{"slug":"best-time-series-database","title":"Best time-series database","question":"What are the best time-series database?","verdict":"As of 2026-07-15, ChatGPT, Claude, Gemini and Grok collectively rank TimescaleDB #1 for time-series database on ModelsAgree — a unanimous pick. The models' case: Best all-round choice for most teams: full PostgreSQL compatibility, strong SQL and relational joins, hypertables, automatic retention, columnar compression, and. The models' main caveat: Native multi-node TimescaleDB was sunset, so it is not the best self-hosted option for extreme horizontal scale. The strongest alternative is ClickHouse — Exceptional ingest, compression, and analytical-query performance at large scale, with mature clustering and materialized views. Source: https://modelsagree.com/best/best-time-series-database (modelsagree.com, CC BY 4.0).","category":"Time-Series DB","url":"https://modelsagree.com/best/best-time-series-database","updated":"2026-07-15","models":["ChatGPT","Claude","Gemini","Grok"],"consensus":"All 4 models rank TimescaleDB the top pick","disagreement":null,"combined":[{"rank":1,"product":"TimescaleDB","domain":"timescale.com","score":20,"appearances":4,"modelRanks":{"ChatGPT":1,"Claude":1,"Gemini":1,"Grok":1},"reason":"Best all-round choice for most teams: full PostgreSQL compatibility, strong SQL and relational joins, hypertables, automatic retention, columnar compression, and incremental continuous aggregates; near-tied with ClickHouse, but easier to adopt for general application workloads"},{"rank":2,"product":"ClickHouse","domain":"clickhouse.com","score":13,"appearances":4,"modelRanks":{"ChatGPT":2,"Claude":2,"Gemini":3,"Grok":4},"reason":"Exceptional ingest, compression, and analytical-query performance at large scale, with mature clustering and materialized views; it can outperform purpose-built TSDBs for observability and high-cardinality event data"},{"rank":3,"product":"InfluxDB","domain":"influxdata.com","score":10,"appearances":4,"modelRanks":{"ChatGPT":3,"Claude":4,"Gemini":4,"Grok":3},"reason":"Purpose-built ingestion, retention, time-series APIs, SQL/InfluxQL querying, Parquet-based storage, and a strong telemetry ecosystem make it productive for metrics, IoT, and operational data"},{"rank":4,"product":"VictoriaMetrics","domain":"victoriametrics.com","score":9,"appearances":3,"modelRanks":{"ChatGPT":4,"Claude":3,"Gemini":2},"reason":"Exceptional resource efficiency, offering extremely low CPU and memory usage alongside excellent compression and high ingestion rates. It is highly compatible with the Prometheus ecosystem and trivial to operate as a single binary."},{"rank":5,"product":"QuestDB","domain":"questdb.io","score":7,"appearances":4,"modelRanks":{"ChatGPT":5,"Claude":5,"Gemini":5,"Grok":2},"reason":"Exceptional high-throughput ingestion + sub-second low-latency SQL queries on massive datasets in independent TSBS-style benchmarks, columnar design, time-series SQL extensions, open formats (Parquet tiering), and strong real-world performance for demanding analytics/monitoring without heavy ops overhead."},{"rank":6,"product":"TDengine","domain":"tdengine.com","score":1,"appearances":1,"modelRanks":{"Grok":5},"reason":"Strong ingestion/compression/query performance in IoT-focused benchmarks, built-in clustering/HA in OSS, efficient for high-volume device/sensor data with SQL-like querying."}],"perModel":{"ChatGPT":[{"rank":1,"product":"TimescaleDB","reason":"Best all-round choice for most teams: full PostgreSQL compatibility, strong SQL and relational joins, hypertables, automatic retention, columnar compression, and incremental continuous aggregates; near-tied with ClickHouse, but easier to adopt for general application workloads","fix":"Native multi-node TimescaleDB was sunset, so it is not the best self-hosted option for extreme horizontal scale"},{"rank":2,"product":"ClickHouse","reason":"Exceptional ingest, compression, and analytical-query performance at large scale, with mature clustering and materialized views; it can outperform purpose-built TSDBs for observability and high-cardinality event data","fix":"Its append-oriented OLAP model and operational complexity are a poor fit for update-heavy workloads or teams wanting conventional transactional database behavior"},{"rank":3,"product":"InfluxDB","reason":"Purpose-built ingestion, retention, time-series APIs, SQL/InfluxQL querying, Parquet-based storage, and a strong telemetry ecosystem make it productive for metrics, IoT, and operational data","fix":"Product-generation churn plus clustering and advanced production