Alternatives to KairosDB

Compare KairosDB alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to KairosDB in 2026. Compare features, ratings, user reviews, pricing, and more from KairosDB competitors and alternatives in order to make an informed decision for your business.

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    Co:tunity

    Co:tunity

    Kairos Future

    Co:tunity by Kairos Future is a cloud-based idea management and foresight platform that helps improve enterprise innovation capabilities. Designed for all types of organizations Co:tunity offers collaboration, idea submission, review, and analytics functionalities within a powerful suite. This collaborative trendspotting and innovation platform also includes a variety of flexible and customizable features that enable users to gather, share, develop, and/or evaluate ideas and trends together with colleagues and customers. Important features of Co:tunity include competitive intelligence, role-based permissions, real-time notifications, best practices repository, trend analysis, campaign analysis, communication management, and more.
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    Kairos AI

    Kairos AI

    Meet Kairos

    Fueled by your objective, Kairos AI will facilitate a meaningful, high-impact agenda, recommend who should attend, and when to meet.  Cutting-edge workforce intelligence platform designed to help organizations make smarter, data-driven decisions about their people and operations. Powered by AI and advanced analytics, Kairos provides deep insights into workforce dynamics, enabling businesses to optimize talent allocation, improve productivity, and drive organizational success. Key Features & Benefits Meeting management: Ensure the right people are in the right roles at the right time, improving efficiency and reducing operational costs. Craft effective agendas: Backed by decades of meeting research, Kairos AI recommends well-structured and relevant agendas, every time. AI-driven workforce insights: Unlock real-time visibility into workforce trends, employee performance, and skills gaps to enhance decision-making. Discover the future of workforce intelligence with Kairos. 🚀
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    Kairos

    Kairos

    Kairos

    Integrate Face Recognition via our cloud API, or host Kairos on your own servers for ultimate control of data, security, and privacy, start creating safer, more accessible customer experiences today. The leading face recognition AI company with an ethical approach to identity, that reflects our globally diverse communities. Through computer vision and deep learning, we recognize faces in videos, photos, and the real-world, our innovative API platform simplifies how developers and businesses integrate human identity into their software. Kairos provides state-of-the-art, ethical face recognition to developers and businesses worldwide. Kairos enables developers and businesses to easily build face recognition into their software products, integrate with our API today. Find human faces in photos and images. Detects age groups, child, young-adult, adult, or senior, and detects gender of each face found, female or male.
    Starting Price: $19 per month
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    Kairos Software

    Kairos Software

    Kairos Software Solutions

    Kairos software is designed for medium and large manufacturing and service organizations working project based. A smart and easy way to attribute client time and manage project performance. Time in Real-Time Advanced, intuitive and fast tracking and reporting systems, for real-time evaluation. Future Ready, Mobile, collaborative & cloud based for maximum fruibility and extension of self. Project Intelligence. Highly configurable built-in project logics for fast implementation, ease of maintenance and valuable analysis. Attendance Management. Managing attendance or presence at work. Set work shifts, manage clocking in and out via mobile geolocation, prepare payroll data through timesheets or use all three of them. Kairos People. Activate Kairos's employee app for fast and easy time, attendance, expense and leave management. Give your employees access to the app's Outlook and Google integrated agenda and smoothly communicate with the entire workforce.
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    Kairos Terminal

    Kairos Terminal

    Kairos Terminal

    Kairos Terminal is a platform offering data-driven insights and tools for crypto trading. It serves as an intelligence layer between on-chain and off-chain data, helping users analyze blockchain activity and social sentiment. The platform features real-time sentiment analytics, cross-chain liquidity, and gas-free trading, aimed at improving crypto decision-making. Users can track blockchain wallets, uncover profitable trading strategies, and analyze trends before they emerge, all from a unified dashboard. We source and process vast amounts of data directly from the blockchain and social platforms, ensuring you get the most accurate and actionable insights. The current on-chain trading experience is complex and fragmented, Kairos Terminal is the world's first gasless trading terminal that seamlessly integrates real-time sentiment analysis and instant cross-chain execution. It provides a seamless, efficient, and secure trading experience with real-time threat analysis.
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    Hawkular Metrics

    Hawkular Metrics

    Hawkular Metrics

    Hawkular Metrics is a scalable, asynchronous, multi tenant, long term metrics storage engine that uses Cassandra as the data store and REST as the primary interface. This section provides an overview of some of the key features of Hawkular Metrics. The following sections provide in-depth discussions on these as well as other features. Hawkular Metrics is all about scalability. You can run a single instance backed by a single Cassandra node. You can also scale out Cassandra to multiple nodes to handle increasing loads. The Hawkular Metrics server employs a stateless architecture, which makes it easy to scale out as well. This diagram illustrates the various deployment options made possible with Hawkular Metrics' scalable architecture. The upper left shows the simplest deployment with a single Cassandra node and single Hawkular Metrics node. The bottom right picture shows that it is possible to run more Hawkular Metrics nodes than Cassandra nodes.
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    VictoriaMetrics

