TimescaleDB (TigerData) Alternatives: 10 Best Options Compared

These are the best alternatives to TimescaleDB for time-series data and real-time analytics:

1. Tinybird
2. ClickHouse®
3. VictoriaMetrics
4. QuestDB
5. InfluxDB
6. Apache Druid
7. Prometheus with Thanos/Mimir
8. CrateDB
9. Amazon Timestream
10. PostgreSQL Native Partitioning

When you need to handle time-series workloads while staying within the PostgreSQL ecosystem, TimescaleDB (now part of TigerData after the June 2025 rebrand) has become a popular choice. It transforms PostgreSQL into a time-series powerhouse through hypertables, compression, and continuous aggregates.

TimescaleDB's core appeal is PostgreSQL compatibility: keep your existing SQL skills, tooling, and integrations while gaining automatic partitioning, columnar compression, and incremental materialized views optimized for time-series patterns.

But that PostgreSQL foundation comes with inherent limitations. When workloads shift from telemetry and IoT toward product analytics with high concurrency, TimescaleDB requires significant tuning. High-cardinality queries without pre-aggregation become expensive. And serving real-time APIs at scale demands more infrastructure than many teams expect.

Teams evaluating TimescaleDB alternatives typically fall into four categories: those focused on observability and Prometheus metrics, those handling IoT and telemetry, those building real-time product analytics, and those who simply want time-series without leaving PostgreSQL.

We evaluate each alternative based on architecture, ideal workloads, operational complexity, and performance characteristics to help you choose the right tool for your specific needs.

Need real-time analytics APIs without time-series database complexity?

If you're evaluating TimescaleDB alternatives because your real need is product analytics at scale—user funnels, cohort analysis, real-time dashboards with instant APIs—consider Tinybird. It's a real-time data platform built on ClickHouse® that handles everything from streaming ingestion to instant API publication. No hypertable tuning, no compression policies, just SQL queries that become production-ready HTTP endpoints in seconds.

1. Tinybird: Real-Time Analytics Platform Built for Scale

Before diving into time-series database alternatives, let's address a fundamentally different approach that solves the underlying problem many teams face when evaluating TimescaleDB.

Tinybird isn't a time-series database—it's a complete real-time analytics platform built on ClickHouse® that handles ingestion, transformation, and API publication in one integrated service.

If your actual need is product analytics and real-time APIs rather than pure time-series storage, Tinybird eliminates the complexity that makes TimescaleDB challenging for these workloads.

Why Product Analytics Doesn't Fit Time-Series Databases

TimescaleDB optimizes PostgreSQL for time-series patterns: data partitioned by time, queries over time ranges, aggregations by time buckets. This works brilliantly for telemetry, IoT, and metrics.

But product analytics has different characteristics:

• High-cardinality dimensions like user_id, session_id, feature flags
• Complex aggregations beyond time buckets: funnels, cohorts, rankings
• High concurrent queries from user-facing dashboards
• Sub-100ms latency requirements for embedded analytics

TimescaleDB can handle some of these patterns with continuous aggregates and careful tuning, but you're fighting against the architecture rather than working with it.

Purpose-Built for Event Analytics

Tinybird uses ClickHouse® under the hood—a columnar database designed from the ground up for analytical queries on event data. The difference is architectural:

• Columnar storage reads only the columns needed, not entire rows
• Vectorized execution processes data in batches for massive efficiency
• Native support for high cardinality without the performance cliff
• No pre-aggregation required for most query patterns

The result: sub-100ms queries on billions of events without continuous aggregates or careful index design, delivering consistently low latency performance even under high concurrency.

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 TimescaleDB relies on PostgreSQL's transactional model with hypertables and chunks, Tinybird is streaming-first by design. Streaming data flows continuously from sources like Kafka, webhooks, S3, or direct HTTP ingestion and becomes immediately queryable.

No batch windows. No chunk management. One unified system handles everything with sub-100ms query latency on fresh data.

Fully Managed Infrastructure

With TimescaleDB, you manage hypertable configuration, compression policies, continuous aggregate refreshes, and chunk retention. Even with Timescale Cloud, there's operational complexity around tuning and optimization.

