These are the best alternatives to ParadeDB for search and analytics:
- Tinybird
- PostgreSQL Native Full-Text Search
- pg_trgm
- pg_bm25 / pg_textsearch
- ZomboDB
- Elasticsearch
- OpenSearch
- Meilisearch
- Typesense
- Quickwit
When you need full-text search or low latency real-time analytics capabilities, ParadeDB has emerged as an interesting option—it promises Elasticsearch-quality search directly inside PostgreSQL through extensions like pg_search (formerly pg_bm25).
ParadeDB's core appeal is simplicity: keep everything in Postgres, avoid syncing data between your transactional database and a separate search engine, and maintain ACID guarantees across your entire stack.
But that unified approach comes with trade-offs. Running search and analytics workloads inside your PostgreSQL instance increases CPU, memory, and I/O pressure on your transactional database. Extension availability varies across managed Postgres providers. And when your real problem is massive-scale analytics—not just search—ParadeDB's architecture wasn't designed for billions of rows and sub-100ms aggregations.
As a result, teams evaluating ParadeDB alternatives typically fall into two camps: those who want to stay Postgres-first with different extension choices, and those ready to adopt purpose-built external systems that excel at specific workloads.
We evaluate each alternative based on ease of use, performance characteristics, operational complexity, and ideal use cases to help you choose the right tool for your specific needs.
Need real-time analytics without the operational complexity?
If your search for ParadeDB alternatives is really about analytics at scale—dashboards, aggregations, user-facing metrics—consider Tinybird. It's a real-time data platform built on ClickHouse® that handles everything from streaming ingestion to instant API publication. No cluster management, no extension compatibility worries, just SQL queries that become production-ready HTTP endpoints in seconds.
1. Tinybird: Real-Time Analytics Platform Built for Scale
Rather than starting with Postgres-based alternatives, let's address a fundamentally different approach that solves the underlying problem many teams face when evaluating ParadeDB.
Tinybird isn't a search engine or a Postgres extension—it's a complete real-time data platform built on ClickHouse® that handles ingestion, transformation, and API publication in one integrated service. If your actual need is analytics at scale rather than full-text search, Tinybird eliminates the architectural complexity that drives teams to consider ParadeDB in the first place.
Beyond the Postgres Extension Model
ParadeDB extends PostgreSQL to add search and analytics capabilities. This keeps your stack simple but creates inherent limitations: your transactional database now handles analytical workloads, competing for the same resources.
Tinybird takes a different approach. It's purpose-built for analytical queries on massive datasets, using columnar storage and vectorized execution that processes billions of rows in milliseconds. You don't add capabilities to an existing database—you connect a dedicated analytics layer designed from the ground up for this workload.
Instant APIs from SQL Queries
One of Tinybird's most powerful features is the instant API layer. Write a SQL query, publish it as a secure HTTP endpoint with one click. No backend service to build, no API framework to maintain, no infrastructure to scale.
For teams building user-facing analytics—embedded dashboards, real-time metrics in applications, customer-facing reports—this capability alone can save months of development time.
Streaming-First Architecture
While ParadeDB relies on Postgres's transactional model, Tinybird is streaming-first by design. Data flows continuously from sources like Kafka, webhooks, S3, or direct HTTP ingestion—true streaming data that becomes immediately queryable.
No batch windows. No ETL delays. No lambda architectures with separate real-time and historical pipelines. Using real-time change data capture, one unified system handles everything with sub-100ms query latency.
Fully Managed Infrastructure
The entire operational burden disappears with Tinybird. Automatic scaling handles traffic spikes without capacity planning. High availability and failover are built in. SOC 2 Type II compliance comes standard.
Your engineers write SQL queries and build data products. Tinybird's team handles the distributed systems complexity so you don't need that expertise in-house.
When Tinybird Makes Sense
Tinybird is ideal when:
- Your core need is analytics and aggregations, not full-text search
- You're building user-facing dashboards or embedded analytics
- You need sub-100ms query latency on billions of rows
- You want instant APIs without building backend services
- Operational simplicity matters more than staying purely within Postgres
Tinybird is also well-suited for telemetry-heavy Internet of Things (IoT) environments, where devices produce continuous event streams that require fast ingestion and real-time analytical visibility.
