These are the best ParadeDB alternatives:
- Tinybird
- Elasticsearch
- ClickHouse
- Apache Druid
- TimescaleDB
- SingleStore
- PostgreSQL with Extensions
- QuestDB
The 8 Best ParadeDB Alternatives
1. Tinybird
Tinybird represents a fundamentally different approach than extending PostgreSQL: instead of trying to make a transactional database handle analytics, Tinybird provides a purpose-built real-time analytics platform with managed ClickHouse® infrastructure, sub-100ms queries on billions of rows, and instant APIs.
If your goal is analytics, not running PostgreSQL, Tinybird delivers dramatically better performance without database management complexity.
Key Features:
- Sub-100ms query latency on billions of rows
- Managed ClickHouse® infrastructure with automatic scaling
- Instant SQL-to-API transformation with authentication
- Real-time continuous data ingestion
- SQL-based analytics accessible to analysts
- Incremental materialized views for efficient computation
- Columnar storage optimized for analytics
- Zero database infrastructure management
- Built-in monitoring and observability
Pros
Purpose-Built for Analytics vs. Extended PostgreSQL:
- Columnar storage architecture designed for analytical queries from ground up
- Sub-100ms queries on billions of rows without compromises
- No PostgreSQL row-storage limitations holding back performance
- Vectorized query execution optimized for analytics
- Architecture choices optimized for one thing: fast analytics
Real-Time Analytics Without PostgreSQL Constraints:
- Continuous data ingestion replaces batch loading to PostgreSQL
- Data immediately queryable after ingestion
- Incremental materialized views update automatically
- No batch processing delays or refresh windows
- True real-time, not "near real-time" batch updates
Managed Infrastructure vs. Self-Operated PostgreSQL:
- Fully managed service eliminates database operations entirely
- No PostgreSQL provisioning, scaling, or maintenance
- Automatic scaling handles data growth and query load
- Built-in high availability without replication configuration
- Zero operational overhead compared to managing PostgreSQL
Instant APIs for Applications:
- Every SQL query automatically becomes authenticated API
- Serve analytics to applications without custom backend development
- No building API layer on top of PostgreSQL
- Built-in rate limiting and authentication
- Power dashboards, mobile apps, and integrations directly
Performance at Scale:
- Consistent sub-100ms queries as data grows to billions of rows
- No performance degradation with concurrent users
- Scales horizontally without manual sharding
- Query optimization automatic without tuning
- PostgreSQL can't match analytical query performance
Developer-First Experience:
- SQL-based development familiar to data teams
- Local development environment with CLI
- Version control with Git for collaboration
- CI/CD integration for automated deployment
- Modern workflows vs. traditional database management
Cost-Effective Economics:
- Usage-based pricing scales with actual value
- No idle database infrastructure consuming budget
- Eliminates need for database operations team
- Better price-performance than managed PostgreSQL
- Lower total cost of ownership for analytics
Complete Analytics Platform:
- Ingestion, storage, transformation, query, and API layers integrated
- No assembling separate systems around PostgreSQL
- Single platform for all real-time analytics needs
- Built-in monitoring across entire stack
SQL Without PostgreSQL Limitations:
- Write standard SQL without PostgreSQL-specific syntax
- No understanding PostgreSQL internals required
- Optimized query execution for analytics
- Window functions, aggregations, and joins performant
Specialized for Analytics, Not Hybrid:
- Purpose-built for analytical workloads exclusively
- No compromises for supporting transactions
- No search-analytics-transaction trade-offs
- Excels at one thing rather than adequate at three
Best for: Organizations building real-time analytics dashboards, operational monitoring, API-backed analytics features, customer-facing analytics, usage-based billing, or any scenario where analytical performance and real-time capabilities matter more than running PostgreSQL.
When to Consider Tinybird Instead of ParadeDB:
- Need sub-second query performance on large datasets
- Building real-time analytics, not extending transactional database
- Want managed service vs. operating PostgreSQL
- Require APIs for serving analytics programmatically
- Team focused on analytics, not database administration
- Performance and developer velocity priorities
- Don't actually need PostgreSQL compatibility
- True real-time required, not batch updates
2. Elasticsearch
Elasticsearch is the leading search and analytics engine designed for full-text search, log analytics, and observability use cases with distributed architecture.