Tinybird eliminates operational burden entirely. Automatic scaling, built-in high availability, SOC 2 Type II compliance, and expert support come standard. Your engineers write SQL queries and build data products while Tinybird's team handles infrastructure.

When Tinybird Makes Sense

Tinybird is ideal when:

• Your core need is product analytics and event data, not pure time-series
• You're building user-facing dashboards or embedded analytics
• You need sub-100ms query latency with high concurrency
• You want instant APIs without building backend services
• Operational simplicity matters more than PostgreSQL compatibility

2. ClickHouse®: The Open-Source Columnar Powerhouse

If Tinybird's managed platform isn't the right fit but you need columnar analytics at scale, ClickHouse® is the open-source engine that powers many high-performance analytics systems including Tinybird itself.

Built for Analytical Queries

ClickHouse® was designed from the ground up for OLAP workloads. Unlike TimescaleDB, which adds time-series capabilities to PostgreSQL's row-oriented foundation, ClickHouse® uses native columnar storage that fundamentally changes performance characteristics.

Vectorized query execution processes columns in batches, achieving speeds impossible with row-oriented databases. Compression algorithms optimized for columnar data reduce storage by 10-100x while accelerating queries.

Time-Series Capabilities

ClickHouse® handles time-series workloads extremely well, though it wasn't designed exclusively for them. Features like TTL policies, aggregating merge trees, and materialized views provide similar capabilities to TimescaleDB's continuous aggregates.

The key difference: ClickHouse® doesn't sacrifice general analytical performance for time-series optimization. It excels at both patterns.

Operational Complexity

Running ClickHouse® in production requires significant expertise. Cluster management, replication configuration, capacity planning, and query optimization all demand attention.

For teams with strong infrastructure skills, this provides maximum flexibility. For others, managed solutions like Tinybird eliminate this burden while providing ClickHouse® performance.

When ClickHouse® Fits

Consider ClickHouse® when:

• Analytics performance is the primary requirement
• Your team has distributed systems expertise
• You need maximum control over configuration
• You want one engine for time-series and general analytics

3. VictoriaMetrics: Purpose-Built for Prometheus Metrics

If your TimescaleDB use case is observability and Prometheus-compatible metrics, VictoriaMetrics offers a purpose-built alternative designed specifically for this workload.

Prometheus-Native Architecture

VictoriaMetrics is built around Prometheus compatibility: native support for PromQL, remote write, and remote read. It can serve as a long-term storage backend for Prometheus or as a complete replacement.

For teams running Prometheus-based monitoring stacks, VictoriaMetrics provides drop-in compatibility that TimescaleDB's Prometheus adapter can't match.

Exceptional Compression and Performance

VictoriaMetrics achieves extremely high compression ratios on metrics data—often 10x better than Prometheus and competitive with or better than TimescaleDB for this specific workload.

Query performance on metrics workloads is also excellent, with PromQL execution optimized for the data patterns common in monitoring.

Cost Efficiency at Scale

For high-cardinality metrics at scale, VictoriaMetrics typically offers better cost efficiency than general-purpose time-series databases. The architecture is optimized specifically for the cardinality and query patterns of infrastructure monitoring.

When VictoriaMetrics Fits

Consider VictoriaMetrics when:

• Prometheus compatibility is essential
• Your workload is metrics and observability, not general analytics
• Cost efficiency at high cardinality matters
• You want purpose-built performance for monitoring data

4. QuestDB: High-Performance Time-Series SQL

QuestDB focuses on maximum ingestion performance for time-series data while maintaining SQL compatibility and supporting the InfluxDB Line Protocol (ILP).

Ingestion-Optimized Architecture

QuestDB is built for sustained high-throughput ingestion—millions of rows per second on modest hardware. For workloads where ingestion rate is the primary constraint, QuestDB often outperforms alternatives.

The architecture uses memory-mapped files and append-only structures optimized specifically for time-series write patterns.

SQL and ILP Support

Unlike some time-series databases with proprietary query languages, QuestDB supports standard SQL. It also provides native InfluxDB Line Protocol support, making it a natural migration path from InfluxDB.

