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Datadog Releases Flex Logs

Datadog announced Flex Logs, a new tier for log management.

Built on top of Datadog's Husky technology, Flex Logs enables organizations to retain and query high-volume data that has traditionally been cost prohibitive to use for observability.

Flex Logs enables organizations to retain massive volumes of data that they would previously not collect or store because of high costs. This new capability works alongside Datadog's standard indexing so users have the flexibility to choose which logs are indexed for real-time alerts and dashboards, and which are stored for long-term querying use cases. With Flex Logs, teams also have control over their level of computational power needed so they can provision for thousands of users making a large number of queries, or control costs for a small number of users who only query occasionally.

"As application complexity grows, so do log volumes. Organizations need to improve their visibility into these logs while staying within a reasonable budget," said Michael Whetten, VP of Product at Datadog. "Flex Logs introduces Datadog's easy-to-use Log Management platform to more teams—from IT troubleshooting to policy compliance and business analytics—in a cost-effective and scalable way so that they can store and take action on all their logs."

With Flex Logs, Datadog customers will benefit from:

- Better ROI: Teams can optimize compute to match the needs of users for investigations, compliance audits, security investigations and more.

- Instant access to historical data: Engineering and security teams can investigate old issues without needing to perform a rehydration.

- Predictable growth: As logging volumes grow, organizations can ramp up compute separately from storage in order to manage their budgets in a predictable way.

- Unified observability: Datadog's unified platform enriches logs by automatically integrating and correlating different types of data from application metrics and security sources so that organizations have a unified view of their observability data.

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Datadog Releases Flex Logs

Datadog announced Flex Logs, a new tier for log management.

Built on top of Datadog's Husky technology, Flex Logs enables organizations to retain and query high-volume data that has traditionally been cost prohibitive to use for observability.

Flex Logs enables organizations to retain massive volumes of data that they would previously not collect or store because of high costs. This new capability works alongside Datadog's standard indexing so users have the flexibility to choose which logs are indexed for real-time alerts and dashboards, and which are stored for long-term querying use cases. With Flex Logs, teams also have control over their level of computational power needed so they can provision for thousands of users making a large number of queries, or control costs for a small number of users who only query occasionally.

"As application complexity grows, so do log volumes. Organizations need to improve their visibility into these logs while staying within a reasonable budget," said Michael Whetten, VP of Product at Datadog. "Flex Logs introduces Datadog's easy-to-use Log Management platform to more teams—from IT troubleshooting to policy compliance and business analytics—in a cost-effective and scalable way so that they can store and take action on all their logs."

With Flex Logs, Datadog customers will benefit from:

- Better ROI: Teams can optimize compute to match the needs of users for investigations, compliance audits, security investigations and more.

- Instant access to historical data: Engineering and security teams can investigate old issues without needing to perform a rehydration.

- Predictable growth: As logging volumes grow, organizations can ramp up compute separately from storage in order to manage their budgets in a predictable way.

- Unified observability: Datadog's unified platform enriches logs by automatically integrating and correlating different types of data from application metrics and security sources so that organizations have a unified view of their observability data.

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As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

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