Decision comparison
TimescaleDB vs QuestDB
QuestDB and Tiger Cloud both address time-series workloads but expose different documented operating models. QuestDB combines SQL, a multi-tier storage engine, native Parquet support, PGwire, and REST APIs. Tiger Cloud is managed Postgres with hypertables, row-to-columnar conversion as data ages, and approximately 200 native SQL hyperfunctions. The appropriate choice depends on the required deployment model, storage approach, protocol and integration needs, availability controls, and commercial terms.
Direct comparison. These are reviewed substitutes bought for the same job, so the differences below are the ones that decide between them.
All 2 are time-series databases.
Quick Comparison
| Decision factor | TimescaleDB | QuestDB |
|---|---|---|
| Architecture | Managed Postgres platform with hypertables that partition tables by time or ID. | Open-source time-series database with a multi-tier storage engine and column-oriented, parallelized vector execution. |
| Ingestion Speed | Tiger Cloud documents row storage for fast writes and streaming into hypertables from Kafka, S3, and other Postgres databases. | QuestDB documents low-latency, high-throughput ingestion from single events to millions per second. |
| SQL Compatibility | Managed Postgres; Tiger Cloud documents approximately 200 native SQL hyperfunctions. | SQL support with PostgreSQL Wire Protocol (PGwire), REST API, and time-series extensions including ASOF JOIN and SAMPLE BY. |
| Storage Engine | Automatic conversion between rowstore and columnstore as data ages; Tiger Data separately lists tiered storage at $0.021/GB-month. | WAL, native time-partitioned columnar storage, and Apache Parquet on object storage. |
| Pricing Model | Self-hosted open-source TimescaleDB is free. Tiger Cloud bills hourly for compute and storage used. Performance plan: compute from $30 per month, storage $0.177 per GB-month. Scale plan: compute from $36 per month, storage $0.212 per GB-month. Storage prices are effective prices based on 5x compression. Tiered storage: $0.021 per GB-month. Enterprise: contact sales. New users get a free 30-day trial of the Performance plan, no credit card required. Verified 2026-10-05 against https://www.tigerdata.com/pricing. | Self-hosted free under Apache-2.0 license. Enterprise features available (contact for pricing details). |
| Best For | Workloads requiring managed Postgres with time/id-partitioned hypertables and time-series SQL functions. | Time-series workloads needing SQL, native Parquet support, and a WAL-to-columnar-to-Parquet storage design. |
TimescaleDB
- Architecture:
- Managed Postgres platform with hypertables that partition tables by time or ID.
- Ingestion Speed:
- Tiger Cloud documents row storage for fast writes and streaming into hypertables from Kafka, S3, and other Postgres databases.
- SQL Compatibility:
- Managed Postgres; Tiger Cloud documents approximately 200 native SQL hyperfunctions.
- Storage Engine:
- Automatic conversion between rowstore and columnstore as data ages; Tiger Data separately lists tiered storage at $0.021/GB-month.
- Pricing Model:
- Self-hosted open-source TimescaleDB is free. Tiger Cloud bills hourly for compute and storage used. Performance plan: compute from $30 per month, storage $0.177 per GB-month. Scale plan: compute from $36 per month, storage $0.212 per GB-month. Storage prices are effective prices based on 5x compression. Tiered storage: $0.021 per GB-month. Enterprise: contact sales. New users get a free 30-day trial of the Performance plan, no credit card required. Verified 2026-10-05 against https://www.tigerdata.com/pricing.
- Best For:
- Workloads requiring managed Postgres with time/id-partitioned hypertables and time-series SQL functions.
QuestDB
- Architecture:
- Open-source time-series database with a multi-tier storage engine and column-oriented, parallelized vector execution.
- Ingestion Speed:
- QuestDB documents low-latency, high-throughput ingestion from single events to millions per second.
- SQL Compatibility:
- SQL support with PostgreSQL Wire Protocol (PGwire), REST API, and time-series extensions including ASOF JOIN and SAMPLE BY.
- Storage Engine:
- WAL, native time-partitioned columnar storage, and Apache Parquet on object storage.
- Pricing Model:
- Self-hosted free under Apache-2.0 license. Enterprise features available (contact for pricing details).
- Best For:
- Time-series workloads needing SQL, native Parquet support, and a WAL-to-columnar-to-Parquet storage design.
