List & Promote Your Business to the Right Audience Starting at $100

    Find Alternatives
    IT Infrastructure Software

    Best Time Series Databases in 2026

    14 tools highlightedUpdated September 2026

    Top Time Series Databases Tools for 2026

    Compare leading time series databases platforms by pricing, strengths, trade-offs, and best-fit teams.

    #1

    1. InfluxDB

    The open-source time series database for metrics, events, and analytics.

    4.6

    InfluxDB is an open-source time series database developed by InfluxData. It is optimized for fast, high-availability storage and retrieval of time series data in fields such as operations monitoring, application metrics, IoT sensor data, and real-time analytics. It is part of the InfluxData TICK stack, which includes Telegraf, InfluxDB, Chronograf, and Kapacitor.

    Open-source (free), Cloud (various tiers), Enterprise (custom pricing)
    Best for: Developers and organizations needing high-performance time series data management

    Pros

    • High performance for time-series data
    • Integrated data collection and visualization
    • Flexible data model with tags

    Cons

    • Can have a learning curve
    • Scalability can be complex in self-hosted versions
    Visit InfluxDB
    #2

    2. Prometheus

    An open-source monitoring system with a flexible query language.

    4.5

    Prometheus is a free software application used for event monitoring and alerting. It records real-time metrics in a time series database built using an HTTP pull model, with flexible queries and real-time alerting. It is a standalone open-source project and maintains an independent service alongside Kubernetes.

    Open-source (free)
    Best for: Monitoring and alerting for cloud-native environments

    Pros

    • Powerful multi-dimensional data model
    • PromQL query language is very flexible
    • Large and active community

    Cons

    • Lacks long-term storage out-of-the-box
    • Not a full-fledged database for all use cases
    Visit Prometheus
    #3

    3. TimescaleDB

    SQL for time-series data at scale.

    4.7

    TimescaleDB is an open-source relational database for time-series data, built on PostgreSQL. It brings the power and familiarity of SQL to time-series workloads, excelling in high-ingest and complex query scenarios. It offers automatic partitioning and analytical functions, making it suitable for IoT, financial data, and monitoring applications.

    Open-source (free), Timescale Cloud (various tiers), Enterprise (custom pricing)
    Best for: Users comfortable with SQL needing time-series capabilities

    Pros

    • Full SQL support
    • Scales PostgreSQL for time series
    • Excellent for complex analytical queries

    Cons

    • Dependent on PostgreSQL ecosystem
    • Can require more manual tuning than some alternatives
    Visit TimescaleDB
    #4

    4. Graphite

    Scalable graphing and monitoring system.

    4.3

    Graphite is a highly scalable real-time graphing system. It consists of a storage backend (whisper), a data feeder (carbon), and a web application that renders graphs (graphite-web). It's widely used for collecting, storing, and visualizing time-series data from various sources like servers, applications, and network devices.

    Open-source (free)
    Best for: Monitoring system metrics and simple data visualization

    Pros

    • Simple and easy to set up
    • Flexible data aggregation
    • Good for basic monitoring and trending

    Cons

    • Limited query language compared to others
    • Can be resource-intensive for large datasets
    Visit Graphite
    #5

    5. Druid

    A high-performance analytics data store for event-driven data.

    4.4

    Apache Druid is a real-time analytics database designed for fast slice-and-dice analytics ("OLAP" queries) on large datasets. It's often used for powering user-facing analytic applications and for ingesting high volumes of event data. Druid is highly scalable and fault-tolerant, making it suitable for many big data use cases.

    Open-source (free)
    Best for: Real-time analytics on large event datasets

    Pros

    • Real-time ingestion and query performance
    • Excellent for interactive analytics
    • Scalable and fault-tolerant architecture

    Cons

    • Complex to set up and manage
    • Not designed for transactional workloads
    Visit Druid
    #6

    6. OpenTSDB

    Scalable, distributed time series database on HBase.

    4.2

    OpenTSDB is a scalable, distributed time series database written in Java. It stores and serves massive amounts of time-series data and is built on top of HBase. It allows you to collect, store, and serve metrics from any source at a large scale, making it suitable for infrastructure monitoring and application performance management.

    Open-source (free)
    Best for: Large-scale infrastructure and application metrics

    Pros

    • Highly scalable and fault-tolerant
    • Leverages HBase's distributed capabilities
    • Good for large-scale infrastructure monitoring

    Cons

    • Requires HBase, increasing complexity
    • Query language can be limited
    Visit OpenTSDB
    #7

    7. CrateDB

    Distributed SQL database for real-time analytics and IoT.

    4.1

    CrateDB is an open-source, distributed SQL database that combines the familiarity of SQL with the scalability and flexibility of NoSQL. It's designed for real-time analytics on machine and IoT data, offering high-performance ingest and complex query capabilities. It leverages a shared-nothing architecture for horizontal scalability.