2. PostgreSQL Native Full-Text Search: The Built-In Baseline
PostgreSQL includes native full-text search capabilities through tsvector and tsquery types, along with GIN indexes for efficient querying. This is the zero-dependency baseline that every Postgres user has available.
How It Works
PostgreSQL's FTS uses tsvector to store preprocessed document representations and tsquery for search expressions. You create GIN indexes on tsvector columns to enable fast lookups across millions of rows.
The built-in functions handle tokenization, stemming, stop words, and ranking. For many use cases, this provides sufficient search quality without any extensions.
Strengths
Native FTS requires no additional components. It's available on every Postgres installation, every managed provider, every hosting environment. There's nothing to install, no compatibility concerns, no extension approval processes.
Operational simplicity is the main advantage. Your search lives inside your existing database with familiar tooling, backups, and monitoring.
Limitations Compared to ParadeDB
Where native FTS falls short:
- Ranking sophistication: BM25 in ParadeDB generally produces better relevance than native ranking functions
- Feature depth: Fuzzy matching, faceted search, and highlighting require significant custom code
- Query flexibility: ParadeDB's DSL offers more expressive search capabilities
Best-Fit Scenarios
PostgreSQL native FTS works well for:
- Small to medium catalogs where relevance tuning isn't critical
- Teams prioritizing simplicity over search sophistication
- Applications where basic keyword matching is sufficient
- Environments with strict extension restrictions
3. pg_trgm: Trigram Similarity for Fuzzy Search
The pg_trgm extension adds trigram-based similarity matching to PostgreSQL, enabling fuzzy search, typo tolerance, and efficient LIKE/ILIKE queries through GiST or GIN indexes.
How Trigrams Work
Trigrams break text into three-character sequences. "hello" becomes {"hel", "ell", "llo"}. By comparing trigram overlap between query and indexed content, pg_trgm finds similar strings even with typos or variations.
This approach excels at "did you mean?" suggestions and search-as-you-type interfaces where exact matching fails.
Complementing Other Approaches
Many teams use pg_trgm alongside native FTS or ParadeDB: trigrams handle the fuzzy, user-friendly search box while structured search handles precise queries.
This combination provides typo tolerance without the operational complexity of deploying a dedicated search engine.
Trade-offs
pg_trgm isn't a complete search solution. It handles similarity matching, not relevance ranking. For catalog search with facets, filtering, and sophisticated scoring, you'll need additional components.
Index size can grow significantly with trigram indexes, especially on large text fields. Monitor storage and query performance as data scales.
When pg_trgm Fits
Consider pg_trgm when:
- Autocomplete and typo tolerance are primary requirements
- You're searching short fields like names, cities, or product codes
- You want fuzzy matching without leaving Postgres
- Search volume is moderate enough for trigram index overhead
4. pg_bm25 and pg_textsearch: Modern BM25 in Postgres
If your main attraction to ParadeDB is BM25 ranking, several extensions provide similar capabilities. pg_bm25 (the core of ParadeDB's search) and newer projects like pg_textsearch bring modern relevance algorithms directly into PostgreSQL.
The BM25 Advantage
BM25 is the industry-standard ranking algorithm used by Elasticsearch, Solr, and most modern search engines. It consistently produces better relevance than PostgreSQL's native ranking for most search use cases.
These extensions implement BM25 as native Postgres index types, letting you create indexes with CREATE INDEX ... USING bm25 syntax and query with familiar SQL.
Tantivy Under the Hood
Both pg_bm25 and pg_search use Tantivy, a high-performance search library written in Rust. This provides Lucene-like indexing and query execution without the JVM overhead.
The result is search performance that can match dedicated search engines for many workloads, all accessible through SQL.
Extension Availability Challenges
The practical barrier for these extensions is often availability on managed Postgres services. Not every provider supports custom extensions, and approval processes can take months.
Before committing to this approach, verify that your Postgres provider supports the specific extension version you need. Self-hosted Postgres has no such restrictions.
Hybrid Search Possibilities
These BM25 extensions integrate well with pgvector for hybrid search: combine keyword precision with semantic similarity using techniques like Reciprocal Rank Fusion (RRF).
This is particularly valuable for RAG applications where you need both exact matches and conceptual relevance.
5. ZomboDB: Postgres SQL with Elasticsearch Power
ZomboDB takes a hybrid approach: keep your SQL interface in Postgres while delegating actual search to Elasticsearch. It creates a bridge where Elasticsearch indexes appear as native Postgres index types.