    VictoriaMetrics

    VictoriaMetrics

    VictoriaMetrics is a fast and scalable open source time series database and monitoring solution. It's designed to be user-friendly, allowing users to build a monitoring platform without scalability issues and with minimal operational burden. VictoriaMetrics is ideal for solving use cases with large amounts of time series data for IT infrastructure, APM, Kubernetes, IoT sensors, automotive vehicles, industrial telemetry, financial data, and other enterprise-level workloads. VictoriaMetrics is powered by several components, making it the perfect solution for collecting metrics (both push and pull models), running queries, and generating alerts. With VictoriaMetrics, you can store millions of data points per second on a single instance or scale to a high-load monitoring system across multiple data centers. Plus, it's designed to store 10x more data using the same compute and storage resources as existing solutions, making it a highly efficient choice.
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    Circonus IRONdb
    Circonus IRONdb makes it easy to handle and store unlimited volumes of telemetry data, easily handling billions of metric streams. Circonus IRONdb enables users to identify areas of opportunity and challenge in real time, providing forensic, predictive, and automated analytics capabilities that no other product can match. Rely on machine learning to automatically set a “new normal” as your data and operations dynamically change. Circonus IRONdb integrates with Grafana, which has native support for our analytics query language. We are also compatible with other visualization apps, such as Graphite-web. Circonus IRONdb keeps your data safe by storing multiple copies of your data in a cluster of IRONdb nodes. System administrators typically manage clustering, often spending significant time maintaining it and keeping it working. Circonus IRONdb allows operators to set and forget their cluster, and stop wasting resources manually managing their time series data store.
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    Blueflood

    Blueflood

    Blueflood

    Blueflood is a high throughput, low latency, multi-tenant distributed metric processing system behind Rackspace Metrics, which is currently used in production by the Rackspace Monitoring team and Rackspace public cloud team to store metrics generated by their systems. In addition to Rackspace metrics, other large scale deployments of Blueflood can be found at community Wiki. Data from Blueflood can be used to construct dashboards, generate reports, graphs or for any other use involving time-series data. It focuses on near-realtime data, with data that is queryable mere milliseconds after ingestion. You send metrics to the ingestion service. You query your metrics from the Query service. And in the background, rollups are batch-processed offline so that queries for large time-periods are returned quickly.
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    Prometheus

    Prometheus

    Prometheus

    Power your metrics and alerting with a leading open-source monitoring solution. Prometheus fundamentally stores all data as time series: streams of timestamped values belonging to the same metric and the same set of labeled dimensions. Besides stored time series, Prometheus may generate temporary derived time series as the result of queries. Prometheus provides a functional query language called PromQL (Prometheus Query Language) that lets the user select and aggregate time series data in real time. The result of an expression can either be shown as a graph, viewed as tabular data in Prometheus's expression browser, or consumed by external systems via the HTTP API. Prometheus is configured via command-line flags and a configuration file. While the command-line flags configure immutable system parameters (such as storage locations, amount of data to keep on disk and in memory, etc.). Download: https://sourceforge.net/projects/prometheus.mirror/
    Starting Price: Free
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    Riak TS
    Riak® TS is the only enterprise-grade NoSQL time series database optimized specifically for IoT and Time Series data. It ingests, transforms, stores, and analyzes massive amounts of time series data. Riak TS is engineered to be faster than Cassandra. The Riak TS masterless architecture is designed to read and write data even in the event of hardware failures or network partitions. Data is evenly distributed across the Riak ring and, by default, there are three replicas of your data. This ensures at least one copy of your data is available for read operations. Riak TS is a distributed system with no central coordinator. It is easy to set up and operate. The masterless architecture makes it easy to add and remove nodes from a cluster. The masterless architecture of Riak TS makes it easy to add and remove nodes from your cluster. You can achieve predictable and near-linear scale by adding nodes using commodity hardware.
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    QuestDB

