Comparing InfluxDB, TimescaleDB, and QuestDB Time-Series Databases

Time-series database have quietly become one of the most competitive categories in the modern data stack. What began as a niche solution for metrics and monitoring has expanded into a foundational layer for observability, IoT, financial markets, and, more recently, AI and ML workloads that depend on high-resolution temporal data.

Among the most frequently evaluated options today are InfluxDB, TimescaleDB, and QuestDB. All three target time-series workloads, but they arrive there via very different architectural paths. And those differences matter more than ever in 2026.

Before diving into the comparison, it’s worth revisiting why time-series databases have grown so quickly.

Why use a time-series database?

Time-series databases (TSDB) are optimized to ingest, store, and query data indexed by time. Typical examples include:

• Infrastructure and application metrics
• Observability signals (logs, traces, metrics)
• IoT and sensor readings
• User behavior and event streams
• Financial market and trading data
• Feature stores and time-indexed ML inputs

Time-series workloads also tend to share a few defining characteristics:

• Every record is time-indexed, often at high resolution
• Write throughput matters more than transactional updates
• Queries are window-based, aggregating across time ranges
• Downsampling and rollups are more important than point lookups
• Data volume grows continuously, while older data becomes less relevant

Traditional relational databases can model time-series data, but they are rarely optimized for sustained high-frequency ingestion or large analytical scans. As cloud-native architectures, real-time analytics, and AI pipelines proliferate, TSDBs have emerged as one of the fastest-growing database categories.

InfluxDB

InfluxData released InfluxDB in 2013 and helped define the modern TSDB category. For years, it dominated metrics and observability use cases, supported by a rich ecosystem of agents, plugins, and integrations. That dominance, however, came with architectural trade-offs, particularly around cardinality and query flexibility, which led to a multi-year redesign effort culminating in InfluxDB 3.0 Core, generally available as of April 15, 2025.

A clean architectural break

InfluxDB 3.0 is not an incremental update. It is a ground-up rewrite:

• Rust-based core engine for safety and performance
• Apache Arrow as the in-memory format
• Parquet as the primary on-disk storage format
• DataFusion as the SQL query engine
• Arrow Flight for high-performance client/server transport

This new architecture replaces the v1/v2 era stack entirely. Gone are the TICK stack (Telegraf, InfluxDB, Chronograf, Kapacitor) and Flux, replaced by the Rust-based engine with SQL as the primary query language.

What improves in InfluxDB 3.0

InfluxDB 3.0 addressed several long-standing pain points from its previous predecessors:

• Unlimited cardinality: series explosion is no longer a first-order concern
• Standard SQL: no custom query language required
• Better ecosystem alignment: Arrow + Parquet are lingua franca formats
• Embedded Python VM: user-defined functions and in-engine processing

After years of uncertainty with licensing, InfluxDB 3.0 Core was released under MIT / Apache 2.0, and separated the OSS version with the cloud-hosted, enterprise model of InfluxDB 3.

Where the trade-offs remain

Despite the architectural leap, InfluxDB 3.0 is still a young engine. Benchmarks published in late 2025 show that, while v3 eliminates cardinality-driven degradation, it is often slower than InfluxDB v1/v2 on sustained ingestion and some analytical workloads. This is not unusual for a complete engine rewrite with improvements incoming, but still an important practical consideration for teams migrating either from older versions or other TSDBs today.

Also, many in the community were disappointed to find rather strict limitations released under the Core version (e.g., 72h of retention, 5 database limits) compared to the paid tier as well as losing backwards compatibility for those on older versions of InfluxDB. InfluxDB 3.0 also currently lacks convenience features related to time-series data such as built-in continuous aggregates or materialized views. The suggested workaround (a trigger with a downsampler plugin) adds complexity, consumes extra storage, and is less real-time than native implementations.

• Pros: Modern columnar architecture (Arrow + Parquet), SQL-first querying, unlimited cardinality, strong Python and AI interoperability
• Cons: immature engine, performance issues, lack of backwards compatibility, stringent limits

TimescaleDB

TigerData (formerly known as Timescale) rebranded in June 2025, reflecting a broader ambition beyond “just” time-series. The core open-source product remains TimescaleDB, a PostgreSQL extension, now positioned under the banner of “PostgreSQL++ for Time Series, Analytics & AI.”