Key Features:
- Full-text search with relevance scoring
- Distributed architecture for scalability
- Real-time indexing and search
- Aggregations for analytics
- Kibana for visualization
- Extensive ecosystem and integrations
Pros
Purpose-Built for Search:
- Optimized for full-text search workloads
- Advanced search capabilities (fuzzy, phrase, proximity)
- Relevance scoring and ranking
- Better search than PostgreSQL extensions
Proven at Scale:
- Battle-tested in massive deployments
- Handles petabytes of data
- Distributed architecture scales horizontally
- Mature platform with extensive documentation
Rich Ecosystem:
- Elastic Stack (Elasticsearch, Kibana, Beats, Logstash)
- Extensive integrations and plugins
- Large community support
- Well-understood patterns
Cons
Operational Complexity:
- Requires managing Elasticsearch clusters
- Complex configuration and tuning
- Understanding distributed systems essential
- Resource-intensive infrastructure
- Significant operational overhead
Expensive at Scale:
- Heavy resource requirements (memory, CPU)
- Licensing costs for advanced features
- Infrastructure costs add up quickly
- Not cost-effective for simple analytics
Not Optimized for Analytics:
- Designed for search, not analytical queries
- Aggregations slower than analytical databases
- Not suitable for complex analytical workloads
- Better alternatives for pure analytics
Query Performance:
- Analytical queries slower than ClickHouse® or Tinybird
- Not sub-second for complex aggregations
- Optimized for search, not analytics speed
- Performance trade-offs for hybrid workloads
When to Consider Tinybird Instead: If you're considering Elasticsearch primarily for analytics (not search), Tinybird provides dramatically better analytical query performance. Elasticsearch excels at search but Tinybird delivers sub-100ms analytical queries on billions of rows. For metrics, dashboards, and APIs, Tinybird is purpose-built; Elasticsearch is adapted from search.
A deeper comparison between analytics and search-first engines appears in Tinybird’s post on ClickHouse vs Elasticsearch for analytical workloads.
3. ClickHouse
ClickHouse is the open-source columnar analytics database that Tinybird uses under the hood, offering exceptional analytical query performance for self-hosted deployments.
Key Features:
- Columnar storage for analytics
- Vectorized query execution
- SQL interface
- Horizontal scalability
- Real-time data ingestion
- Open source (Apache 2.0)
Pros
Exceptional Analytics Performance:
- Sub-second queries on billions of rows
- Columnar architecture optimized for analytics
- Vectorized execution for speed
- Best-in-class analytical database performance
Open Source:
- Free to use and deploy
- No licensing costs
- Community development
- Transparency in implementation
Flexible Deployment:
- Self-hosted with complete control
- Deploy anywhere (cloud, on-premises)
- No vendor lock-in
- Customization possible
Cons
Operational Complexity:
- Requires managing ClickHouse® clusters
- Understanding distributed systems necessary
- Complex configuration and tuning
- Significant expertise required
- No managed operations in open source
No Built-In APIs:
- Provides database, not API layer
- Must build custom APIs for applications
- No instant API generation
- Additional development required
Deployment Challenges:
- Production deployment non-trivial
- High availability requires expertise
- Replication and backup configuration complex
- Monitoring and alerting your responsibility
Learning Curve:
- ClickHouse®-specific syntax and functions
- Understanding table engines and merges
- Performance optimization requires expertise
- Different from traditional databases
When to Consider Tinybird Instead: Tinybird is managed ClickHouse® with instant APIs and zero operational overhead. If you want ClickHouse® performance without managing clusters, learning ClickHouse® internals, or building API layers, Tinybird provides that. Same performance engine, dramatically simpler operations.
4. Apache Druid
Apache Druid is an open-source analytics database designed for real-time exploratory analytics on event data with high concurrency.
Key Features:
- Columnar storage for analytics
- Real-time and batch ingestion
- Sub-second queries on event data
- Time-based partitioning
- Approximate algorithms for speed
- Horizontal scalability
Pros
Real-Time Ingestion:
- Streaming data ingestion
- Immediate queryability
- Good for event analytics
- Time-series optimized
High Concurrency:
- Handles many concurrent queries
- Good for user-facing analytics
- Performance maintained under load
Open Source:
- Apache-licensed
- Community development
- No licensing costs
- Active project
Cons
Operational Complexity:
- Requires managing multiple services (historical, broker, coordinator)
- Complex architecture with many components
- Significant operational overhead
- Expertise required for production deployment
Limited SQL Support:
- SQL support improving but still limited
- Native JSON-based queries complex
- Not full SQL compatibility
- Learning curve for queries
Data Modeling Constraints:
- Must define schema and rollup at ingestion
- Changing schema requires re-ingestion
- Less flexible than alternatives
- Pre-aggregation requirements limiting
Resource Intensive:
- Heavy memory requirements
- Complex capacity planning
- Expensive to run at scale
- Optimization challenging
When to Consider Tinybird Instead: Druid's operational complexity (brokers, coordinators, historical nodes) makes it challenging to manage. Tinybird provides similar real-time analytics performance with dramatically simpler operations, managed infrastructure, full SQL support, flexible schemas. Better developer experience without Druid's operational burden.
