Best time-series database
4 models · updated 2026-08-14
The verdict
TimescaleDB leads — 3 of 4 models rank TimescaleDB the top pick.
Not unanimous: Grok picks QuestDB.
As of 2026-08-14, ChatGPT, Claude, Gemini and Grok collectively rank TimescaleDB #1 for time-series database on ModelsAgree by aggregate score. The models' case: Best default for a typical team needing time-series plus relational data: full PostgreSQL, automatic time partitioning, row-to-column storage, continuous aggregates. The models' main caveat: Built-in distributed hypertables are deprecated, so self-hosted horizontal scaling is weaker for extreme append-only workloads. The strongest alternative is ClickHouse — Exceptional for massive event, telemetry, and log streams: fast columnar scans, high ingest, specialized compression codecs, materialized views, TTLs. Not unanimous: Grok picks QuestDB. Source: https://modelsagree.com/best/best-time-series-database (modelsagree.com, CC BY 4.0).
Combined ranking
- 1GPT #1Claude #1Gemini #1Grok #2
Best default for a typical team needing time-series plus relational data: full PostgreSQL, automatic time partitioning, row-to-column storage, continuous aggregates, retention policies, rich SQL, and mature tooling. It is a near-tie with ClickHouse but wins on developer leverage and workload breadth.
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GPT Best default for a typical team needing time-series plus relational data: full PostgreSQL, automatic time partitioning, row-to-column storage, continuous aggregates, retention policies, rich SQL, and mature tooling. It is a near-tie with ClickHouse but wins on developer leverage and workload breadth.
Claude PostgreSQL extension, so full SQL, joins, and the Postgres ecosystem (tooling, replication, ACID) apply directly; hypertables give strong ingest and time-partitioned query performance, and continuous aggregates plus compression handle the classic time-series pain points; the safest default when a team wants time-series without abandoning relational data.
Gemini Unmatched versatility by building directly on PostgreSQL, allowing seamless joins between relational metadata and time-series data using full SQL, native tooling, mature continuous aggregates, and columnar compression; flags a near-tie with VictoriaMetrics for monitoring-heavy stacks
Grok Postgres extension delivering hypertables, continuous aggregates, compression, and full SQL/ACID in one system so practitioners reuse existing skills/tools/data with near-zero friction; strong real-world ingest/query for typical (non-extreme) volumes plus seamless mixed relational+TS workloads; near-tie with QuestDB when Postgres compatibility or ops simplicity outweighs raw speed
Where it falls shortper GPT Built-in distributed hypertables are deprecated, so self-hosted horizontal scaling is weaker for extreme append-only workloads.
per Claude Single-node scaling ceiling is real — very high-cardinality or petabyte-scale distributed workloads strain it, and its multi-node story has been de-emphasized versus specialized columnar stores.
per Gemini Resource overhead and single-node write scaling limits make it inefficient for pure petabyte-scale telemetry where relational guarantees and transactional semantics are unnecessary
per Grok Not the absolute throughput leader on pure high-ingest analytical benchmarks; inherits Postgres resource overhead and requires careful chunk/compression tuning for extreme scale
- 2GPT #2Claude #2Gemini #3Grok —
Exceptional for massive event, telemetry, and log streams: fast columnar scans, high ingest, specialized compression codecs, materialized views, TTLs, ASOF joins, rich analytics, and proven sharding and replication. It would rank first for petabyte-scale append-heavy analytics.
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GPT Exceptional for massive event, telemetry, and log streams: fast columnar scans, high ingest, specialized compression codecs, materialized views, TTLs, ASOF joins, rich analytics, and proven sharding and replication. It would rank first for petabyte-scale append-heavy analytics.
Claude Blistering columnar analytical performance on massive datasets with excellent compression; handles wide, high-cardinality time-series and observability workloads at scale that choke row stores; huge, active development and broad adoption for logs/metrics/events analytics.
