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Datadog Acquires Eppo

Datadog has acquired Eppo, a feature flagging and experimentation platform, which will tightly integrate with Datadog's existing Product Analytics suite.

With its acquisition of Eppo, Datadog creates a full end-to-end product analytics solution on one platform. This unified approach means that engineers can track code changes with feature flags, data science leaders together with product managers can design and measure impact with experiments, and business analysts can use Datadog’s Product Analytics suite to understand overall product usage and business outcomes.

As AI workloads grow, Eppo’s experimentation capabilities help developers safely scale complex systems. These capabilities can measure the impact to the overall user experience in real time and accelerate the safe roll-out of changes, ultimately creating a more agile and trustworthy development workflow.

“The use of multiple AI models increases the complexity of deploying applications in production. This complexity makes it difficult for developers to quantify the business impact of different models, agent behaviors, prompts or UI changes,” said Michael Whetten, VP of Product at Datadog. “Experimentation solves this correlation and measurement problem, enabling teams to compare multiple models side-by-side, determine user engagement against cost tradeoffs and ultimately build AI products that deliver measurable value."

“Eppo wants to bring a high velocity, experiment-first culture to companies of every size, stage and industry,” said Chetan Sharma, founder and CEO of Eppo. “With Datadog, we are uniting product analytics, feature management, AI and experimentation capabilities for businesses to reduce risk, learn quickly and ship high-quality products.”

Eppo will continue supporting existing customers and bringing on new customers as part of Eppo by Datadog.

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Datadog Acquires Eppo

Datadog has acquired Eppo, a feature flagging and experimentation platform, which will tightly integrate with Datadog's existing Product Analytics suite.

With its acquisition of Eppo, Datadog creates a full end-to-end product analytics solution on one platform. This unified approach means that engineers can track code changes with feature flags, data science leaders together with product managers can design and measure impact with experiments, and business analysts can use Datadog’s Product Analytics suite to understand overall product usage and business outcomes.

As AI workloads grow, Eppo’s experimentation capabilities help developers safely scale complex systems. These capabilities can measure the impact to the overall user experience in real time and accelerate the safe roll-out of changes, ultimately creating a more agile and trustworthy development workflow.

“The use of multiple AI models increases the complexity of deploying applications in production. This complexity makes it difficult for developers to quantify the business impact of different models, agent behaviors, prompts or UI changes,” said Michael Whetten, VP of Product at Datadog. “Experimentation solves this correlation and measurement problem, enabling teams to compare multiple models side-by-side, determine user engagement against cost tradeoffs and ultimately build AI products that deliver measurable value."

“Eppo wants to bring a high velocity, experiment-first culture to companies of every size, stage and industry,” said Chetan Sharma, founder and CEO of Eppo. “With Datadog, we are uniting product analytics, feature management, AI and experimentation capabilities for businesses to reduce risk, learn quickly and ship high-quality products.”

Eppo will continue supporting existing customers and bringing on new customers as part of Eppo by Datadog.

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A new study by the IBM Institute for Business Value reveals that enterprises are expected to significantly scale AI-enabled workflows, many driven by agentic AI, relying on them for improved decision making and automation. The AI Projects to Profits study revealed that respondents expect AI-enabled workflows to grow from 3% today to 25% by the end of 2025. With 70% of surveyed executives indicating that agentic AI is important to their organization's future, the research suggests that many organizations are actively encouraging experimentation ...

Respondents predict that agentic AI will play an increasingly prominent role in their interactions with technology vendors over the coming years and are positive about the benefits it will bring, according to The Race to an Agentic Future: How Agentic AI Will Transform Customer Experience, a report from Cisco ...

A new wave of tariffs, some exceeding 100%, is sending shockwaves across the technology industry. Enterprises are grappling with sudden, dramatic cost increases that threaten to disrupt carefully planned budgets, sourcing strategies, and deployment plans. For CIOs and CTOs, this isn't just an economic setback; it's a wake-up call. The era of predictable cloud pricing and stable global supply chains is over ...

As artificial intelligence (AI) adoption gains momentum, network readiness is emerging as a critical success factor. AI workloads generate unpredictable bursts of traffic, demanding high-speed connectivity that is low latency and lossless. AI adoption will require upgrades and optimizations in data center networks and wide-area networks (WANs). This is prompting enterprise IT teams to rethink, re-architect, and upgrade their data center and WANs to support AI-driven operations ...

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

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