5. TimescaleDB
TimescaleDB extends PostgreSQL specifically for time-series data with automatic partitioning and time-oriented features.
Key Features:
- PostgreSQL extension for time-series
- Automatic time-based partitioning (hypertables)
- Continuous aggregates
- Data retention policies
- Full SQL support
- PostgreSQL ecosystem compatibility
Pros
PostgreSQL Compatibility:
- Works with existing PostgreSQL tools
- Familiar PostgreSQL interface
- Full SQL support
- Easy adoption for PostgreSQL users
Time-Series Optimized:
- Automatic time-based partitioning
- Good for IoT and monitoring
- Time-oriented functions
- Retention policies
Better Than Plain PostgreSQL:
- Significant improvements for time-series
- Performance better than unextended PostgreSQL
- Useful abstractions for time data
Cons
Still PostgreSQL Underneath:
- Row-oriented storage limitations remain
- Cannot match columnar analytics databases
- PostgreSQL architecture constraints
- Not designed for analytical workloads at scale
Performance Limitations:
- Better than PostgreSQL but slower than ClickHouse®
- Not sub-second on billions of rows
- Analytical queries still slow at scale
- Compromises from PostgreSQL base
Operational Overhead:
- Still requires managing PostgreSQL infrastructure
- Tuning and optimization necessary
- Not managed by default
- Database administration burden
Limited to Time-Series:
- Optimized specifically for time-series patterns
- Less suitable for general analytics
- Specialized use case focus
- Not versatile analytics platform
When to Consider Tinybird Instead: TimescaleDB improves PostgreSQL for time-series but can't match purpose-built analytics databases. Tinybird provides sub-100ms queries on time-series data at massive scale with managed infrastructure. No PostgreSQL tuning, no hypertable configuration, just fast queries and instant APIs.
6. SingleStore
SingleStore (formerly MemSQL) is a distributed SQL database supporting both transactional and analytical workloads with in-memory processing.
Key Features:
- Hybrid transactional and analytical (HTAP)
- In-memory processing for speed
- Distributed architecture
- Full SQL support
- Real-time analytics capabilities
- MySQL wire protocol compatibility
Pros
Hybrid Workloads:
- Handles both transactions and analytics
- No separate systems needed
- Unified platform
- Flexibility in use cases
Good Performance:
- In-memory processing provides speed
- Better than traditional databases
- Real-time analytics capable
- Distributed for scale
SQL Compatibility:
- Full SQL support
- MySQL wire protocol
- Familiar interfaces
- Easy integration
Cons
Expensive:
- Enterprise pricing model
- Costly at scale
- Better suited for large organizations with budget
- Not cost-effective for smaller deployments
Operational Complexity:
- Distributed system management required
- Cluster configuration and tuning
- Not simple to operate
- Expertise necessary
Compromises from Hybrid:
- Jack of all trades approach
- Not best-in-class for pure analytics
- Trade-offs supporting multiple workloads
- Specialized databases better for specific use cases
Limited Managed Options:
- Cloud offering but with limitations
- Not fully hands-off
- Still requires understanding architecture
- Operational burden remains
When to Consider Tinybird Instead: SingleStore's hybrid approach means compromises for analytics performance. Tinybird focuses exclusively on analytics with better query performance, simpler operations, and better economics. If you need analytics (not transactions), purpose-built platform outperforms hybrid.
7. PostgreSQL with Extensions
Plain PostgreSQL with extensions like pg_analytics, columnar storage extensions, or full-text search represents the DIY approach ParadeDB builds upon.