PostgreSQL at the center

TimescaleDB’s value proposition has always been clear: keep PostgreSQL, add time-series performance. This brings immediate advantages:

• Full SQL compatibility
• Mature ecosystem of extensions
• Familiar operational model
• Strong transactional guarantees

For teams already invested in PostgreSQL, TimescaleDB remains the lowest-friction way to introduce time-series capabilities.

What’s new in 2025–2026

Recent releases have expanded the scope significantly:

• TimescaleDB 2.20: up to 10× faster backfills
• Hypercore: a hybrid row-columnar storage engine
• pgvectorscale: scalable vector similarity search
• pgai: SQL-native AI workflows inside Postgres
• Tiger Cloud: managed offering with integrated analytics

These additions push TimescaleDB beyond metrics and into analytical and AI-adjacent workloads—while still remaining PostgreSQL at heart.

The architectural ceiling

Despite these improvements, TimescaleDB inherits fundamental constraints from PostgreSQL:

• Ingestion rates are bounded by Postgres write paths
• Row-oriented storage remains dominant
• Continuous aggregates consume additional storage
• Streaming ingestion is not a first-class protocol

For many workloads, these are acceptable trade-offs. For ultra-high-frequency ingestion or large analytical scans, they become limiting.

• Pros: PostgreSQL compatibility, rich SQL and extension ecosystem, strong AI/vector roadmap

• Cons: limited by PostgreSQL performance, proprietary components under TSL, no longer focused on time-series

QuestDB

QuestDB takes the opposite approach: a purpose-built time-series engine, written from scratch on low-latency Java, C++, and Rust, optimized for ingestion and analytical queries. In 2026, QuestDB matured well beyond its early “fast but narrow” reputation.

Core architecture

QuestDB is:

• Column-oriented
• Append-only
• Time-partitioned
• SIMD-vectorized
• Highly parallel

Data is stored in immutable partitions sorted by time, allowing queries to remain stable even under heavy concurrent ingestion.

In recent releases, QuestDB has significantly expanded its expressiveness:

• N-dimensional arrays (ideal for L2 order books, vectors, embeddings)
• DECIMAL type for financial precision
• Nanosecond timestamps (TIMESTAMP_NS)
• Materialized views with calendar and timezone awareness
• ASOF JOIN with TOLERANCE
• Native Parquet import/export

These features build on one of the niceties of QuestDB supporting multiple ingestion paths (InfluxDB line protocol, PostgreSQL wire, REST, CSV) while maintaining SQL as the primary query language for compatibility with the existing ecosystem. However, similar to TimescaleDB, advanced features like continuous aggregation and materialized views do come with additional storage needs.

Performance

QuestDB shines against other TSDB when put under extreme performance needs. Benchmarks consistently show:

• Extremely high sustained ingestion rates
• Stable query latency under concurrent load
• Strong OLAP performance on ClickBench-style workloads

QuestDB is 12-36x faster than InfluxDB 3 Core for ingestion and whopping 43-418x faster for complex analytical queries. When running similar comparisons with TimescaleDB, QuestDB was 6-13x faster for ingestion and 16-20x faster on complex queries.

This makes QuestDB particularly attractive for market data, observability at scale, and analytical workloads that blur into AI feature generation.

• Pros: best-in-class performance, rich ecosystem compatibility, strong analytical and time-series feature set
• Cons: growing ecosystem, less focus on AI

Final thoughts

The time-series database market is no longer about whether to specialize, but how. InfluxDB is reinventing itself, TigerData is expanding PostgreSQL’s reach, and QuestDB continues to push the performance ceiling.

There is no single “best” choice, but in 2026, there are more options than ever for your use case. If you were already in the InfluxDB ecosystem, migrating to InfluxDB 3 Core might be a natural progression. You can choose TigerData if you want to keep PostgreSQL semantics intact or have an AI story you are exploring. Finally, QuestDB can be a great choice if performance is mission-critical and you want a SQL experience without the PostgreSQL overhead.