaudeClaude
1.PostgreSQL
2.pgvector
3.Elasticsearch
4.Logstash
5.Weaviate

+4 more

GeminiGemini
1.pgvector
2.PostgreSQL
3.Supabase
4.Neon
5.Amazon RDS
13.Paradedb

+7 more

AI OverviewsAI Overviews
1.pgvector
2.pg_search
3.ParadeDB
4.Elasticsearch
5.pg_textsearch

+2 more

Enterprise Database Scalability & Deployment(1 query)

“looking for a managed postgres search service that handles full-text search and scales well for a startup”

2/4 platforms mentioned

ChatGPTChatGPT
1.Postgres
2.Citus
3.Hyperscale
4.Supabase
5.Neon

+20 more

ClaudeClaude
1.Supabase
2.PostgreSQL
3.Elastic Cloud
4.Elasticsearch
5.Neon

+2 more

GeminiGemini
1.PostgreSQL
2.Elasticsearch
3.ParadeDB
4.Tantivy
5.Rust

+8 more

AI OverviewsAI Overviews
1.Neon
2.pg_search
3.Tantivy
4.Lucene
5.Xata
9.ParadeDB

+4 more

Category Trust & Search Tool Evaluation(1 query)

“what are the most reliable postgres extensions for enterprise-grade search right now?”

2/4 platforms mentioned

ChatGPTChatGPT
1.PostgreSQL
2.pg_trgm
3.unaccent
4.fuzzystrmatch
5.rum

+8 more

ClaudeClaude
1.PostgreSQL
2.pg_search
3.pgvector
4.Elasticsearch
5.elasticsearch_fdw

+2 more

GeminiGemini
1.pg_search
2.ParadeDB
3.Elasticsearch
4.Algolia
5.Tantivy

+12 more

AI OverviewsAI Overviews
1.PostgreSQL
2.pg_trgm
3.pgvector
4.Percona
5.Northflank
9.ParadeDB

+6 more

Analysis

Key Insights

What AI visibility analysis reveals about this brand

Strength

Absolute dominance in Google Gemini and AI Overviews, consistently ranking in top positions for infrastructure simplification queries.

Strength

High resonance with the 'Security-Conscious Lead Developer' persona, achieving an impressive average position of 3.3.

Strength

Successfully positioned as the primary alternative for users tired of managing Elasticsearch clusters, often appearing as the top recommendation for Postgres-native search.

Gap

Near-zero presence in ChatGPT and Claude across all non-brand queries, suggesting a significant lag in LLM training data integration.

Gap

Significant competitive disadvantage against pgvector and Supabase, who maintain broader cross-platform visibility in conversational AI contexts.

Gap

Underperforming in 'hybrid search' queries on Gemini, where positions drop as low as #13 compared to the #2 or #3 spots held for general Postgres extensions.

Opportunity

Leverage existing momentum in AI Overviews to capture 'Postgres extension' category authority by targeting technical comparison keywords.

Opportunity

Aggressively pursue developer-focused content syndication on platforms frequently crawled by OpenAI and Anthropic to break the conversational visibility barrier.

Opportunity

Develop specific messaging for the 'Efficiency-Focused Startup CTO' who is currently seeing less frequent mentions (50%) than their DevOps counterparts.

Technical Health

Site Health for AI Visibility

How well ParadeDB's website is optimized for AI agent discovery and comprehension.

88/100
17 passed 5 warnings 1 issues
Audited 2/28/2026
Crawlability100

Can AI bots find your pages?

Technical90

SSL, mobile, doctype basics

On-Page SEO80

Titles, descriptions, headings

Content Quality73

Word count, depth, freshness

Schema Markup85

Structured data for AI comprehension

Social & OG87

Open Graph, Twitter cards

AI Readability60

How well AI can parse your content

Critical Issues

!

Page has no meta description

Add a <meta name="description"> tag summarizing the page (150-160 characters).

Warnings

!

4 render-blocking resources are slowing initial render

Defer non-critical JS with async/defer. Inline critical CSS. Move stylesheets to load asynchronously.