capabilities being concentrated in commercial editions make the open-source path less predictable"},{"rank":4,"product":"VictoriaMetrics","reason":"Outstanding value for Prometheus-style monitoring: high compression, fast MetricsQL queries, efficient long-term retention, straightforward single-node operation, and a scalable cluster edition","fix":"Its metrics-centric data model and query ecosystem are not suitable for general relational time-series applications"},{"rank":5,"product":"QuestDB","reason":"Near-tied with VictoriaMetrics for specialized use cases; very fast ingestion and low-latency SQL over timestamped data, especially compelling for market data, IoT, and real-time analytics","fix":"Its ecosystem, integrations, and distributed-production track record remain narrower than those of the leaders"}],"Claude":[{"rank":1,"product":"TimescaleDB","reason":"Full PostgreSQL — the typical practitioner keeps SQL, joins, their existing drivers and tooling, and adds hypertables, columnar compression (90%+ typical), and continuous aggregates; the least new-system risk of any serious TSDB, and now proven at petabyte scale. Assumes the practitioner values ecosystem fit over raw peak ingest","fix":"Single-node-Postgres heritage means the highest-cardinality, highest-ingest workloads (millions of series, multi-node) are handled better by purpose-built columnar engines; not for massive metrics fleets"},{"rank":2,"product":"ClickHouse","reason":"Near-tie with #1 — the fastest widely-available analytical engine for time-series at scale, dominant backend for observability products (Datadog-scale clones, logs/traces/metrics), superb compression and query speed on billions of rows, healthy open-source core plus mature Cloud. Ranked #2 only because it's a general OLAP store, so it demands more schema/ops sophistication than a turnkey TSDB","fix":"No built-in TSDB conveniences (retention/downsampling policies, PromQL) — you assemble them yourself; sparse-index design and merge mechanics punish naive schemas and high-frequency small inserts"},{"rank":3,"product":"VictoriaMetrics","reason":"The pragmatic choice for metrics/monitoring: drop-in Prometheus remote-write and PromQL(-ish MetricsQL), dramatically lower RAM/disk than Prometheus+Thanos stacks, single-binary simplicity scaling to a clustered version, permissive open source. Assumes the workload is observability metrics rather than general event analytics","fix":"Metrics-shaped only — no SQL, weak fit for irregular events, business analytics, or wide records; MetricsQL divergence from strict PromQL occasionally bites migrations"},{"rank":4,"product":"InfluxDB","reason":"InfluxDB 3's Rust/Arrow/Parquet/DataFusion rewrite fixed the old cardinality ceiling and brought real SQL, strong ingest performance, and the largest brand recognition in the category with good IoT/sensor ergonomics","fix":"Years of platform churn (1.x→2.x/Flux deprecated→3.x) fractured the ecosystem and trust, and the open-source Core edition is deliberately limited (recent-data query window), pushing serious use toward paid Enterprise/Cloud"},{"rank":5,"product":"QuestDB","reason":"Exceptional single-node ingest speed (ILP protocol) with plain SQL and Postgres-wire compatibility, open source, genuinely easy to operate; a favorite for financial tick data and industrial telemetry on modest hardware","fix":"Smaller ecosystem and team than the four above; replication/HA and distributed scale-out are recent and enterprise-gated, so large clustered deployments carry more risk"}],"Gemini":[{"rank":1,"product":"TimescaleDB","reason":"Integrates natively with PostgreSQL, providing full SQL, ACID transactions, and relational joins alongside automatic partitioning and columnar compression, making it the most versatile and lowest-risk option for typical practitioners.","fix":"Inherits PostgreSQL's write-path overhead, making it less resource-efficient and harder to scale to extreme write-heavy workloads compared to dedicated columnar TSDBs."},{"rank":2,"product":"VictoriaMetrics","reason":"Exceptional resource efficiency, offering extremely low CPU and memory usage alongside excellent compression and high ingestion rates. It is highly compatible with the Prometheus ecosystem and trivial to operate as a single binary.","fix":"Limited to numeric metrics and monitoring use cases, lacking SQL support, relational joins, or the ability to update/delete individual data points easily."