    QuestDB

    QuestDB

    QuestDB is a relational column-oriented database designed for time series and event data. It uses SQL with extensions for time series to assist with real-time analytics. These pages cover core concepts of QuestDB, including setup steps, usage guides, and reference documentation for syntax, APIs and configuration. This section describes the architecture of QuestDB, how it stores and queries data, and introduces features and capabilities unique to the system. Designated timestamp is a core feature that enables time-oriented language capabilities and partitioning. Symbol type makes storing and retrieving repetitive strings efficient. Storage model describes how QuestDB stores records and partitions within tables. Indexes can be used for faster read access on specific columns. Partitions can be used for significant performance benefits on calculations and queries. SQL extensions allow performant time series analysis with a concise syntax.
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    CrateDB

    CrateDB

    CrateDB

    The enterprise database for time series, documents, and vectors. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data.
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    OpenTSDB

    OpenTSDB

    OpenTSDB

    OpenTSDB consists of a Time Series Daemon (TSD) as well as set of command line utilities. Interaction with OpenTSDB is primarily achieved by running one or more of the independent TSDs. There is no master, no shared state so you can run as many TSDs as required to handle any load you throw at it. Each TSD uses the open source database HBase or hosted Google Bigtable service to store and retrieve time-series data. The data schema is highly optimized for fast aggregations of similar time series to minimize storage space. Users of the TSD never need to access the underlying store directly. You can communicate with the TSD via a simple telnet-style protocol, an HTTP API or a simple built-in GUI. The first step in using OpenTSDB is to send time series data to the TSDs. A number of tools exist to pull data from various sources into OpenTSDB.
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    Apache Druid
    Apache Druid is an open source distributed data store. Druid’s core design combines ideas from data warehouses, timeseries databases, and search systems to create a high performance real-time analytics database for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture. Druid stores and compresses each column individually, and only needs to read the ones needed for a particular query, which supports fast scans, rankings, and groupBys. Druid creates inverted indexes for string values for fast search and filter. Out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures.
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    Alibaba Cloud TSDB
    Time Series Database (TSDB) supports high-speed data reading and writing. It offers high compression ratios for cost-efficient data storage. This service also supports visualization of precision reduction, interpolation, multi-metric aggregate computing, and query results. The TSDB service reduces storage costs and improves the efficiency of data writing, query, and analysis. This enables you to handle large amounts of data points and collect data more frequently. This service has been widely applied to systems in different industries, such as IoT monitoring systems, enterprise energy management systems (EMSs), production security monitoring systems, and power supply monitoring systems. Optimizes database architectures and algorithms. TSDB can read or write millions of data points within seconds. Applies an efficient compression algorithm to reduce the size of each data point to 2 bytes, saving more than 90% in storage costs.
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    VictoriaMetrics Cloud

    VictoriaMetrics Cloud

    VictoriaMetrics

    VictoriaMetrics Cloud allows users to run the Enterprise version of VictoriaMetrics, hosted on AWS, without the need to perform typical DevOps tasks such as proper configuration, monitoring, log collection, access protection, software updates, and backups. We run VictoriaMetrics Cloud instances in our environment on AWS and provide easy-to-use endpoints for data ingestion and querying. The VictoriaMetrics team takes care of optimal configuration and software maintenance. It comes with the following features: It can be used as a Managed Prometheus - configure Prometheus or Vmagent to write data to Managed VictoriaMetrics and then use the provided endpoint as a Prometheus data source in Grafana; Every VictoriaMetrics Cloud instance runs in an isolated environment, so instances cannot interfere with each other; VictoriaMetrics Cloud instance can be scaled up or scaled down in a few clicks; Automated backups;
    Starting Price: $190 per month
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    Betoken

    Betoken

    Betoken

    Many people have the capital to invest in cryptocurrency but not the expertise, and many others have the expertise but not the capital. Betoken is an open-source, community-owned, permissionless, and censorship-resistant protocol for connecting capital with talent, and talent with capital. The Betoken protocol recognizes and quantifies the talent of its crypto-traders by allocating units of reputation to them, denominated in Kairo tokens, and financially rewards them based on their talent and contributions. Hundreds of Betoken managers actively prevent losses and seize profitable opportunities for your sake. Your money is converted into an actively managed portfolio of assets that can immediately respond to even the slightest changes in the market. When you deposit your capital on Betoken, it is converted in Betoken Share tokens. It represents your ownership in the Betoken fund and is fully backed by a collateral in the smart contract.
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    Telegraf

