Skip to main content

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.

The Latest

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

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.

The Latest

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...