    Open-source (free), CrateDB Cloud (various tiers), Enterprise (custom pricing)
    Best for: IoT and machine data analytics with SQL

    Pros

    • SQL interface for time-series data
    • Real-time analytics capabilities
    • Scales horizontally for large datasets

    Cons

    • Less mature than some alternatives
    • Can have a steeper learning curve for new users
    Visit CrateDB
    #8

    8. TDengine

    Big data platform for IoT, Connected Cars, and Industrial IoT.

    4

    TDengine is an open-source, high-performance time-series database. It is optimized for IoT, connected cars, industrial IoT, and IT infrastructure monitoring. It provides data ingestion, storage, query, and computation, all in one platform, designed to efficiently handle massive volumes of time-series data with high concurrency.

    Open-source (free), Cloud (various tiers), Enterprise (custom pricing)
    Best for: IoT and Industrial IoT data management

    Pros

    • High performance for IoT scenarios
    • Built-in caching and data aggregation
    • Integrated platform for data management

    Cons

    • Newer entrant, community still growing
    • Fewer integrations compared to market leaders
    Visit TDengine
    #9

    9. Aqua Data Studio

    Universal IDE for databases.

    4.5

    Aqua Data Studio is a universal integrated development environment (IDE) for databases. While not a time-series database itself, it is a crucial tool for developers and DBAs working with various database types, including many that store time-series data. It provides tools for data management, query construction, and visual analytics.

    Commercial (perpetual license)
    Best for: Developers and DBAs managing diverse database environments

    Pros

    • Connects to numerous database types
    • Rich set of administration tools
    • SQL query builder and visual analytics

    Cons

    • Not a database, but a client tool
    • Annual subscription for updates
    Visit Aqua Data Studio
    #10

    10. QuestDB

    The fastest open-source database for time series data.

    4.7

    QuestDB is an open-source, high-performance time series database designed for financial market data, IoT, and operational analytics. It offers a SQL interface, high-throughput ingestion, and fast queries, making it ideal for real-time applications and large datasets. Its columnar storage and parallel processing ensure efficient data handling and analysis.

    Open-source (free), Enterprise Edition with additional features and support.
    Best for: Real-time analytics, financial market data, IoT data processing, operational monitoring.

    Pros

    • Extremely fast data ingestion and query performance.
    • SQL-based for ease of use and integration.
    • Columnar storage and vectorized execution for efficiency.

    Cons

    • Newer compared to established solutions, smaller community.
    • Limited ecosystem integrations compared to more mature databases.
    Visit QuestDB
    #11

    11. KairnDB

    Scalable time series database for real-time analytics.

    4.5

    KairnDB is a high-performance, scalable time series database built for real-time data ingestion and analytics. It excels at handling massive volumes of time-stamped data from IoT devices, sensors, and financial applications. With its distributed architecture, KairnDB ensures high availability and fault tolerance, making it suitable for mission-critical deployments.

    Contact for pricing (enterprise-grade solution).
    Best for: Large-scale IoT deployments, real-time financial analytics, industrial sensor data.

    Pros

    • Exceptional scalability and performance for large datasets.
    • Built-in support for real-time analytics and complex queries.
    • Distributed architecture ensures high availability and resilience.

    Cons

    • Not open-source, so less community support.
    • Potentially higher cost for smaller deployments.
    Visit KairnDB
    #12

    12. InfluxDB Clustered

    High-performance time series platform for any scale.

    4.6

    InfluxDB Clustered is a cloud-native, distributed time series platform designed for handling massive data volumes and high-cardinality data. It provides horizontal scalability, high availability, and enhanced security features, making it suitable for enterprise-grade applications. It offers powerful querying and visualization capabilities to extract insights from your time series data.

    Consumption-based pricing on cloud platforms (e.g., AWS, Azure).
    Best for: Enterprise-grade IoT solutions, real-time observability, financial trading platforms.

    Pros

    • Cloud-native and highly scalable for large enterprises.
    • Robust security features and high availability.
    • Powerful data exploration and visualization tools.

    Cons

    • Can be more complex to manage compared to single-instance InfluxDB.
    • Pricing can increase with high data ingestion and query loads.
    Visit InfluxDB Clustered
    #13

    13. TimeScale

    Cloud-native PostgreSQL for time series and analytics.

    4.8

    TimeScale is a cloud-native, fully managed time series database built on PostgreSQL. It combines the power and flexibility of a relational database with optimizations for time series workloads, delivering high performance and scalability. With its familiar SQL interface, developers can quickly build and deploy time-series applications without learning new database languages.

    Consumption-based pricing on cloud platforms (e.g., AWS, Azure, GCP).
    Best for: Developers familiar with PostgreSQL, time series analytics, IoT applications.

    Pros

    • Familiar SQL interface (PostgreSQL compatible).
    • Excellent performance for time series workloads.
    • Fully managed, reducing operational overhead.

    Cons

    • Can be more expensive than self-hosted PostgreSQL solutions.
    • Limited to PostgreSQL ecosystem tools and extensions.
    Visit TimeScale
    #14

    14. ClickHouse

    Real-time analytical data warehouse for big data.

    4.9

    ClickHouse is an open-source, columnar database management system for online analytical processing (OLAP). While not exclusively a time series database, its unparalleled speed for large-scale analytical queries makes it a popular choice for time series data. It's renowned for its ability to process billions of rows per second per server.