    Telegraf

    InfluxData

    Telegraf is the open source server agent to help you collect metrics from your stacks, sensors and systems. Telegraf is a plugin-driven server agent for collecting and sending metrics and events from databases, systems, and IoT sensors. Telegraf is written in Go and compiles into a single binary with no external dependencies, and requires a very minimal memory footprint. Telegraf can collect metrics from a wide array of inputs and write them into a wide array of outputs. It is plugin-driven for both collection and output of data so it is easily extendable. It is written in Go, which means that it is a compiled and standalone binary that can be executed on any system with no need for external dependencies, no npm, pip, gem, or other package management tools required. With 300+ plugins already written by subject matter experts on the data in the community, it is easy to start collecting metrics from your end-points.
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    KNOREX XPO
    KNOREX XPO is an AI-powered advertising management and execution platform that enables marketers to build, launch, track, attribute, and optimize ads across leading advertising channels, including social media, search, CTV/OTT, video, audio, display, native, DOOH, and email, all within a single unified platform. By obliterating the silos of ad channels, ad formats, and devices, XPO allows you to reach your audience with powerful targeting capabilities, providing full-spectrum coverage across the entire digital marketing funnel. KNOREX XPO's AI/ML-powered automation and optimization features, driven by Knorex KAIROS, include cross-channel budget allocation, bidding optimization, campaign recommendations, and personalization of ads, enhancing productivity and efficiency. With unified reporting, XPO offers instant access to siloed data from across different ad channels into one consolidated view, eliminating manual, painstaking consolidation.
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    KX Streaming Analytics
    KX Streaming Analytics provides the ability to ingest, store, process, and analyze historic and time series data to make analytics, insights, and visualizations instantly available. To help ensure your applications and users are productive quickly, the platform provides the full lifecycle of data services, including query processing, tiering, migration, archiving, data protection, and scaling. Our advanced analytics and visualization tools, used widely across finance and industry, enable you to define and perform queries, calculations, aggregations, machine learning and AI on any streaming and historical data. Deployable across multiple hardware environments, data can come from real-time business events and high-volume sources including sensors, clickstreams, radio-frequency identification, GPS systems, social networking sites, and mobile devices.
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    ArcadeDB

    ArcadeDB

    ArcadeDB

    ArcadeDB is an open-source, next-generation multi-model database. Forget Polyglot Persistence — store graphs, documents, key-value pairs, search engine indexes, vectors, and time-series data all in one database with native support for every model. No translation layers, no performance penalties. Process over 10 million records per second. Traversal speed stays constant whether your database has hundreds or billions of records. Query in the language you prefer: SQL, Cypher, Gremlin, GraphQL, MongoDB API, or Java. Deploy ArcadeDB embedded in your JVM application, on a standalone server, or distributed across multiple nodes with Raft Consensus for high availability. Fully ACID-compliant. Super lightweight. Apache 2.0 licensed — free for production and commercial use.
    Starting Price: Free
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    TimescaleDB

    TimescaleDB

    Tiger Data

    TimescaleDB is the leading time-series database built on PostgreSQL, designed to handle massive volumes of real-time data efficiently. It enables organizations to store, analyze, and query time-series data — such as IoT sensor data, financial transactions, or event logs — using standard SQL. With hypertables, TimescaleDB automatically partitions data by time and ID for fast ingestion and predictable query performance. Its compression engine reduces storage costs by up to 95%, while continuous aggregates make real-time dashboards instantly responsive. Fully compatible with PostgreSQL, it integrates seamlessly with existing tools and applications. TimescaleDB combines the simplicity of Postgres with the scalability and speed of a specialized analytical system.
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    Amazon Timestream
    Amazon Timestream is a fast, scalable, and serverless time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day up to 1,000 times faster and at as little as 1/10th the cost of relational databases. Amazon Timestream saves you time and cost in managing the lifecycle of time series data by keeping recent data in memory and moving historical data to a cost optimized storage tier based upon user defined policies. Amazon Timestream’s purpose-built query engine lets you access and analyze recent and historical data together, without needing to specify explicitly in the query whether the data resides in the in-memory or cost-optimized tier. Amazon Timestream has built-in time series analytics functions, helping you identify trends and patterns in your data in near real-time.
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    JaguarDB

    JaguarDB

    JaguarDB

    JaguarDB enables fast ingestion of time series data, coupling location-based data. It also can index in both dimensions, space and time. Back-filling time series data is also fast (inserting large volumes of data in past time). Normally time series is a series of data points indexed in time order. In JaguarDB, the time series has a different meaning: it is both a sequence of data points and a series of tick tables holding aggregated data values at specified time spans. For example, a time series table in JaguarDB can have a base table storing data points in time order, and tick tables such as 5 minute, 15 minute, hourly, daily, weekly, monthly tables to store aggregated data within these time spans. The format for the RETENTION is the same as the TICK format, except that it can have any number of retention periods. The RETENTION specifies how long the data points in the base table should be kept.
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    SiriDB