How It Works
You create ZomboDB indexes on Postgres tables, and ZomboDB automatically syncs data to an Elasticsearch cluster. Queries written in SQL get translated to Elasticsearch DSL, executed remotely, and results returned to Postgres.
This gives you Elasticsearch's mature search capabilities with the familiar SQL interface your team already knows.
The Best of Both Worlds?
For teams that need enterprise-grade search features—complex faceting, sophisticated relevance tuning, proven scalability—but want to maintain Postgres as the primary interface, ZomboDB offers an interesting middle ground.
You don't rewrite applications to use Elasticsearch APIs directly. Existing SQL queries gain search superpowers.
Operational Reality
The downside is clear: you're now operating two systems. Elasticsearch requires its own cluster management, monitoring, scaling, and maintenance. You need expertise in both Postgres and Elasticsearch.
Sync reliability becomes critical. If the bridge fails or lags, your search results diverge from your transactional data. This is exactly the consistency problem ParadeDB tries to solve.
When ZomboDB Makes Sense
Consider ZomboDB when:
- You already run Elasticsearch for other use cases
- Enterprise search features are non-negotiable
- Your team has strong Elasticsearch operational experience
- The consistency trade-off is acceptable for your use case
6. Elasticsearch: The Industry Standard
Elasticsearch is the dominant search platform globally, powering everything from e-commerce product search to enterprise knowledge bases to observability stacks.
Comprehensive Feature Set
Elasticsearch offers the most complete search feature set available:
- BM25 and vector search for hybrid retrieval
- Sophisticated faceting with aggregations
- Near real-time indexing for fresh results
- Horizontal scaling across hundreds of nodes
- Mature ecosystem with extensive tooling
For complex search requirements, Elasticsearch provides capabilities that Postgres-based solutions simply can't match.
The Operational Cost
Running Elasticsearch requires dedicated expertise. Cluster sizing, shard management, index lifecycle policies, JVM tuning, and capacity planning all require attention.
This operational burden is precisely what drives teams toward ParadeDB. If you're evaluating alternatives because Elasticsearch feels too heavy, other options may serve you better.
Licensing Considerations
Since version 7.11, Elasticsearch uses the SSPL/Elastic License instead of Apache 2.0. This affects how you can deploy and distribute Elasticsearch, particularly for SaaS applications.
Evaluate licensing implications carefully before committing to Elasticsearch for new projects.
Best-Fit Scenarios
Elasticsearch remains the right choice when:
- Search is a core product feature requiring maximum sophistication
- You have dedicated platform engineering resources
- Scale requirements exceed what Postgres can handle
- You need the extensive Elasticsearch ecosystem and integrations
7. OpenSearch: The Community Fork
OpenSearch is AWS's community-driven fork of Elasticsearch, created after Elastic changed licensing. It maintains API compatibility while using the permissive Apache 2.0 license.
Full Elasticsearch Compatibility
Applications built for Elasticsearch generally work with OpenSearch. The query DSL, aggregation framework, and most features remain compatible.
For teams concerned about licensing or preferring community governance, OpenSearch provides a clear migration path from Elasticsearch.
AWS Integration
OpenSearch integrates deeply with AWS services through Amazon OpenSearch Service. If your infrastructure is AWS-centric, this managed offering simplifies operations significantly.
Log analytics, observability pipelines, and security analytics often use OpenSearch as the foundation.
Diverging Roadmaps
As the projects evolve independently, feature parity will decrease over time. Evaluate whether specific Elasticsearch features you depend on have OpenSearch equivalents.
The community is active and well-funded, but development velocity differs from Elastic's commercial focus.
When to Choose OpenSearch
OpenSearch fits well when:
- Apache 2.0 licensing is required
- AWS is your primary cloud platform
- Observability and log analytics are key use cases
- You want managed service options without Elastic's commercial terms
8. Meilisearch: Developer-Friendly Search
Meilisearch represents a different philosophy: prioritize developer experience and instant setup over maximum configurability. It's designed for application search where getting started quickly matters more than tuning every parameter.
Instant Setup, Immediate Results
Deploy Meilisearch and have working search within minutes. Typo tolerance, relevance ranking, and faceted search work out of the box without configuration.
For teams without search expertise, this dramatically reduces time-to-value compared to Elasticsearch or self-tuned Postgres solutions.