!

Title is too short (24 characters)

Expand the title to 50-60 characters with descriptive keywords.

!

Content may be too short

Expand your content to at least 500 words with valuable information.

!

Missing Open Graph tags for social sharing

Add og:title, og:description, and og:image meta tags.

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Brand Identity

Brand Voice & Style

How AI perceives ParadeDB's communication style and personality

ParadeDB communicates with a developer-centric, technically confident voice that balances deep expertise with accessibility. The brand uses clear, direct language that respects developers' intelligence while avoiding unnecessary jargon. There's an underlying tone of pragmatism—acknowledging real pain points with Elasticsearch and offering straightforward solutions. The voice is professional but not corporate, often using relatable developer experiences to connect with the audience.

Core Tone Traits

Technically Confident

Speaks with authority on database and search technology, using precise terminology and concrete benchmarks

Developer-Empathetic

Acknowledges real pain points developers face and positions solutions around their actual workflows

Clear and Direct

Avoids marketing fluff, preferring straightforward explanations and letting the product speak for itself

Pragmatically Optimistic

Focuses on practical benefits and real outcomes rather than hype, backed by customer success stories

Competitive Landscape

Related Ecosystem

Related products and services that AI mentions in conversations alongside or instead of ParadeDB

1Elasticsearch39 mentions
2pgvector32 mentions
3ParadeDB25 mentions
4Postgres21 mentions
5PostgreSQL20 mentions
6Supabase19 mentions
7Tantivy18 mentions
8pg_trgm16 mentions
9MeiliSearch16 mentions
10Algolia14 mentions
11Typesense12 mentions
Source Intelligence

Citations

Sources that AI assistants cite. Getting featured here improves visibility.

blog.blockost.com

https://blog.blockost.com/

Referenced in 1 query

Review
Postgres Full Text Search vs the rest - Supabase

https://supabase.com/blog/postgres-full-text-search-vs-the-rest

Referenced in 4 queries

Review
Comparing Native Postgres, ElasticSearch, and pg_search for ...

https://neon.com/blog/postgres-full-text-search-vs-elasticsearch

Referenced in 2 queries

Review
Full Text Search over Postgres: Elasticsearch vs. Alternatives

https://www.paradedb.com/blog/elasticsearch-vs-postgres

Referenced in 2 queries

Review
Why we replaced Elasticsearch with Postgres Full-Text Search

https://blog.blockost.com/why-we-replaced-elasticsearch-with-postgres-full-text-search

Referenced in 1 query

Review
The 10 Most Popular Postgres Extensions on Neon

https://neon.com/blog/ten-most-popular-postgres-extensions

Referenced in 1 query

Review
18: 12.9. Preferred Index Types for Text Search - PostgreSQL

https://www.postgresql.org/docs/current/textsearch-indexes.html

Referenced in 3 queries

Review
PostgreSQL as a Search Engine Alternative to Elasticsearch

https://www.linkedin.com/posts/ashishume_postgresql-elasticsearch-systemdesign-activity-7420756984372592640-qW3n

Referenced in 1 query

Pitch Story
7 Postgres Extension Combos That Feel Like Superpowers

https://medium.com/@Nexumo_/7-postgres-extension-combos-that-feel-like-superpowers-6d753ee56965

Referenced in 2 queries

Review
Best Elasticsearch alternatives in 2025 for your use case - Algolia

https://www.algolia.com/blog/algolia/best-elasticsearch-alternatives-in-2025-for-your-use-case

Referenced in 1 query

Review
Do you need Elasticsearch when you have PostgreSQL?

https://news.ycombinator.com/item?id=32383902

Referenced in 1 query

Review
Top 10 Elasticsearch Alternatives in 2026: Complete Comparison Guide

https://openobserve.ai/blog/elasticsearch-alternatives/

Referenced in 1 query

Review
Content Engineering

Goals & Content Ideas

Ideas to help AI agents better understand the business and be more likely to use ParadeDB's resources to help users.