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | TimescaleDB | QuestDB |
|---|---|---|
| Docker Hub pulls(Product adoption) | 41.3M | 3.1M |
| GitHub commits, 90d(Product adoption) | 279 | 155 |
| GitHub stars(Product adoption) | 23,000+ | 17,000+ |
| Search interest(Market interest) | 1 | 0 |
| Hacker News mentions, 90d(Community interest) | 1 | 3 |
| npm weekly downloads(Ecosystem adoption) | 95.7M | Not available |
| Product Hunt comments(Community interest) | 1 | 28 |
| Product Hunt rating(Community interest) | 5.0/5 | 5.0/5 |
| Product Hunt reviews(Community interest) | 5 | 4 |
| Product Hunt votes(Community interest) | 9 | 195 |
| PyPI weekly downloads(Ecosystem adoption) | 694 | Not available |
| Stack Overflow questions(Community interest) | 775 | 282 |
| npm weekly downloads(Developer adoption) | Not available | 18.3k |
| PyPI weekly downloads(Developer adoption) | Not available | 31.5k |
As of November 9, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
TimescaleDB
November 9, 2026Package vulnerabilities
npm · pg@8.23.1 · PyPI · timescaledb@0.2.1
0 vulnerabilities
across 2 packages
Repository security score
Not available
QuestDB
November 9, 2026Package vulnerabilities
npm · @questdb/nodejs-client@5.0.0 · PyPI · questdb@6.0.1
0 vulnerabilities
across 2 packages
Repository security score
github.com/questdb/questdb
4.4/10
Interface Preview
TimescaleDB
QuestDB
Feature Comparison
| Feature | TimescaleDB | QuestDB |
|---|---|---|
| Ingestion & Write Performance | ||
| Write-Ahead Logging | Not verified in the cited Tiger Cloud material. | WAL is the first tier; the product page says incoming writes are made durable before processing and the WAL is asynchronously shipped to object storage. |
| Data Partitioning | Hypertables automatically partition Postgres tables by time or ID, with partition skipping at query planning time. | Native data is described as time-ordered, de-duplicated, time-partitioned columnar data. |
| Bulk Ingestion | Tiger Cloud documents streaming from Kafka, S3, and other Postgres databases into hypertables. | The repository describes ingestion from single events to millions per second and supports PGwire and REST API access. |
| Query Engine & Analytics | ||
| Time-Series SQL Extensions | Tiger Cloud documents approximately 200 native SQL hyperfunctions, including statistical rollups, time-weighted averages, approximations, and interpolation. | ASOF JOIN, WINDOW JOIN, HORIZON JOIN, SAMPLE BY, and LATEST ON are documented time-series SQL extensions. |
| Materialized Views | Not verified in the cited Tiger Cloud material. | Views and materialized views are listed in the QuestDB repository. |
| Advanced Data Structures | Not verified in the cited Tiger Cloud material. | The repository lists n-dimensional arrays, including 2D arrays for order books. |
| Storage & Compression | ||
| Compression | Tiger Cloud documents columnar storage for analytical scans and compression-ratio and storage-savings information. | QuestDB documents native columnar storage and Apache Parquet for a cold tier. |
| Tiered Storage | Tiger Data lists tiered storage at $0.021 per GB-month on its pricing page. Separately, Tiger Cloud documents automatic conversion between rowstore and columnstore as data ages; this is not described there as an object-storage tier. | A multi-tier design is documented as WAL, native time-partitioned columnar storage, and Apache Parquet on object storage; Enterprise also documents auto-tiering from hot data to cold Parquet/object storage. |
| Open Formats | Not verified in the cited Tiger Cloud material. | Native Parquet support is documented; the Enterprise page also lists Parquet and Iceberg as open formats. |
| Integration & Protocol Support | ||
| SQL Protocol | Tiger Cloud is managed Postgres. | PGwire and a REST API are documented. |
| Ecosystem Connectors | Tiger Cloud documents streaming from Kafka, S3, and other Postgres databases. | Listed integrations include Kafka, Redpanda, Grafana, Polars, Pandas, PowerBI, Superset, Apache Flink, Telegraf, and MindsDB. |
| Cloud Provider Support | Tiger Cloud is a managed cloud platform; the cited material does not enumerate cloud providers. | Enterprise is documented for cloud, on-premises, hybrid, or BYOC deployment. |
| Enterprise & Operations | ||
| High Availability | Tiger Cloud’s pricing page lists high-availability replicas with automated multi-AZ failover and up to one HA replica for the shown plan. | Enterprise documents replication, auto failover, and Multi-AZ availability; one Enterprise page states automatic failover to standby replicas in under 100 ms. |
| Security & Compliance | Tiger Cloud’s pricing page lists end-to-end encryption, private networking, multi-factor authentication, federated authentication (SAML), SOC 2 Type 2, and HIPAA compliance across its plan comparison. | Enterprise documents TLS, OAuth 2.0/OIDC SSO, RBAC, and audit logs. |
| Scalability Architecture | Tiger Cloud documents independent compute and storage scaling. The pricing page lists up to 64 CPU, 256 GB memory, and 64 TB disk storage per Enterprise service. | Enterprise documents unified SQL across hot data and Parquet/object-storage cold data, with the planner spanning partitions and Parquet. |
Ingestion & Write Performance
Write-Ahead Logging
Data Partitioning
Bulk Ingestion
Query Engine & Analytics
Time-Series SQL Extensions
Materialized Views
Advanced Data Structures
Storage & Compression
Compression
Tiered Storage
Open Formats
Integration & Protocol Support
SQL Protocol
Ecosystem Connectors
Cloud Provider Support
Enterprise & Operations
High Availability
Security & Compliance
Scalability Architecture
Which to choose
QuestDB and Tiger Cloud both address time-series workloads but expose different documented operating models. QuestDB combines SQL, a multi-tier storage engine, native Parquet support, PGwire, and REST APIs. Tiger Cloud is managed Postgres with hypertables, row-to-columnar conversion as data ages, and approximately 200 native SQL hyperfunctions. The appropriate choice depends on the required deployment model, storage approach, protocol and integration needs, availability controls, and commercial terms.