    SiriDB

    Cesbit

    SiriDB is designed with performance in mind, inserts and queries are answered in a blink of an eye. The custom query language gives you the ability to speed up your development. SiriDB is scalable on the fly and has no downtime while updating or expanding your database. The scalable possibilities enable you to enlarge the database time after time without losing speed. We take full leverage of all available resources as we distribute your time series data over all pools. SiriDB is developed to give an unprecedented performance without downtime. A SiriDB cluster distributes time series across multiple pools. Each pool supports active replicas for load balancing and redundancy. When one of the replicas is not available the database is still accessible.
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    DataStax

    DataStax

    DataStax

    The Open, Multi-Cloud Stack for Modern Data Apps. Built on open-source Apache Cassandra™. Global-scale and 100% uptime without vendor lock-in. Deploy on multi-cloud, on-prem, open-source, and Kubernetes. Elastic and pay-as-you-go for improved TCO. Start building faster with Stargate APIs for NoSQL, real-time, reactive, JSON, REST, and GraphQL. Skip the complexity of multiple OSS projects and APIs that don’t scale. Ideal for commerce, mobile, AI/ML, IoT, microservices, social, gaming, and richly interactive applications that must scale-up and scale-down with demand. Get building modern data applications with Astra, a database-as-a-service powered by Apache Cassandra™. Use REST, GraphQL, JSON with your favorite full-stack framework Richly interactive apps that are elastic and viral-ready from Day 1. Pay-as-you-go Apache Cassandra DBaaS that scales effortlessly and affordably.
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    Cortex

    Cortex

    The Cortex Authors

    Cortex is an open source project that adds horizontal scalability. While Prometheus can scale up to 1 million samples/sec on a single machine, with Cortex horizontal scalability is practically limitless. In a constantly changing environment, you need alternative approaches to monitoring individual VMs or servers. Prometheus' service-discovery driven pull-based metrics system was designed for the dynamic nature of microservices. It lets you easily monitor your whole environment no matter how many moving parts. Instrument your application to create custom metrics using standard Prometheus client libraries, or take advantage of the extensive collection of Prometheus Exporters that collect data from existing applications like MySQL, Redis, Java, ElasticSearch and many more.
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    Machbase

    Machbase

    Machbase

    Machbase, a time-series database that stores and analyzes a lot of sensor data from various facilities in real time, is the only DBMS solution that can process and analyze big data at high speed. Experience the amazing speed of Machbase! It is the most innovative product that enables real-time processing, storage, and analysis of sensor data. High speed sensor data storage and inquiry for sensor data by embedding DBMS in an Edge devices. Best data storage and extraction performance by DBMS running in a single server. Configuring Multi-node cluster with the advantages of availability and scalability. Total management solution of Edge computing for device, connectivity and data.
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    Azure Time Series Insights
    Azure Time Series Insights Gen2 is an open and scalable end-to-end IoT analytics service featuring best-in-class user experiences and rich APIs to integrate its powerful capabilities into your existing workflow or application. You can use it to collect, process, store, query and visualize data at Internet of Things (IoT) scale--data that's highly contextualized and optimized for time series. Azure Time Series Insights Gen2 is designed for ad hoc data exploration and operational analysis allowing you to uncover hidden trends, spotting anomalies, and conduct root-cause analysis. It's an open and flexible offering that meets the broad needs of industrial IoT deployments.
    Starting Price: $36.208 per unit per month
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    Warp 10
    Warp 10 is a modular open source platform that collects, stores, and analyzes data from sensors. Shaped for the IoT with a flexible data model, Warp 10 provides a unique and powerful framework to simplify your processes from data collection to analysis and visualization, with the support of geolocated data in its core model (called Geo Time Series). Warp 10 is both a time series database and a powerful analytics environment, allowing you to make: statistics, extraction of characteristics for training models, filtering and cleaning of data, detection of patterns and anomalies, synchronization or even forecasts. The analysis environment can be implemented within a large ecosystem of software components such as Spark, Kafka Streams, Hadoop, Jupyter, Zeppelin and many more. It can also access data stored in many existing solutions, relational or NoSQL databases, search engines and S3 type object storage system.
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    Graphite