Launch LLM-Targeted Technical Documentation Campaign

ChatGPT and Claude currently show zero mention of ParadeDB, indicating our core value proposition hasn't been ingested into their knowledge bases. We'll create comprehensive technical documentation on high-authority domains like GitHub, StackOverflow, and specialized database forums where LLMs actively crawl for training data. Social media will amplify these resources by sharing code snippets, documentation links, and engaging with developer communities.

The complete guide to implementing full-text search in Postgres without external dependencies
Why we built ParadeDB as a Postgres extension instead of a standalone service
Common Postgres search patterns and how to implement them with native extensions
Benchmarking search performance: Postgres extensions vs external search services
How ParadeDB handles faceted search directly in your existing Postgres database

Dominate Hybrid Search Keywords in Postgres Context

ParadeDB is losing ground to pgvector in high-growth hybrid search queries, particularly in Gemini rankings. We'll create optimized technical content targeting 'hybrid search' and 'vector similarity' keywords specifically within the Postgres ecosystem. Social campaigns will feature comparison content, technical deep-dives, and real-world hybrid search implementations.

Hybrid search explained: combining full-text and vector similarity in a single Postgres query
When to use hybrid search vs pure vector search for your application
Building a semantic search feature without leaving Postgres
The developer's guide to vector similarity search in PostgreSQL
How hybrid search improves relevance for e-commerce product discovery

Create Elasticsearch Migration Content Series

Our strongest AI visibility performance correlates with Elasticsearch fatigue narratives, where we already rank well in AI Overviews. We'll develop structured comparison content and migration guides optimized for snippet extraction, reinforcing our position as the pragmatic Elasticsearch alternative. Social content will highlight migration success stories and pain point comparisons.

5 signs your team has outgrown Elasticsearch but not your search requirements
What we learned migrating 10 million documents from Elasticsearch to Postgres
The hidden operational costs of running Elasticsearch that nobody talks about
How to evaluate if ParadeDB can replace your Elasticsearch cluster
Elasticsearch vs ParadeDB: an honest comparison for different use cases

Build CTO-Focused Technical Proof Content

Startup CTOs represent our lowest persona engagement at 50%, creating a gap in overall market penetration. We'll develop ROI-focused technical benchmarks and persona-specific content that speaks directly to CTO priorities: infrastructure costs, team productivity, and technical debt. Social campaigns will target CTO communities with business-impact metrics alongside technical credibility.

The real cost of search infrastructure: what CTOs should calculate before choosing
How one startup cut their search infrastructure bill by 70% without sacrificing quality
Technical debt in search: why your Elasticsearch cluster is slowing down your team
What CTOs get wrong when evaluating search solutions for their stack
Build vs buy vs extend: a framework for search infrastructure decisions
Content Engineering

Recommended Actions

!

Initiate an LLM-targeted technical documentation campaign on high-authority domains like GitHub, StackOverflow, and specialized database forums.

ChatGPT and Claude's zero-percent mention rate indicates that ParadeDB's core value proposition has not yet been ingested into their foundational knowledge bases.

Impact: High
!

Optimize technical content specifically for 'hybrid search' and 'vector similarity' keywords within the Postgres context.

While winning on infrastructure simplification, ParadeDB is losing ground to pgvector in the high-growth hybrid search segment where Gemini rankings are currently weak.

Impact: High
~

Develop a dedicated 'Elasticsearch vs. ParadeDB' migration series optimized for structured data snippets.

The brand's strongest performance is linked to Elasticsearch fatigue; reinforcing this narrative will solidify the top-tier rankings already held in AI Overviews.

Impact: Medium
~

Create persona-specific landing pages and technical benchmarks targeting Startup CTOs.

CTOs currently represent the lowest persona engagement (50%); bridging this gap with ROI-focused technical proof will improve overall market penetration.

Impact: Medium

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Backing

Investors

C
Craft Ventures
Data generated by Pendium.ai AI visibility scanning. Last scanned February 28, 2026.

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