Gemini World-class raw ingestion throughput, deep vectorized SIMD query execution, and high compression ratios for petabyte-scale event, metric, and log analytical workloads
Where it falls shortper GPT Performance depends heavily on schema and ordering-key design, while frequent updates and transactional workloads remain a poor fit.
per Claude Not a purpose-built TSDB — no native time-series conveniences out of the box, updates/deletes and high-frequency point lookups are weak, and operating a cluster well demands real expertise.
per Gemini High operational complexity for distributed clusters, lack of fine-grained point-in-time mutation ergonomics, and excessive overhead for standard, smaller-scale time-series deployments
- 3GPT #4Claude #5Gemini #4Grok #1
Fastest out-of-the-box ingestion and complex analytical queries on TSBS-style workloads in 2026 benchmarks (often 6-20x+ over TimescaleDB/InfluxDB 3), full SQL with native temporal joins (ASOF/WINDOW/HORIZON), columnar Parquet storage, high single-node throughput even at high cardinality; assumes practitioner prioritizes pure high-volume ingest + low-latency time queries over deep relational integration
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Grok Fastest out-of-the-box ingestion and complex analytical queries on TSBS-style workloads in 2026 benchmarks (often 6-20x+ over TimescaleDB/InfluxDB 3), full SQL with native temporal joins (ASOF/WINDOW/HORIZON), columnar Parquet storage, high single-node throughput even at high cardinality; assumes practitioner prioritizes pure high-volume ingest + low-latency time queries over deep relational integration
GPT Near-tied with InfluxDB 3 but ahead on open-source single-node value: very fast ingestion, strong out-of-order handling, PostgreSQL wire compatibility, and excellent time-series SQL including SAMPLE BY, LATEST ON, ASOF, and WINDOW joins.
Gemini Ultra-low-latency real-time ingestion via Influx Line Protocol, SIMD-accelerated time-based SQL extensions (SAMPLE BY), and memory-mapped file design optimized for financial tick data and high-frequency monitoring
Claude Exceptional ingest throughput and low-latency queries with SQL plus time-series extensions (SAMPLE BY, ASOF JOIN); very strong for financial/tick data and high-frequency workloads, with a lean operational footprint.
Where it falls shortper GPT High availability, replicas, robust security, automated backups, and object-storage tiering require QuestDB Enterprise.
per Claude Smaller ecosystem and community, thinner enterprise/HA and replication maturity than the leaders, so it's a riskier bet for large mission-critical deployments.
per Gemini Distributed clustering and advanced replication capabilities are restricted to commercial enterprise editions, and general ecosystem tooling is smaller than Postgres or ClickHouse
per Grok OSS is single-node focused with limited HA (replication is Enterprise); weaker for complex non-TS relational joins or teams already deep in Postgres ecosystems
- 4GPT #3Claude —Gemini #2Grok #3
Superior resource efficiency, near-zero operational maintenance (single-binary deployment), exceptional handling of high-cardinality metric streams, and native drop-in support for Prometheus and Graphite query APIs; flags a near-tie with TimescaleDB
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Gemini Superior resource efficiency, near-zero operational maintenance (single-binary deployment), exceptional handling of high-cardinality metric streams, and native drop-in support for Prometheus and Graphite query APIs; flags a near-tie with TimescaleDB
GPT The strongest value for Prometheus and OpenTelemetry metrics: unusually simple single-node deployment, efficient storage, excellent ingest, PromQL-compatible MetricsQL, and a scalable clustered edition. It would rank first for metrics-only observability.
Grok Exceptional resource efficiency (often 7-10x less RAM/disk than Prometheus alternatives) with native high-cardinality handling,
Where it falls shortper GPT Its labeled-metrics model is too narrow for relational joins, arbitrary event analytics, or general application data.
per Gemini Strictly engineered for numerical metrics and telemetry; entirely lacks general-purpose SQL support, ad-hoc relational joins, and rich data typing beyond timestamps and floating-point values
- 5GPT #5Claude #3Gemini #5Grok —
Purpose-built for metrics with a mature ecosystem (Telegraf collectors, Flux/InfluxQL); the v3 rewrite on Apache Arrow/DataFusion/Parquet brings columnar performance, unlimited cardinality, and SQL, addressing the old cardinality wall; strong fit for IoT and app metrics.
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Claude Purpose-built for metrics with a mature ecosystem (Telegraf collectors, Flux/InfluxQL); the v3 rewrite on Apache Arrow/DataFusion/Parquet brings columnar performance, unlimited cardinality, and SQL, addressing the old cardinality wall; strong fit for IoT and app metrics.
GPT Strong object-storage-first architecture built on Arrow, DataFusion, and Parquet, with SQL and InfluxQL, excellent line-protocol and Telegraf integration, useful last-value caches, and multi-node Enterprise deployments. It can beat QuestDB for established Influx estates.