    Graphite

    Graphite

    Graphite is an enterprise-ready monitoring tool that runs equally well on cheap hardware or Cloud infrastructure. Teams use Graphite to track the performance of their websites, applications, business services, and networked servers. It marked the start of a new generation of monitoring tools, making it easier than ever to store, retrieve, share, and visualize time-series data. Graphite was originally designed and written by Chris Davis at Orbitz in 2006 as side project that ultimately grew to be their foundational monitoring tool. In 2008, Orbitz allowed Graphite to be released under the open-source Apache 2.0 license. Numerous large companies have deployed Graphite to production where it helps them to monitor their production e-commerce services and plan for growth. Metrics get fed into the stack via the Carbon service, which writes the data out to Whisper databases for long-term storage.
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    kdb+

    kdb+

    KX Systems

    A high-performance cross-platform historical time-series columnar database featuring: - An in-memory compute engine - A real-time streaming processor - An expressive query and programming language called q kdb+ powers kdb Insights portfolio and KDB.AI, together delivering time-oriented data insights and generative AI capabilities to the world’s leading enterprise organizations. Independently benchmarked* as the fastest in-memory, columnar analytics database available, kdb+ delivers unmatched value to businesses operating in the toughest data environments. kdb+ improves decision-making processes to help navigate rapidly changing data landscapes​.
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    Google Cloud Datastore
    Datastore is a highly scalable NoSQL database for your applications. Datastore automatically handles sharding and replication, providing you with a highly available and durable database that scales automatically to handle your applications' load. Datastore provides a myriad of capabilities such as ACID transactions, SQL-like queries, indexes, and much more. With Datastore's RESTful interface, data can easily be accessed by any deployment target. You can build solutions that span across App Engine and Compute Engine and rely on Datastore as the integration point. Focus on building your applications without worrying about provisioning and load anticipation. Datastore scales seamlessly and automatically with your data, allowing applications to maintain high performance as they receive more traffic.
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    QuasarDB

    QuasarDB

    QuasarDB

    Quasar's brain is QuasarDB, a high-performance, distributed, column-oriented timeseries database management system designed from the ground up to deliver real-time on petascale use cases. Up to 20X less disk usage. Quasardb ingestion and compression capabilities are unmatched. Up to 10,000X faster feature extraction. QuasarDB can extract features in real-time from the raw data, thanks to the combination of a built-in map/reduce query engine, an aggregation engine that leverages SIMD from modern CPUs, and stochastic indexes that use virtually no disk space. The most cost-effective timeseries solution, thanks to its ultra-efficient resource usage, the capability to leverage object storage (S3), unique compression technology, and fair pricing model. Quasar runs everywhere, from 32-bit ARM devices to high-end Intel servers, from Edge Computing to the cloud or on-premises.
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    MongoDB Compass
    Easily work with your data in Compass, the GUI built by — and for — MongoDB. Compass provides everything from schema analysis to index optimization to aggregation pipelines in a single, centralized interface. Dissect your document schema to understand your data at a deeper level. Compass samples and analyzes your documents to provide detailed metadata on your collections, including the ranges of dates and integers, most frequent values, and more. Find the data you need in seconds with Compass’ built-in query bar. Filter the documents within your collection using intuitive query operators that correspond to expressions in other languages. Sample, sort, and modify results with high granularity. Add new indexes and remove underperforming ones to optimize queries, monitor real-time server and database metrics, investigate performance issues with the visual explain plan, and more.
    Starting Price: Free
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    Proficy Historian
    Proficy Historian is a best-in-class historian software solution that collects industrial time-series and A&E data at very high speed, stores it efficiently and securely, distributes it, and allows for fast retrieval and analysis —driving greater business value. With decades of experience and thousands of successful customer installations around the world, Proficy Historian changes the way companies perform and compete by making data available for asset and process performance analysis. The most recent Proficy Historian enhances usability, configurability and maintainability with significant architectural improvements. Take advantage of the solution’s simple yet powerful features to unlock new value from your equipment, process data, and business models. Remote collector management with UX. Horizontal scalability that enables enterprise-wide data visibility.
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    Tiger Data

    Tiger Data

    Tiger Data

    Tiger Data is the creator of TimescaleDB, the world’s leading PostgreSQL-based time-series and analytics database. It provides a modern data platform purpose-built for developers, devices, and AI agents. Designed to extend PostgreSQL beyond traditional limits, Tiger Data offers built-in primitives for time-series data, search, materialization, and scale. With features like auto-partitioning, hybrid storage, and compression, it helps teams query billions of rows in milliseconds while cutting infrastructure costs. Tiger Cloud delivers these capabilities as a fully managed, elastic environment with enterprise-grade security and compliance. Trusted by innovators like Cloudflare, Toyota, Polymarket, and Hugging Face, Tiger Data powers real-time analytics, observability, and intelligent automation across industries.
    Starting Price: $30 per month
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    PeerDB