Search-as-You-Type Focus
Meilisearch excels at the search box experience: fast autocomplete, tolerance for user mistakes, and results that feel instantly responsive.
E-commerce product search, documentation search, and content catalogs are sweet spots.
Simpler at the Cost of Flexibility
The trade-off for simplicity is reduced configurability. Meilisearch makes opinionated choices that work well for common cases but may not fit specialized requirements.
If you need deep control over ranking algorithms or complex query DSLs, Elasticsearch or Solr offer more flexibility.
Ideal Use Cases
Meilisearch shines when:
- Developer experience and fast integration matter most
- Search-as-you-type UX is the primary goal
- Your team lacks dedicated search engineering resources
- Standard relevance ranking meets your needs
9. Typesense: Fast, Typo-Tolerant Search
Typesense shares Meilisearch's philosophy of prioritizing simplicity and speed over exhaustive features. It's optimized for low-latency search with minimal operational overhead.
Performance-First Design
Typesense is built in C++ for maximum performance. Sub-50ms response times are typical even on modest hardware, making it excellent for real-time search experiences.
The focus on speed doesn't sacrifice search quality—typo tolerance and relevance ranking remain strong.
Simple Deployment Model
A single Typesense binary handles most deployments. Clustering is available for high availability, but many teams run single-node setups successfully at significant scale.
Compare this to Elasticsearch's JVM requirements and cluster complexity. For smaller teams, the operational difference is substantial.
Growing Ecosystem
Typesense's integration ecosystem continues expanding. Official clients exist for major languages, and community adapters cover popular frameworks.
The project is commercially backed, providing confidence in long-term maintenance.
When Typesense Fits
Consider Typesense when:
- Low latency is critical for user experience
- You want minimal operational complexity
- Standard search features (autocomplete, facets, typo tolerance) are sufficient
- Resource efficiency matters for your infrastructure budget
10. Quickwit: Search Over Object Storage
Quickwit approaches search differently: it's designed for log analytics and observability workloads where data volumes are massive and storage cost optimization is essential.
Object Storage Foundation
Unlike traditional search engines that require fast local storage, Quickwit indexes data directly on S3, GCS, or Azure Blob Storage. This dramatically reduces infrastructure costs for high-volume logging use cases.
For retention-heavy workloads, the cost difference can be 5-10x compared to Elasticsearch.
Log Analytics Focus
Quickwit isn't trying to replace e-commerce search or general-purpose text retrieval. It's specifically optimized for structured log data: timestamps, log levels, trace IDs, and semi-structured JSON.
If your "search" problem is really an observability problem, Quickwit may be the better specialized tool.
Trade-offs for Cost Efficiency
Query latency is typically higher than Elasticsearch—seconds rather than milliseconds for complex queries. This trade-off makes sense for log analytics where cost matters more than sub-second response times.
For user-facing search where speed is critical, other options serve better.
Best-Fit Scenarios
Quickwit excels when:
- Log analytics and observability are the primary use case
- Storage costs dominate your infrastructure budget
- Query latency in seconds is acceptable
- You're processing massive volumes with long retention requirements
Why Tinybird Is the Best ParadeDB Alternative
After evaluating all the alternatives, Tinybird emerges as the strongest choice for teams whose real need is analytics at scale rather than pure full-text search.
The Right Architecture for Analytics Workloads
Many teams discover ParadeDB while searching for ways to add analytics capabilities to their PostgreSQL stack. They want dashboards, aggregations, real-time metrics—workloads that ParadeDB's columnar features attempt to address.
But running analytics inside your transactional database creates fundamental architectural tension. Your OLTP workload competes with OLAP queries for the same resources. Index maintenance affects transaction latency. Scaling means scaling everything together.
Tinybird solves this by providing a purpose-built analytical layer. Your PostgreSQL handles transactions. Tinybird handles analytics. Each system does what it was designed for.
Performance That Scales
Tinybird is built on ClickHouse®, the fastest open-source columnar database for analytical queries. While ParadeDB adds columnar capabilities to Postgres, ClickHouse® was designed from the ground up for this workload.
The difference shows at scale:
- Sub-100ms queries on billions of rows
- Linear scaling as data grows
- Consistent performance regardless of concurrent users
- Efficient compression that reduces storage 10-100x
From Query to Production API in Seconds
No other alternative offers Tinybird's instant API publication. Write a SQL query, click publish, get a production-ready HTTP endpoint.