Best-fit scenarios
Choose TimescaleDB if:
Consider Tiger Cloud when a managed Postgres service is required and its documented hypertables, time/id partitioning, automatic rowstore-to-columnstore conversion, and native SQL hyperfunctions fit the workload.
Choose QuestDB if:
Consider QuestDB when SQL time-series processing, PostgreSQL Wire Protocol or REST API access, native Parquet support, and a WAL-to-native-columnar-to-Parquet storage design fit the workload.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What is the main difference between TimescaleDB and QuestDB?
QuestDB is an open-source time-series database with SQL support, a multi-tier storage engine, and native Parquet support. Its repository describes a column-oriented model, parallelized vector execution, SIMD instructions, PostgreSQL wire protocol, and a REST API. Tiger Cloud is a managed Postgres platform from the creators of TimescaleDB. Its documentation describes hypertables that partition Postgres tables by time or ID, automatic conversion between row and columnar storage as data ages, and approximately 200 native SQL hyperfunctions for time-series analysis.
Which database has better ingestion performance?
The available product pages describe QuestDB as supporting low-latency, high-throughput ingestion and state that its repository supports ingestion from single events to millions per second. Tiger Cloud documents hypertable partitioning, streaming into hypertables from Kafka, S3, and other Postgres databases, and row storage intended to keep writes fast. They do not establish a like-for-like benchmark or a common workload, hardware configuration, or ingestion protocol, so they do not support a definitive performance ranking.
Can I use standard PostgreSQL tools with both databases?
QuestDB documents PostgreSQL Wire Protocol (PGwire) for programmatic queries and lists a REST API. Its repository also lists integrations including Kafka, Redpanda, Grafana, Polars, Pandas, PowerBI, Superset, Apache Flink, Telegraf, and MindsDB. Tiger Cloud is managed Postgres, and its documentation describes streaming from Kafka, S3, and other Postgres databases. The available material does not document full equivalence of every PostgreSQL extension, ORM, driver, or administration tool across the two products.
How do TimescaleDB and QuestDB compare on pricing?
Tiger Data lists Tiger Cloud Performance compute starting at $30 per month and storage at $0.177 per GB-month. Its Scale plan lists compute starting at $36 per month and storage at $0.212 per GB-month. Both are described as pay-as-you-go hourly plans; the page says compute is metered hourly and storage is based on average GB consumption per hour. Tiger Data also lists Enterprise as “Contact us,” without publishing an Enterprise amount. QuestDB’s Enterprise page offers an Enterprise trial, while the cited Enterprise material does not publish an amount, currency, billing period, license metric, or plan price. A buyer evaluating QuestDB Enterprise should confirm those commercial terms and which Enterprise capabilities are included; the page identifies high availability, auto failover, SSO/RBAC, audit logs, tiered storage, and SLA-backed creator support.
Which database is better for capital markets and financial data?
QuestDB’s materials explicitly describe financial market data, including tick data, trades, order books, and OHLC, as a use case. The Enterprise page says B3’s Central Securities Depository platform runs on QuestDB and reports $5B+ daily traded volume and a 99.9% uptime guarantee for that case study. QuestDB also documents time-series SQL extensions including ASOF JOIN, WINDOW JOIN, HORIZON JOIN, SAMPLE BY, and LATEST ON, plus n-dimensional arrays including 2D arrays for order books. Tiger Data’s cited materials do not provide an equivalent, directly comparable financial-use-case description, so the available information does not support a general “better” determination.