For teams comfortable with ILP pipelines, QuestDB offers a familiar ingestion interface with different performance characteristics.

Trade-offs to Consider

QuestDB's lock-free architecture creates some operational considerations. Schema changes and certain operations require careful planning in production environments.

The ecosystem is smaller than TimescaleDB's PostgreSQL foundation, meaning fewer integrations and extensions are available.

When QuestDB Fits

Consider QuestDB when:

• Ingestion throughput is your primary concern
• You're migrating from InfluxDB and want ILP compatibility
• SQL queries on time-series meet your analytical needs
• You don't need the broader PostgreSQL ecosystem

5. InfluxDB: The Time-Series Standard

InfluxDB remains one of the most widely deployed time-series databases, with a mature ecosystem and purpose-built architecture for metrics and events.

Purpose-Built Time-Series Engine

InfluxDB was designed from the ground up for time-series workloads. Unlike TimescaleDB's approach of extending PostgreSQL, InfluxDB uses a custom storage engine optimized specifically for time-stamped data.

InfluxQL and Flux query languages provide time-series-specific operations that feel more natural than SQL for some monitoring patterns.

Ecosystem and Integrations

InfluxDB has extensive integrations with monitoring tools, data collectors, and visualization platforms. Telegraf for collection, Chronograf for visualization, and the broader TICK stack provide a complete monitoring solution.

For teams already invested in this ecosystem, the switching cost to alternatives is significant.

Cloud and Open Source Options

InfluxData offers InfluxDB Cloud as a managed service alongside the open-source version. The cloud offering provides serverless scaling without operational burden.

Licensing changes in recent versions have created concerns for some users, making alternatives more attractive for certain deployment scenarios.

When InfluxDB Fits

Consider InfluxDB when:

• Existing investment in the InfluxDB ecosystem
• Purpose-built time-series features matter more than SQL compatibility
• You want a mature, well-documented solution
• Cloud managed options align with your infrastructure strategy

6. Apache Druid: Real-Time OLAP for Events

Apache Druid provides real-time OLAP capabilities with a focus on high concurrency and sub-second queries on event data—a different architectural approach than TimescaleDB.

Column-Oriented Real-Time Analytics

Druid combines real-time ingestion with columnar storage for analytical queries. Data becomes queryable within seconds of ingestion, with no batch processing delays.

The architecture supports very high concurrent query loads, making it suitable for user-facing dashboards where TimescaleDB might struggle.

Pre-Aggregation and Roll-Ups

Druid's roll-up capabilities automatically aggregate data during ingestion, reducing storage and accelerating queries. For workloads with predictable aggregation patterns, this provides significant efficiency gains.

The trade-off: less query flexibility than systems that store raw events. You need to plan aggregations upfront.

Operational Complexity

Druid's architecture involves multiple node types: historicals, brokers, coordinators, and overlords. Operating Druid at scale requires substantial expertise.

For teams without dedicated infrastructure resources, this complexity may outweigh the performance benefits.

When Druid Fits

Consider Druid when:

• High concurrent query loads are expected
• Sub-second latency on analytical queries is critical
• Your team can handle significant operational complexity
• Pre-aggregation patterns match your query needs

7. Prometheus with Thanos or Mimir: Scalable Metrics Stack

For teams whose TimescaleDB use is primarily Prometheus metrics, the native Prometheus ecosystem with Thanos or Mimir for scale may be a better fit.

Native Prometheus Experience

Thanos and Mimir extend Prometheus with long-term storage, horizontal scaling, and multi-cluster federation while maintaining full PromQL compatibility.

You keep the native Prometheus experience with additional capabilities for scale and retention.

Object Storage Backend

Both Thanos and Mimir use object storage (S3, GCS) for long-term data, providing cost-effective retention at scale. This architecture suits high-volume metrics with long retention requirements.

Compaction and downsampling reduce storage costs while maintaining query performance for historical data.

Ecosystem Integration

These solutions integrate seamlessly with Grafana, alert managers, and the broader Prometheus ecosystem. For teams with existing investments, the integration is natural.