Gemini The complete rebuild on Apache Arrow, DataFusion, and Parquet resolved legacy high-cardinality bottlenecks while adding native SQL querying and decoupled object storage architectures
Where it falls shortper GPT The open-source Core edition is single-node and normally limits each query to roughly 72 hours of data, making Enterprise effectively necessary for serious production use.
per Claude The 1.x→2.x→3.x churn burned trust and fragmented tooling/migration paths; some v2 features (e.g. built-in tasks/UI) were dropped or changed in v3, so upgrades aren't seamless.
per Gemini Ecosystem fragmentation resulting from multiple legacy transition cycles (v1 to v2 to v3) and more constrained standalone open-source operational tooling compared to earlier generations
- 6GPT —Claude #4Gemini —Grok —
The de facto standard for cloud-native/Kubernetes metrics and alerting; pull model, PromQL, and service discovery make operational monitoring nearly turnkey, with an enormous exporter and integration ecosystem.
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Claude The de facto standard for cloud-native/Kubernetes metrics and alerting; pull model, PromQL, and service discovery make operational monitoring nearly turnkey, with an enormous exporter and integration ecosystem.
Where it falls shortper Claude Deliberately not built for long-term storage, high cardinality, or durable/highly-available scale — you need Thanos, Cortex, or Mimir for that, and it's poorly suited to event/high-precision non-metric data.
By use case
How this board's leaders rank when the same four models are asked a more specific question.
| Product | This board | databases for high-cardinality observability data | databases for industrial IoT telemetry |
|---|---|---|---|
| TimescaleDB | #1 | #5 | #3 |
| ClickHouse | #2 | #2 | #7 |
| QuestDB | #3 | #8 | #4 |
| VictoriaMetrics | #4 | #1 | — |
| InfluxDB 3 | #5 | #4 | — |
Rank history
Just missed the top 5
GPT Grafana Mimir — excellent multi-tenant Prometheus storage, but too specialized and operationally heavy for the general ranking · Apache Druid — formidable streaming OLAP, but its multi-service architecture and more constrained SQL make ClickHouse the better typical choice
Claude VictoriaMetrics — excellent cost-efficient, high-cardinality Prometheus-compatible metrics store — arguably belongs, edged out by narrower category focus and lower name-level familiarity for the typical practitioner · Amazon Timestream — fully managed and AWS-integrated, but lock-in, pricing surprises at scale, and uneven query performance keep it off the list
Gemini Prometheus — Unbeatable standard for Kubernetes scraping and immediate alerting, but intentionally lacks native long-term distributed storage and deep historical analytical capabilities · TDengine — Strong technical architecture for specialized Industrial IoT/SCADA edge-to-cloud use cases, but constrained by a smaller general-purpose ecosystem and niche query abstractions
By model
ChatGPT
- 1.TimescaleDB
- 2.ClickHouse
- 3.VictoriaMetrics
- 4.QuestDB
- 5.InfluxDB 3
Claude
- 1.TimescaleDB
- 2.ClickHouse
- 3.InfluxDB 3
- 4.Prometheus
- 5.QuestDB
Gemini
- 1.TimescaleDB
- 2.VictoriaMetrics
- 3.ClickHouse
- 4.QuestDB
- 5.InfluxDB 3
Grok
- 1.QuestDB
- 2.TimescaleDB
- 3.VictoriaMetrics
Common questions
What is the best time-series database according to AI models?
TimescaleDB leads. 3 of 4 models rank TimescaleDB the top pick. The current top 3: TimescaleDB, ClickHouse, QuestDB. Ranked by asking ChatGPT, Claude, Gemini, Grok the same buying question and merging their top-5 picks, updated 2026-08-14. Source: modelsagree.com.
Which time-series database did each AI model pick first?
ChatGPT: TimescaleDB. Claude: TimescaleDB. Gemini: TimescaleDB. Grok: QuestDB.
Do the AI models agree on the best time-series database?
Not unanimous. Grok picks QuestDB.
What changed in the latest time-series database ranking?
In the latest poll (2026-08-14): QuestDB climbed 2 spots; VictoriaMetrics dropped 1 spot, InfluxDB 3 dropped 1 spot; Prometheus entered the ranking. The models are re-polled on demand, so this ranking moves.
How is this time-series database ranking made?
ChatGPT, Claude, Gemini, Grok are each asked the same buying question in a fresh session with no system steering. Their top-5 answers are merged (rank 1 = 5 pts … rank 5 = 1 pt) into the consensus ranking, re-polled on demand and tracked over time.
More on how polling works: full methodology →
Cite this ranking
ModelsAgree, “Best time-series database” — merged ranking from ChatGPT, Claude, Gemini & Grok, polled 2026-08-14. https://modelsagree.com/best/best-time-series-database (CC BY 4.0)
Tracked by ModelsAgree · rank 1 = 5 pts … rank 5 = 1 pt · re-polled on demand