Key Features:
- PostgreSQL base with extensions
- Columnar storage extensions available
- Full-text search with pg_trgm, pg_search
- Flexibility to add capabilities
- Full control over configuration
Pros
PostgreSQL Ecosystem:
- Mature, stable database
- Extensive tooling and support
- Large community
- Well-understood patterns
Flexibility:
- Choose extensions based on needs
- Customize configuration
- Complete control
- No vendor lock-in
Cost:
- Open source and free
- No licensing fees
- Deploy anywhere
Cons
Limited Analytics Performance:
- Row-oriented storage fundamentally limits analytics
- Extensions help but can't overcome architecture
- Not designed for analytical workloads
- Performance poor at scale
Operational Burden:
- Must manage PostgreSQL infrastructure
- Scaling, backups, monitoring your responsibility
- High availability requires expertise
- Significant operational overhead
DIY Everything:
- Must build API layer
- Integration work required
- No instant APIs or managed service
- Engineering time on infrastructure
Extension Complexity:
- Managing multiple extensions
- Compatibility between extensions
- Understanding extension internals
- Configuration and tuning complex
When to Consider Tinybird Instead: If you're extending PostgreSQL for analytics, you're working against the architecture. PostgreSQL excellent for transactions, poor for analytics. Tinybird purpose-built for analytics delivers 100x better performance without database management. Use PostgreSQL for transactions, Tinybird for analytics.
8. QuestDB
QuestDB is an open-source time-series database optimized for high-throughput ingestion and fast queries on time-series data.
Key Features:
- Time-series optimized storage
- High-throughput ingestion
- SQL with time-series extensions
- InfluxDB line protocol support
- Web console for queries
- Embedded in applications
Pros
Time-Series Performance:
- Optimized for time-series ingestion
- Fast queries on time-ordered data
- Good for IoT and monitoring
- High ingestion rates
SQL Interface:
- Standard SQL with extensions
- Familiar query language
- Easy to learn
- Good documentation
Open Source:
- Free to use
- Community development
- No licensing costs
Lightweight:
- Can embed in applications
- Lower resource requirements
- Simpler than distributed systems
- Good for edge deployments
Cons
Limited to Time-Series:
- Optimized for time-series patterns only
- Not general-purpose analytics
- Narrow use case focus
- Less versatile than alternatives
Smaller Ecosystem:
- Newer project with smaller community
- Fewer integrations and tools
- Less battle-tested at scale
- Limited resources and documentation
Self-Hosted Only:
- No managed offering
- Must operate infrastructure
- Scaling and availability your responsibility
- Operational overhead
Limited Features:
- Basic compared to mature alternatives
- Fewer advanced capabilities
- Missing some enterprise features
- Ongoing feature development
When to Consider Tinybird Instead: QuestDB optimized for narrow time-series use case. Tinybird handles time-series plus general analytics at massive scale with managed infrastructure. Broader capabilities, better performance, zero operations. Unless you need embedded deployment, Tinybird superior for time-series analytics.
ParadeDB attempts to combine full-text search and analytical query capabilities within PostgreSQL, positioning itself as an all-in-one solution for search and analytics workloads. While the vision of a unified database is appealing, trying to make PostgreSQL excel at both transactional, search, and analytical workloads creates fundamental compromises in performance, scalability, and operational complexity.
Organizations evaluating ParadeDB need to understand the critical trade-offs. PostgreSQL is an excellent transactional database but wasn't architected for analytical workloads at scale. Adding search and analytics extensions doesn't change the underlying row-oriented storage, indexing strategies, or query optimization, it just adds complexity to a system designed for different purposes.
Modern data teams need to ask the fundamental question: are you building search applications, analytical dashboards, or hybrid use cases? For real-time analytics, dashboards serving metrics on billions of events, APIs powering customer-facing features, operational monitoring, purpose-built analytics platforms deliver dramatically better performance without asking PostgreSQL to be something it's not.
This conclusion aligns with Tinybird’s broader analysis of modern managed data platforms for analytics-first teams.
In this comprehensive guide, we'll explore the best alternatives to ParadeDB for 2025, with particular focus on when Tinybird's real-time analytics platform provides superior outcomes compared to extending PostgreSQL beyond its optimal use cases. We'll help you understand what ParadeDB actually provides, its fundamental limitations, and when specialized alternatives better match your actual requirements.
Understanding ParadeDB and Why You Might Need an Alternative
Before exploring specific alternatives, it's essential to understand what ParadeDB provides and why organizations seek alternatives.