TimescaleDB's Prometheus adapter can't match this level of native compatibility.

When Prometheus Stack Fits

Consider Thanos/Mimir when:

• Prometheus is your primary metrics system
• You need horizontal scaling beyond single Prometheus
• Long-term retention on object storage fits your cost model
• PromQL is your preferred query language

8. CrateDB: Distributed SQL for Time-Series

CrateDB offers distributed SQL with a focus on time-series and machine data, providing horizontal scaling with familiar SQL syntax.

Distributed Architecture

CrateDB distributes data across nodes automatically, providing horizontal scaling without the manual sharding decisions TimescaleDB requires for multi-node deployments.

SQL compatibility is high, making migration from PostgreSQL-based systems relatively straightforward.

Time-Series Optimizations

CrateDB includes partitioning by time, columnar storage for specific columns, and aggregation optimizations for time-series patterns.

The combination of distributed architecture and time-series features addresses some of TimescaleDB's scaling limitations.

Operational Considerations

CrateDB requires cluster management and capacity planning. The distributed nature adds complexity compared to single-node TimescaleDB deployments.

Cloud offerings are available but less mature than some alternatives.

When CrateDB Fits

Consider CrateDB when:

• Horizontal scaling is essential
• SQL compatibility matters
• You want distributed time-series without manual sharding
• Your team can handle cluster operations

9. Amazon Timestream: AWS-Native Time-Series

Amazon Timestream is AWS's managed time-series database, offering serverless scaling and tight AWS integration.

Serverless Architecture

Timestream automatically scales based on workload, with no infrastructure to manage. You pay for ingestion and queries, not for provisioned capacity.

For AWS-centric teams, this removes operational burden entirely.

AWS Ecosystem Integration

Timestream integrates with IoT Core, Kinesis, Lambda, and other AWS services. For IoT and telemetry workloads on AWS, the integration simplifies architecture.

IAM-based security and CloudWatch monitoring provide consistent management with other AWS services.

Query Capabilities and Limitations

Timestream uses SQL-like syntax but with time-series-specific extensions. The query language is less mature than PostgreSQL SQL.

Performance and cost at scale have received mixed reviews from users, particularly for complex analytical queries.

When Timestream Fits

Consider Timestream when:

• AWS is your primary cloud platform
• Serverless operations are a priority
• Your workload is IoT or telemetry with AWS integration
• You can accept query language limitations

10. PostgreSQL Native Partitioning: The DIY Baseline

Before adopting TimescaleDB, it's worth understanding what native PostgreSQL partitioning can achieve—and where its limitations drive teams toward specialized solutions.

Built-In Table Partitioning

PostgreSQL supports declarative partitioning by range, list, or hash. For time-series data, range partitioning by time provides the foundation of TimescaleDB's approach.

You can implement similar patterns manually without extensions.

What You Gain and Lose

Native partitioning gives you partition pruning and easier maintenance. What you lose without TimescaleDB: automatic chunk management, compression policies, continuous aggregates, and hyperfunctions.

For simple time-series patterns, native partitioning may be sufficient. For complex workloads, the manual overhead becomes significant.

When to Stay Native

Consider PostgreSQL native partitioning when:

• Your time-series needs are simple and well-defined
• You want zero extension dependencies
• Manual partition management is acceptable
• You don't need compression or continuous aggregates

Why Tinybird Is the Best TimescaleDB Alternative

After evaluating all the alternatives, Tinybird emerges as the strongest choice for teams whose real need is product analytics and real-time APIs rather than pure time-series storage.

The Right Architecture for Product Analytics

Many teams adopt TimescaleDB hoping to serve analytics from their time-series data. They want dashboards, user-facing metrics, embedded analytics—workloads that stretch TimescaleDB's architecture.

But serving high-concurrency analytics from a PostgreSQL-based system creates challenges:

• Continuous aggregates help but require planning and maintenance
• High-cardinality queries without pre-aggregation become expensive
• Concurrent user requests compete for PostgreSQL resources
• API serving requires additional infrastructure layers

Tinybird solves this by providing a purpose-built analytical layer. Your TimescaleDB handles time-series storage. Tinybird handles analytics serving. Each system does what it was designed for.