    Open-source (free), cloud services and enterprise support available.
    Best for: Real-time analytics, web analytics, IoT data analysis, operational intelligence.

    Pros

    • Extremely fast analytical query performance.
    • Highly scalable and efficient for large datasets.
    • Open-source with a strong and active community.

    Cons

    • Not optimized for transactional (OLTP) workloads.
    • Steeper learning curve compared to traditional databases.
    Visit ClickHouse
    Buyer's Guide

    Time Series Databases Buyer's Guide for 2026

    Everything you need to know before choosing a time series databases solution — features, pricing, evaluation criteria, and answers to common questions.

    01

    How we compare Time Series Databases for US teams

    This page tracks 14 time series databases platforms that are actively sold and supported in the United States. Each listing is reviewed for US availability, English-language support during North American business hours, and pricing published in US dollars, so a buyer in New York or San Francisco can shortlist without chasing regional resellers.

    The strongest current options are InfluxDB, Prometheus, and TimescaleDB. We look at what each product actually does day to day, where it fits in a US tech stack, and who it is genuinely a good fit for — rather than ranking purely on marketing spend.

    Across the shortlist, the capabilities buyers cite most often are High performance for time-series data, Integrated data collection and visualization, and Powerful multi-dimensional data model. Use those as the baseline: if a vendor cannot match them, it usually needs a very specific reason to stay on your list.

    02

    Time Series Databases pricing in the US

    Published pricing across these time series databases tools falls into 4 broad shapes: Open-source (free), Cloud (various tiers), Enterprise (custom pricing), Open-source (free), Open-source (free), Timescale Cloud (various tiers), Enterprise (custom pricing), and Open-source (free), CrateDB Cloud (various tiers), Enterprise (custom pricing). US list prices are normally quoted per user per month in USD, billed annually, with a discount of roughly 10–20% for the annual commitment.

    At least one option here has a free or freemium tier, which is the cheapest way to validate the workflow before you involve procurement. Free tiers usually cap seats, history, or integrations — confirm those limits before you build a process on top of them.

    Several vendors list quote-only enterprise pricing. Ask for the total first-year cost including implementation, data migration, sandbox environments, and premium support — those line items are where US enterprise deals typically grow 30–50% beyond the seat price.

    Also budget for the non-obvious costs: SSO/SAML is often gated behind a higher tier, API rate limits can force an upgrade, and multi-year contracts frequently include automatic uplift clauses. Sales tax treatment for SaaS varies by state, so confirm whether quotes are tax-inclusive.

    03

    Security, compliance and procurement checks

    For US buyers, security review is usually the step that decides the deal. Before you sign for time series databases, ask each vendor for a current SOC 2 Type II report, their sub-processor list, and their data residency options — many teams require that data stays in US regions.

    Layer on the regulations that apply to you: HIPAA and a signed BAA for anything touching patient data, CCPA/CPRA obligations for California consumer data, FERPA in education, GLBA in financial services, and FedRAMP or StateRAMP authorization if you sell to public sector. If you have EU users too, check the vendor's Data Privacy Framework certification.

    Practical checklist: SSO and SCIM provisioning, role-based access control, audit logs exportable to your SIEM, documented breach-notification timelines, and a data-deletion path you can actually execute at the end of the contract.

    04

    Which time series databases option fits your team

    The tools on this page are built for different buyers — Developers and organizations needing high-performance time series data management, Monitoring and alerting for cloud-native environments, Users comfortable with SQL needing time-series capabilities, and Monitoring system metrics and simple data visualization. Match the tool to your stage rather than to the longest feature list.

    Startups and small US teams (1–50 employees): prioritize fast self-serve setup, month-to-month billing, and a free or low-cost tier. You want something running this week, not a three-month rollout.

    Mid-market (50–1,000 employees): the deciding factors are usually SSO, granular permissions, an open API, and integrations with the rest of your stack. Expect a security questionnaire and a 4–8 week evaluation.

    Enterprise (1,000+): weight the contract, not the demo — uptime SLA with credits, named support with US-hours coverage, sandbox environments, migration assistance, and a clear roadmap commitment.

    A practical shortlist method: pick two options from this list — typically InfluxDB and Prometheus — run the same real workflow through both for two weeks, and score them on setup time, support responsiveness, and how much manual work is left over.

    FAQ

    Time Series Databases — Frequently Asked Questions

    Quick answers to the most common questions about choosing time series databases in 2026.

    Related IT Infrastructure Software Categories

    Explore other it infrastructure software categories closely connected to Time Series Databases.

    Vector Database Software

    18 tools

    Structured Data Archiving and Application Retirement Software

    18 tools

    Real-time Analytic Database Software

    18 tools

    Desktop Database Software

    18 tools

    Database Comparison Software

    18 tools

    Big Data Software

    16 tools

    Relational Databases

    15 tools

    Database Management Systems (DBMS)

    15 tools

    ← Browse all software categories↑ Back to Database SoftwareCompare popular software alternativesHire vetted developers
    Need expert help? Chat with us