Cloud Time-Series Databases vs On-Premises: Which Is Better?

Chait Diwadkar
Chait Diwadkar
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The rapid proliferation of Internet of Things (IoT) tools for collecting and harnessing the power of timestamped data has revolutionized the way many industries operate. Enterprises of all types — and especially those across the industrial landscape — are increasingly adopting IoT technologies to reap the competitive advantages of data-driven decision-making. However, to truly realize the potential of their data, organizations need a time-series database to optimize data storage and enable a framework for extracting meaningful trends.

When it comes to selecting a time-series database solution, organizations often encounter a pivotal decision: whether to opt for a cloud-based solution or an on-premises database. Each alternative introduces a distinct approach to storing and managing timestamped data.

The fundamental difference lies in the deployment approach. Cloud time-series databases are hosted and managed on a cloud service provider’s platform or a third-party server, following a managed service model. On-premises time-series databases are installed locally within an organization’s data center or physical infrastructure, making the user entirely responsible for all facets of database operations, including the data itself.

This article compares the pros and cons of cloud time-series databases against on-premises solutions, addressing several key considerations. The purpose of this analysis is to empower readers with insights into each option, helping you to carefully decide which database type aligns most appropriately with your company’s long-term strategy and specific needs.


Cloud: Is your IoT deployment projected to expand, leading to increased data storage demands? If so, cloud time-series databases should be prioritized. Built to scale horizontally in response to escalating timestamped data volumes, cloud time-series databases offer an ideal foundation for supporting shifting workload demands and rapid data growth from an ever-larger IoT program.

On-Premises: Owners of on-premises time-series databases face more complexity when it comes to scaling and may have to invest in additional infrastructure and hardware in order to accommodate higher data volumes.  

Cost Structure

Cloud: Cloud time-series databases offer the cost flexibility of a pay-as-you-go model, enabling organizations to avoid upfront hardware and infrastructure expenses and only pay for the resources they need, thus optimizing cost efficiency.

On-Premises: Procuring and establishing an on-premises solution often entails a significant capital outlay, requiring investments in hardware, software and dedicated IT personnel. While longer-term reoccurring costs might be predictable, owners must still contend with ongoing maintenance costs.

Oversight and Management

Cloud: Cloud time-series databases are managed by the cloud service provider, which includes regular updates and automated backups. Users are relieved of the operational burden associated with database maintenance.  

On-Premises: Selecting an on-premises solution makes the owner accountable for managing and sustaining the database. This includes backups and system upgrades—all of which may require IT expertise.

Data Security and Compliance

Cloud: Cloud providers implement comprehensive security protocols, including measures such as encryption, access controls, and compliance certifications. However, organizations with strict data protection requirements may harbor concerns about storing sensitive data on external servers.

On-Premises: On-premises time-series databases offer organizations more control over data security and compliance—allowing them to implement specific security measures for meeting internal requirements or regulatory standards.


Cloud: The elasticity of a time-series database refers to its ability to dynamically expand or reduce resources in response to demand fluctuations. Cloud time-series databases excel in this realm, seamlessly adapting storage and computing resources to automatically meet workload and latency needs.

On-Premises: Achieving elasticity with an on-premises solution typically requires manual intervention and procuring additional hardware based on the expectation of higher workloads. 

Data Accessibility

Cloud: With a stable internet connection, cloud time-series databases offer global accessibility, an advantage for companies with geographically distributed teams and remote applications.

On-Premises: The availability of on-premises databases could be confined to the physical site where the infrastructure is deployed, presenting challenges for remote users. With a direct database connection, data can be accessed with low latency.


A company’s preference for a time-series database hinges on a multitude of factors, including scalability needs, security mandates, cost considerations, and the nature of a long-term data collection program. For instance, organizations in highly regulated industries that may be bound by legal constraints or data sovereignty requirements might be inclined to adopt an on-premises solution to provide greater command over sensitive data. 

Conversely, industrial operators with IoT deployments would be well-served with the scalability of a cloud time-series database. Tailored to the unique demands of industrial IoT, cloud-based solutions incorporate an architecture that is optimized for managing surging amounts of timestamped data while integrating into a data historian or broader ecosystem of tools for leveraging the benefits of predictive modeling, AI-enabled analytics, and more.

  • Chait Diwadkar
    Chait Diwadkar

    Chait Diwadkar is Director of Solution Engineering at TDengine. Prior to joining TDengine he was in the biotechnology industry in technical marketing, professional services, and product management roles and supported customers in pharma, medical devices and diagnostics on analytical chemistry, and genetic platforms.