Vector Database for Time-Series Internet of Things (IoT) Global Market Report 2025

The vector database for time-series internet of things (IoT) market size has grown exponentially in recent years. It will grow from $1.52 billion in 2024 to $1.92 billion in 2025 at a compound annual growth rate (CAGR) of 26.9%. The growth during the historic period can be attributed to increasing integration of cloud platforms with vector databases, rising focus on data security and compliance, growing investment in smart manufacturing and industrial IoT, expanding use of sensor-rich environments for monitoring, and rising interest in digital twins for industrial applications.

The vector database for time-series internet of things (IoT) market size is expected to see exponential growth in the next few years. It will grow to $4.92 billion in 2029 at a compound annual growth rate (CAGR) of 26.5%. The growth during the forecast period can be attributed to increased adoption of AI and machine learning in IoT, rising demand for real-time data analytics, growth in connected devices and sensor deployment, increasing need for low-latency query processing, and heightened focus on scalable and high-performance storage solutions. Key trends in the forecast period include advancements in real-time analytics, enhanced machine learning integration, innovations in data compression techniques, incorporation of AI-driven query optimization, and progress in hybrid cloud deployment.

The increasing adoption of IoT devices is expected to drive the growth of the vector database for time-series internet of things (IoT) market going forward. IoT devices are physical objects equipped with sensors and internet connectivity that enable them to collect, share, and respond to data in real time. Their rise is driven by the ability to enhance efficiency through process automation and real-time data insights, allowing businesses and consumers to make faster, smarter decisions. A vector database for time-series IoT is essential as it efficiently processes and analyzes large volumes of sensor data, enabling IoT devices to deliver quicker and more accurate real-time insights. For example, in September 2024, according to Ericsson, a Sweden-based telecommunications company, broadband and critical IoT (4G/5G) connections are projected to double, reaching 4.3 billion by 2030. Therefore, the increasing adoption of IoT devices is fueling the growth of the vector database for time-series internet of things (IoT) market.

Key companies in the vector database for time-series internet of things (IoT) market are focusing on developing RAFT-based integration to simplify the implementation of consensus algorithms. RAFT-based integration is a consensus mechanism that ensures data consistency and fault tolerance across distributed systems by synchronizing updates among multiple nodes. For instance, in March 2023, Zilliz, a US-based enterprise-grade vector database provider, launched Milvus 2.3. It incorporates RAFT-based integration to enable heterogeneous computing and maintain efficient synchronization across distributed systems. With NVIDIA GPU support, it offers enhanced flexibility and substantial improvements in real-time workload efficiency. The system achieves faster parallel processing and query speeds, performing up to four times better than Milvus 2 and more than ten times faster than databases using traditional architectures for vector search. Its GPU acceleration delivers tenfold higher performance compared to CPU-only setups, establishing Milvus 2.3 as a robust solution for AI and machine learning workloads.

In June 2024, OpenAI Inc., a US-based AI research and deployment company, acquired Rockset Inc. for an undisclosed amount. Through this acquisition, OpenAI Inc. aims to strengthen its real-time data processing and analytics capabilities by integrating Rockset Inc.’s cloud-native search and database technologies, enabling faster and more efficient access to large-scale data, improving AI model performance, and supporting the development of more responsive, data-driven applications. Rockset Inc. is a US-based company specializing in vector database for time-series IoT.

Major players in the vector database for time-series internet of things (iot) market are Microsoft Corp., Alibaba Group Holding Limited, International Business Machines Corporation, MongoDB Inc., Elastic N.V., Redis Ltd., Kx Systems Inc., SingleStore Inc., ClickHouse Inc., Timescale Inc., PlanetScale Inc., Pinecone Systems Inc., Crate.io GmbH, Piaggio & C. S.p.A., Weaviate Holding Inc., Zilliz Inc., Qdrant Solutions GmbH, OpenSearch Software Foundation, Rockset Inc., and ObjectBox Ltd.

North America was the largest region in the vector database for time-series internet of things (IoT) market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in vector database for time-series internet of things (IoT) report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa. The countries covered in the vector database for time-series internet of things (IoT) market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report’s Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

The rapid escalation of U.S. tariffs and the resulting trade tensions in spring 2025 are significantly impacting the information technology sector, particularly in hardware manufacturing, data infrastructure, and software deployment. Higher duties on imported semiconductors, circuit boards, and networking equipment have raised production and operational costs for tech firms, cloud service providers, and data centers. Companies relying on globally sourced components for laptops, servers, and consumer electronics are facing longer lead times and increased pricing pressures. In parallel, tariffs on specialized software tools and retaliatory measures from key international markets have disrupted global IT supply chains and reduced overseas demand for U.S.-developed technologies. To navigate these challenges, the sector is accelerating investments in domestic chip fabrication, diversifying supplier bases, and adopting AI-driven automation to enhance operational resilience and cost efficiency.

The vector database for time-series internet of things (IoT) market research report is one of a series of new reports that provides vector database for time-series internet of things (IoT) market statistics, including vector database for time-series internet of things (IoT) industry global market size, regional shares, competitors with a vector database for time-series internet of things (IoT) market share, detailed vector database for time-series internet of things (IoT) market segments, market trends and opportunities, and any further data you may need to thrive in the vector database for time-series internet of things (IoT) industry. This vector database for time-series internet of things (IoT) market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Vector database for time-series internet of things (IoT) is a specialized database designed to store, manage, and query high-dimensional vector data generated by IoT devices over time. It enables efficient similarity searches, real-time analytics, and retrieval of patterns from large volumes of time-series sensor data. By converting IoT signals into vector representations, it supports advanced AI and machine learning applications, including anomaly detection and predictive maintenance.

The key components of a vector database for time-series internet of things (IoT) are software, hardware, and services. The software is a specialized database solution designed to efficiently store, index, and query high-dimensional vector representations of time-series IoT data, enabling fast similarity searches, real-time analytics, and AI-driven insights. Deployment modes include on-premises and cloud, and applications include predictive maintenance, real-time analytics, asset tracking, anomaly detection, and others. It is used by various end users, including manufacturing, energy and utilities, healthcare, transportation and logistics, smart cities, and others.

The vector database for time-series internet of things (IoT) market consists of revenues earned by entities by providing services such as historical data aggregation services, feature extraction services, anomaly detection services, data retention services, and predictive maintenance services. The market value includes the value of related goods sold by the service provider or included within the service offering. The vector database for time-series internet of things (IoT) market also includes sales of sensors, network routers, storage appliances, smart meters, and data logging devices. Values in this market are ‘factory gate’ values; that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

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