    PeerDB

    PeerDB

    If Postgres is at the core of your business and is a major source of data, PeerDB provides a fast, simple, and cost-effective way to replicate data from Postgres to data warehouses, queues, and storage. Designed to run at any scale, and tailored for data stores. PeerDB uses replication messages from the Postgres replication slot to replay the schema messages. Alerts for slot growth and connections. Native support for Postgres toast columns and large JSONB columns for IoT. Optimized query design to reduce warehouse costs; particularly useful for Snowflake and BigQuery. Support for partitioned tables via both publish. Blazing fast and consistent initial load by transaction snapshotting and CTID scans. High-availability, in-place upgrades, autoscaling, advance logs, metrics and monitoring dashboards, burstable instance types, and suitable for dev environments.
    Starting Price: $250 per month
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    NoSQL

    NoSQL

    NoSQL

    NoSQL is a domain-specific programming language used for accessing, managing, and manipulating non-tabular databases. A NoSQL (originally referring to "non-SQL" or "non-relational") database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but the name "NoSQL" was only coined in the early 21st century, triggered by the needs of Web 2.0 companies. NoSQL databases are increasingly used in big data and real-time web applications.NoSQL systems are also sometimes called Not only SQL to emphasize that they may support SQL-like query languages or sit alongside SQL databases in polyglot-persistent architectures. Many NoSQL stores compromise consistency (in the sense of the CAP theorem) in favor of availability, partition tolerance, and speed. Barriers to the greater adoption of NoSQL stores include the use of low-level query languages.
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    eXtremeDB

    eXtremeDB

    McObject

    How is platform independent eXtremeDB different? - Hybrid data storage. Unlike other IMDS, eXtremeDB can be all-in-memory, all-persistent, or have a mix of in-memory tables and persistent tables - Active Replication Fabric™ is unique to eXtremeDB, offering bidirectional replication, multi-tier replication (e.g. edge-to-gateway-to-gateway-to-cloud), compression to maximize limited bandwidth networks and more - Row & Columnar Flexibility for Time Series Data supports database designs that combine row-based and column-based layouts, in order to best leverage the CPU cache speed - Embedded and Client/Server. Fast, flexible eXtremeDB is data management wherever you need it, and can be deployed as an embedded database system, and/or as a client/server database system -A hard real-time deterministic option in eXtremeDB/rt Designed for use in resource-constrained, mission-critical embedded systems. Found in everything from routers to satellites to trains to stock markets worldwide
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    Canary Historian
    The beauty of the Canary Historian is that the same solution works as well on site as it does for the entire enterprise. You can log data locally, while sending it to your enterprise historian simultaneously. Best of all, as you grow, so does the solution. A single Canary Historian can log more than two million tags, and multiple Canary Historians can be clustered to handle tens of millions of tags. Enterprise historian solutions can be hosted in your own data centers or in AWS and Azure. And, unlike other enterprise historian solutions, Canary Historians don't require specialized teams of ten and more to maintain them. The Canary Historian is a NoSQL time series database that uses loss-less compression algorithms to provide you the best of both worlds, high-speed performance without requiring data interpolation!
    Starting Price: $9,970 one-time payment
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    Heroic

    Heroic

    Heroic

    Heroic is an open-source monitoring system originally built at Spotify to address problems faced with large scale gathering and near real-time analysis of metrics. Heroic uses a small set of components which are responsible for very specific things. Indefinite retention, as long as you have the hardware spend. Federation support to connect multiple Heroic clusters into a global interface. Heroic uses a small set of components which are responsible for very specific things. Consumers are the component responsible for consuming metrics. When building Heroic it was quickly realized that navigating hundreds of millions of time series without context is hard. Heroic has support for federating requests, which allows multiple independent Heroic clusters to serve clients through a single global interface. This can be used to reduce the amount of geographical traffic by allowing one cluster to operate completely isolated within its zone.
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    ITTIA DB
    The ITTIA DB product family combines the best of time series, real-time data streaming, and analytics for embedded systems to reduce development time and costs. ITTIA DB IoT is a small-footprint embedded database for real-time resource-constrained 32-bit microcontrollers (MCUs), and ITTIA DB SQL is a high-performance time-series embedded database for single or multicore microprocessors (MPUs). Both ITTIA DB products enable devices to monitor, process, and store real-time data. ITTIA DB products are also built for the automotive industry Electronic Control Units (ECUs). ITTIA DB data security protocols offer data protection against malicious access with encryption, authentication, and DB SEAL. ITTIA SDL is conformant to the principles of IEC/ISO 62443. Embed ITTIA DB to collect, process, and enrich incoming real-time data streams in a purpose-built SDK for edge devices. Search, filter, join, and aggregate at the edge.
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    Google Cloud Bigtable
    Google Cloud Bigtable is a fully managed, scalable NoSQL database service for large analytical and operational workloads. Fast and performant: Use Cloud Bigtable as the storage engine that grows with you from your first gigabyte to petabyte-scale for low-latency applications as well as high-throughput data processing and analytics. Seamless scaling and replication: Start with a single node per cluster, and seamlessly scale to hundreds of nodes dynamically supporting peak demand. Replication also adds high availability and workload isolation for live serving apps. Simple and integrated: Fully managed service that integrates easily with big data tools like Hadoop, Dataflow, and Dataproc. Plus, support for the open source HBase API standard makes it easy for development teams to get started.
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    HugeGraph