What Is ParadeDB:
ParadeDB is a PostgreSQL extension adding:
- Full-text search capabilities similar to Elasticsearch
- Columnar storage for analytical queries
- Hybrid workload support in single database
- PostgreSQL compatibility and ecosystem
- BM25 search ranking
- Analytical query acceleration
ParadeDB attempts to make PostgreSQL viable for search and analytics workloads traditionally requiring specialized databases.
6 Common Reasons for Seeking ParadeDB Alternatives
Organizations look beyond ParadeDB for several compelling reasons:
PostgreSQL Architecture Limitations: PostgreSQL was designed for transactional workloads with row-oriented storage. Adding columnar extensions doesn't change fundamental architecture limitations for analytical queries at scale. Purpose-built analytical databases outperform extended PostgreSQL by orders of magnitude.
Performance at Scale: ParadeDB improves PostgreSQL analytics but can't match specialized analytical databases. Sub-100ms queries on billions of rows require architecture designed for analytics from the ground up, not extensions to transactional databases.
Operational Complexity: ParadeDB still requires managing PostgreSQL infrastructure, provisioning, scaling, backups, replication, monitoring. Managed analytics platforms eliminate this operational burden entirely.
Not True Real-Time: PostgreSQL batch updates data, even with ParadeDB extensions. True real-time analytics requires streaming ingestion and immediate queryability, not batch loading into PostgreSQL.
Jack of All Trades, Master of None: Databases optimized for multiple conflicting workloads (transactions, search, analytics) make compromises. Specialized databases excel at their specific use case without compromise.
No API Layer: ParadeDB provides database with better analytics, not APIs for serving results. Building analytics applications still requires custom API development on top of database.
The PostgreSQL Extension vs. Purpose-Built Analytics Question
The most critical decision is understanding what problem you're actually solving:
You Might Use ParadeDB or PostgreSQL Extensions When:
- Already committed to PostgreSQL infrastructure
- Need PostgreSQL compatibility for applications
- Handling hybrid transactional and light analytical workloads
- Team expertise entirely PostgreSQL-focused
- Data volumes small enough for PostgreSQL to handle
- Simple analytics sufficient
You Need Tinybird (Purpose-Built Analytics) When:
- Analytics performance critical (sub-second queries)
- Building real-time dashboards or customer-facing analytics
- Handling billions of rows requiring analytical database
- Want managed infrastructure, not database operations
- Need APIs for serving analytics programmatically
- Developer velocity and time-to-market priorities
- True real-time required, not batch updates
The Critical Insight: Extending PostgreSQL for analytics is working against the architecture. PostgreSQL designed for transactions with row-oriented storage, adding columnar extensions doesn't change fundamental limitations. Purpose-built analytics databases outperform by orders of magnitude because architecture optimized for analytics from ground up.
Making the Right Choice
Understanding your actual requirements guides the decision:
Ask These Questions:
What's the primary workload?
- Transactions → PostgreSQL or SingleStore
- Search → Elasticsearch
- Analytics → Tinybird or ClickHouse®
- Hybrid → Trade-offs required
What's the performance requirement?
- Multi-second acceptable → PostgreSQL extensions work
- Sub-second required → Need analytical database
- Sub-100ms → Tinybird or ClickHouse®
What's the operational capacity?
- Database operations team → Self-hosted options viable
- Limited ops capacity → Managed services only
- No ops team → Definitely managed (Tinybird)
What's the scale?
- Millions of rows → PostgreSQL might suffice
- Billions of rows → Need analytical database
- Petabytes → Distributed analytics platform
What's the use case?
- Internal dashboards → Various options work
- Customer-facing analytics → Need performance
- Real-time monitoring → Need speed and freshness
- API-backed features → Need instant APIs
Real-World Scenario Analysis
Scenario 1: Customer Usage Analytics Dashboard
Problem: Show customers real-time usage analytics with sub-second queries.
ParadeDB Approach:
- Extend PostgreSQL with ParadeDB
- Batch load usage data periodically
- Queries take seconds on large datasets
- Build custom API layer on PostgreSQL
- Result: Slow, batch updates, complex
Tinybird Approach:
- Continuous usage event ingestion
- Sub-100ms queries on billions of events
- Instant APIs from SQL queries
- Real-time data, not batch
- Result: Fast, real-time, simple
Verdict: Tinybird delivers experience customers expect.
Scenario 2: Application with PostgreSQL for Transactions
Problem: Application uses PostgreSQL for transactional data, needs some analytics.