},{"rank":3,"product":"ClickHouse","reason":"Although a general columnar database, its unmatched ingestion rates, compression, and query speed on billions of rows make it the premier choice for large-scale event, log, and time-series data.","fix":"High operational complexity and steep learning curve for performance tuning, with no support for transactions or point updates."},{"rank":4,"product":"InfluxDB","reason":"The v3 storage engine (IOx/Rust/Apache Arrow) has solved historical high-cardinality bottlenecks, enabling high-performance SQL and InfluxQL queries over massive tag sets using open data standards.","fix":"Heavy fragmentation between v1/v2/v3 versions and complex licensing, with the latest high-performance v3 engine primarily gated behind commercial/cloud tiers."},{"rank":5,"product":"QuestDB","reason":"Achieves ultra-low latency and raw ingestion performance via a memory-mapped columnar design, zero-GC Java, and C++, supporting standard SQL queries and InfluxDB line protocol.","fix":"The open-source version lacks built-in clustering or native high-availability, and its community ecosystem is much smaller than its competitors."}],"Grok":[{"rank":1,"product":"TimescaleDB","reason":"PostgreSQL extension delivers full SQL + time-series optimizations (continuous aggregates, hypertables, compression) with excellent ecosystem integration, developer familiarity, reliability for mixed workloads, strong compression/query perf in benchmarks, and mature scaling options for typical monitoring/IoT/analytics users.","fix":"Not the absolute fastest raw ingestion for extreme high-cardinality IoT compared to purpose-built columnar engines (better for moderate-to-high volume where SQL maturity matters)."},{"rank":2,"product":"QuestDB","reason":"Exceptional high-throughput ingestion + sub-second low-latency SQL queries on massive datasets in independent TSBS-style benchmarks, columnar design, time-series SQL extensions, open formats (Parquet tiering), and strong real-world performance for demanding analytics/monitoring without heavy ops overhead.","fix":"Smaller ecosystem/community than leaders; less ideal for users needing deep PostgreSQL extensions or very broad non-time-series features."},{"rank":3,"product":"InfluxDB","reason":"Mature, purpose-built for metrics/observability with straightforward InfluxQL/Flux/SQL, excellent managed cloud offering, downsampling/retention policies, and widespread adoption making it low-friction for typical monitoring stacks and small-to-medium deployments.","fix":"Commercial licensing shifts and single-node limits in core OSS version; higher costs or complexity at extreme scale vs columnar alternatives."},{"rank":4,"product":"ClickHouse","reason":"Blazing fast columnar analytics on time-series data, outstanding compression/ingestion scalability, SQL support, and versatility for both pure TSDB and broader OLAP use cases, proven in high-volume production environments.","fix":"Steeper learning curve for pure time-series (more general-purpose); overkill or less optimized out-of-box for simple metrics collection vs dedicated TSDBs."},{"rank":5,"product":"TDengine","reason":"Strong ingestion/compression/query performance in IoT-focused benchmarks, built-in clustering/HA in OSS, efficient for high-volume device/sensor data with SQL-like querying.","fix":"Smaller overall adoption/ecosystem and less flexible for non-IoT general time-series or complex analytical joins compared to top options."}]},"missedByModel":{"ChatGPT":[{"product":"Apache Druid","reason":"excellent distributed real-time OLAP, but heavier to operate and less natural as a general-purpose TSDB"},{"product":"Prometheus","reason":"the monitoring standard, but its local storage is not designed as a durable general-purpose time-series database"}],"Claude":[{"product":"Prometheus","reason":"the de-facto metrics standard, but it's a monitoring system with deliberately limited long-term/durable storage — as a database you end up on Thanos, Mimir, or VictoriaMetrics anyway"},{"product":"kdb+","reason":"unmatched latency for quant finance tick analytics, but proprietary q language, opaque enterprise pricing, and a talent pool that keeps it a niche rather than a general practitioner pick"}],"Gemini":[{"product":"Prometheus","reason":"lacks long-term storage efficiency and scalability out of the box, typically requiring integration with Thanos or VictoriaMetrics"},{"product":"Apache Druid","reason":"offers powerful real-time analytics but is complex to deploy and maintain, requiring a heavy JVM stack"}],"Grok":[{"product":"Prometheus","reason":"dominant for Kubernetes/monitoring but pull-model and limited long-term storage make it more a monitoring system component than general TSDB"}]}}