For teams building user-facing analytics—embedded dashboards, real-time metrics in applications, customer-facing reports—this capability alone can save months of development time.
Zero Operational Burden
With ParadeDB, you still operate PostgreSQL—just with more extensions adding complexity. With Elasticsearch, you manage clusters. With self-hosted alternatives, you handle everything.
Tinybird eliminates operational burden entirely. Automatic scaling, built-in high availability, SOC 2 compliance, and expert support come standard. Your team builds products while Tinybird's team handles infrastructure.
Developer Experience That Accelerates Teams
Tinybird integrates with modern development workflows:
- Git-based version control for all configurations
- CLI tools for local development and CI/CD
- Instant APIs without backend code
- AI-assisted workflows through the agentic experience
This developer-first approach means faster iteration, better collaboration, and reduced time-to-production.
Predictable Economics
Unlike consumption-based pricing that can spike unpredictably, Tinybird offers fixed monthly plans that include compute and storage allocations.
You know your costs upfront. No surprise bills when traffic spikes. No need to implement query governors or usage limits to control spending.
For developers evaluating event analytics platforms, Tinybird can even serve as a Google Analytics alternative, providing complete ownership of your metrics stack while keeping real-time performance.
Conclusion
Choosing a ParadeDB alternative depends on understanding what problem you're actually solving.
For full-text search within Postgres, native FTS provides the simplest baseline. Extensions like pg_bm25 and pg_textsearch add BM25 sophistication. pg_trgm handles fuzzy matching. ZomboDB bridges to Elasticsearch when you need its full power.
For dedicated search infrastructure, Elasticsearch remains the industry standard despite operational complexity. OpenSearch offers an Apache-licensed alternative. Meilisearch and Typesense provide simpler, developer-friendly options for straightforward use cases.
For analytics at scale—the hidden driver behind many ParadeDB evaluations—Tinybird offers the most compelling solution. Purpose-built columnar architecture, instant API publication, and fully managed infrastructure let teams focus on building products rather than managing databases.
The right choice depends on your specific workload, team capabilities, and architectural priorities. But if your real need is real-time analytics rather than search, starting with a platform designed for that workload will serve you far better than extending PostgreSQL beyond its design parameters.
Frequently Asked Questions (FAQs)
What is ParadeDB and why do teams look for alternatives?
ParadeDB is a PostgreSQL extension that adds Elasticsearch-quality search (BM25 ranking, faceting, full-text capabilities) directly inside Postgres. Teams look for alternatives when they need better performance at scale, broader extension availability, or purpose-built solutions for specific workloads like analytics.
Is Tinybird a search engine like ParadeDB?
No. Tinybird is a real-time analytics platform built on ClickHouse®, not a search engine. If your need is full-text search with facets and ranking, ParadeDB or Elasticsearch-style solutions are more appropriate. If your need is analytics, aggregations, and dashboards at scale, Tinybird is the better architectural choice.
Can I use PostgreSQL native FTS instead of ParadeDB?
Yes. PostgreSQL's built-in tsvector/tsquery with GIN indexes provides basic full-text search without any extensions. It's less sophisticated than ParadeDB's BM25 ranking but works everywhere and adds no operational complexity.
What's the main advantage of staying within Postgres for search?
ACID consistency. When search indexes live inside Postgres, they update transactionally with your data. External search engines require sync mechanisms that can lag or fail, creating consistency gaps between your database and search results.
When should I use Elasticsearch instead of Postgres-based solutions?
When search is a core product feature requiring maximum sophistication: complex faceting, extensive relevance tuning, massive scale, or the rich Elasticsearch ecosystem. The operational cost is higher, but capabilities are unmatched.
How does Tinybird compare to running ClickHouse® myself?
Tinybird provides managed ClickHouse® with additional layers: streaming ingestion, instant API publication, and developer tooling. Self-hosted ClickHouse® gives maximum control but requires significant operational investment. Tinybird trades some flexibility for dramatically reduced complexity.
Is hybrid search (BM25 + vectors) possible with ParadeDB alternatives?
Yes. Both ParadeDB and alternatives like pg_textsearch support hybrid search by combining BM25 indexes with pgvector for semantic similarity. Elasticsearch and OpenSearch also offer hybrid retrieval. Tinybird focuses on analytics rather than search, so vector similarity isn't its primary use case.