    HugeGraph

    HugeGraph

    HugeGraph is a fast-speed and highly-scalable graph database. Billions of vertices and edges can be easily stored into and queried from HugeGraph due to its excellent OLTP ability. As compliance to Apache TinkerPop 3 framework, various complicated graph queries can be accomplished through Gremlin (a powerful graph traversal language). Among its features, it provides compliance to Apache TinkerPop 3, supporting Gremlin. Schema Metadata Management, including VertexLabel, EdgeLabel, PropertyKey and IndexLabel. Multi-type Indexes, supporting exact query, range query and complex conditions combination query. Plug-in Backend Store Driver Framework, supporting RocksDB, Cassandra, ScyllaDB, HBase and MySQL now and easy to add other backend store driver if needed. Integration with Hadoop/Spark. HugeGraph relies on the TinkerPop framework, we refer to the storage structure of Titan and the schema definition of DataStax.
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    Oracle Big Data SQL Cloud Service
    Oracle Big Data SQL Cloud Service enables organizations to immediately analyze data across Apache Hadoop, NoSQL and Oracle Database leveraging their existing SQL skills, security policies and applications with extreme performance. From simplifying data science efforts to unlocking data lakes, Big Data SQL makes the benefits of Big Data available to the largest group of end users possible. Big Data SQL gives users a single location to catalog and secure data in Hadoop and NoSQL systems, Oracle Database. Seamless metadata integration and queries which join data from Oracle Database with data from Hadoop and NoSQL databases. Utilities and conversion routines support automatic mappings from metadata stored in HCatalog (or the Hive Metastore) to Oracle Tables. Enhanced access parameters give administrators the flexibility to control column mapping and data access behavior. Multiple cluster support enables one Oracle Database to query multiple Hadoop clusters and/or NoSQL systems.
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    InfluxDB

    InfluxDB

    InfluxData

    InfluxDB is a purpose-built data platform designed to handle all time series data, from users, sensors, applications and infrastructure — seamlessly collecting, storing, visualizing, and turning insight into action. With a library of more than 250 open source Telegraf plugins, importing and monitoring data from any system is easy. InfluxDB empowers developers to build transformative IoT, monitoring and analytics services and applications. InfluxDB’s flexible architecture fits any implementation — whether in the cloud, at the edge or on-premises — and its versatility, accessibility and supporting tools (client libraries, APIs, etc.) make it easy for developers at any level to quickly build applications and services with time series data. Optimized for developer efficiency and productivity, the InfluxDB platform gives builders time to focus on the features and functionalities that give their internal projects value and their applications a competitive edge.
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    Chronosphere

    Chronosphere

    Chronosphere

    Purpose built for cloud-native’s unique monitoring challenges. Built from day one to handle the outsized volume of monitoring data produced by cloud-native applications. Offered as a single centralized service for business owners, application developers and infrastructure engineers to debug issues throughout the stack. Tailored for each use case from sub-second data for continuous deployments to one hour data for capacity planning. One-click deployment with support for Prometheus and StatsD ingestion protocols. Storage and index for both Prometheus and Graphite data types in the same solution. Embedded Grafana compatible dashboards with full support for PromQL and Graphite. Dependable alerting engine with integration for PagerDuty, Slack, OpsGenie and webhooks. Ingest and query billions of metric data points per second. Trigger alerts, pull up dashboards and detect issues within a second. Keep three consistent copies of your data across failure domains.
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    miniDBA

    miniDBA

    miniDBA Software

    miniDBA provides the most cost-effective way to monitor and tune SQL Server on premises or in the cloud (Azure VM, Linux, Amazon RDS or Google Cloud Platform) Improve query speed. Performance Tune with real time metrics and T-SQL query plan analysis. Drill down to your slowest batch and query plan operator using no code. See instant performance gains using easy to implement recommendations. Run over 50 health checks targetting both server configuration and database/T-SQL level. If your current Windows user security gives you enough permission on the Sql Server being connected to then leave authentication as windows. If you need to use Sql Server authentication including name and password then choose that here. Note: passwords are encrypted by miniDBA using built in Windows encryption functions to ensure no sensitive data is held unencrypted by miniDBA.
    Starting Price: $99 one-time payment