Performance That Scales for Real-Time APIs

Tinybird is built on ClickHouse®, the fastest open-source columnar database for real-time data processing and analytical queries. While TimescaleDB adds time-series capabilities to PostgreSQL's architecture, ClickHouse® was designed from the ground up for analytics.

The difference shows at scale:

• Sub-100ms queries on billions of events
• High concurrency without performance degradation
• No continuous aggregates required for most patterns
• Native high-cardinality support without workarounds

From Query to Production API in Seconds

No TimescaleDB 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, this capability alone can replace weeks of backend development. No API framework to deploy, no scaling infrastructure to manage, no authentication layer to build.

Zero Time-Series Configuration Complexity

With TimescaleDB, you configure hypertable chunk intervals, compression policies, segment-by columns, continuous aggregate refresh intervals, and retention policies. Each decision affects performance and cost.

Tinybird eliminates this configuration complexity. No chunk tuning, no compression policies to maintain, no continuous aggregate refreshes to monitor. The platform handles all optimization automatically while you focus on queries.

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 TimescaleDB deployments where costs scale with compression efficiency, continuous aggregate compute, and PostgreSQL resource consumption, Tinybird offers fixed monthly plans that include compute and storage allocations.

You know your costs upfront. No surprise bills when query patterns change. No need to optimize compression settings to control spending.

Conclusion

Choosing a TimescaleDB alternative depends on understanding what problem you're actually solving.

For Prometheus metrics and observability, VictoriaMetrics provides native PromQL support and purpose-built performance. Thanos and Mimir extend Prometheus natively for scale.

For high-throughput time-series ingestion, QuestDB and InfluxDB offer specialized architectures. CrateDB provides distributed SQL with time-series features, while data warehouses remain a strong option for batch analytics and long-term storage use cases.

For large-scale telemetry and Internet of Things (IoT) environments, Amazon Timestream’s tight integration with AWS IoT Core enables continuous device data ingestion and time-series analysis with minimal operational overhead.

For product analytics at scale—the hidden driver behind many TimescaleDB 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 time-series databases.

The right choice depends on your specific workload, team capabilities, and architectural priorities. But if your real need is real-time analytics and APIs rather than pure time-series storage, 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 TimescaleDB and why did it become TigerData?

TimescaleDB is a PostgreSQL extension that adds time-series capabilities: hypertables for automatic partitioning, compression, and continuous aggregates. TigerData is the company's new name as of June 2025, repositioning from "time-series database" toward modern PostgreSQL for analytics and AI workloads.

Is Tinybird a time-series database like TimescaleDB?

No. Tinybird is a real-time analytics platform built on ClickHouse®, not a time-series database. If your need is pure time-series storage with PostgreSQL compatibility, TimescaleDB may fit better. If your need is product analytics with instant APIs, Tinybird is the better architectural choice.

Can TimescaleDB handle product analytics workloads?

TimescaleDB can handle some analytics patterns with continuous aggregates and careful tuning. For high-concurrency user-facing dashboards with complex queries, purpose-built OLAP systems like ClickHouse® or Tinybird typically perform significantly better.

What's the main advantage of staying with TimescaleDB?

PostgreSQL compatibility. TimescaleDB preserves PostgreSQL syntax, extensions, tools, and ecosystem. For teams with strong PostgreSQL investments and time-series workloads, this reduces migration costs and learning curves.

When should I use VictoriaMetrics instead of TimescaleDB?

When your workload is Prometheus metrics and observability. VictoriaMetrics provides native PromQL support, better compression for metrics, and purpose-built performance for monitoring data. TimescaleDB is more general-purpose.

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 continuous aggregates in TimescaleDB similar to materialized views in ClickHouse®?

Conceptually similar, but with different mechanics. TimescaleDB continuous aggregates refresh incrementally as data arrives. ClickHouse® materialized views can achieve similar results with different performance characteristics. Tinybird handles these patterns with its pipe-based transformation layer.