ParadeDB Approach:
- Add ParadeDB extensions to existing PostgreSQL
- Run analytics queries on same database
- Share resources with transactional workload
- Result: Unified database, potential resource conflicts
Tinybird Approach:
- Keep PostgreSQL for transactions
- Replicate data to Tinybird for analytics
- Specialized databases for each workload
- Result: Optimal performance for each use case
Verdict: Specialized databases better than forcing PostgreSQL to do everything.
Scenario 3: Log Analytics and Search
Problem: Analyze application logs with search and aggregations.
ParadeDB Approach:
- Load logs into PostgreSQL with ParadeDB
- Full-text search and analytics
- Single database for both
- Result: Unified but compromised
Alternative Approach:
- Elasticsearch for search-heavy workloads
- Tinybird for analytical queries
- Right tool for each job
Verdict: Specialized tools excel vs. jack-of-all-trades compromises.
Conclusion
ParadeDB represents an ambitious attempt to make PostgreSQL handle search and analytics workloads by adding extensions. While the unified database vision is appealing, fundamental architecture limitations remain, PostgreSQL's row-oriented storage, indexing strategies, and query optimization weren't designed for analytical workloads at scale.
For organizations whose primary need is real-time analytics, dashboards showing metrics on billions of events, APIs serving aggregations, operational monitoring, purpose-built analytics platforms like Tinybird deliver dramatically better outcomes. Sub-100ms queries on billions of rows, managed infrastructure eliminating database operations, instant APIs without custom development, capabilities PostgreSQL extensions fundamentally cannot match.
Elasticsearch remains the best choice for search-heavy workloads. ClickHouse® (self-hosted) provides exceptional analytics performance for teams wanting infrastructure control. TimescaleDB improves PostgreSQL for time-series but can't overcome architectural limitations.
The right choice depends on your actual requirements. If you need PostgreSQL for transactions and light analytics, ParadeDB might help. If you need real-time analytics performance at scale, purpose-built platforms deliver better results without asking PostgreSQL to be something it's not.
Frequently Asked Questions
What's the main limitation of ParadeDB?
PostgreSQL's underlying row-oriented architecture. ParadeDB adds columnar storage and search extensions, but the fundamental database architecture wasn't designed for analytical workloads at scale. Purpose-built analytics databases (ClickHouse®, Druid, Tinybird) outperform by orders of magnitude because they're optimized for analytics from the ground up.
ParadeDB improves PostgreSQL analytics but can't match specialized analytics databases. Architecture matters more than extensions.
Can Tinybird replace ParadeDB?
If you're using ParadeDB for analytics, yes. Tinybird provides dramatically better analytical query performance (sub-100ms vs. seconds), managed infrastructure (vs. operating PostgreSQL), and instant APIs (vs. building custom).
If you specifically need PostgreSQL compatibility for application requirements, Tinybird uses ClickHouse® instead. But for analytics use cases, better performance and operations matter more than PostgreSQL compatibility.
Is it better to specialize databases or use hybrid solutions?
Specialized databases excel at their specific use case. PostgreSQL excellent for transactions, ClickHouse® for analytics, Elasticsearch for search. Hybrid solutions (ParadeDB, SingleStore) make compromises, adequate at multiple things, best-in-class at none.
For production use cases where performance matters, specialized databases deliver better results. Use PostgreSQL for transactions, Tinybird for analytics, each excelling at its purpose.
How does Tinybird compare to self-hosted ClickHouse?
Tinybird is managed ClickHouse® with instant APIs and zero operational overhead. Same performance engine, dramatically simpler:
- No cluster management or ClickHouse® expertise required
- Automatic scaling without capacity planning
- Instant APIs from SQL queries (vs. building custom)
- Built-in monitoring and observability
If you want ClickHouse® performance without operational complexity, Tinybird provides that. Self-hosted ClickHouse® for teams wanting infrastructure control and having operational capacity.
When should I actually use ParadeDB?
ParadeDB makes sense when:
- Already committed to PostgreSQL infrastructure
- Need PostgreSQL compatibility for specific applications
- Handling light analytical workloads within PostgreSQL's capabilities
- Team expertise entirely PostgreSQL-focused
- Data volumes small enough (millions, not billions of rows)
For real-time analytics at scale, customer-facing use cases, or when performance critical, purpose-built analytics platforms deliver better outcomes without PostgreSQL limitations.