The future of observability is in AI

Discover how AI agents are shaping modern cloud-native environments into systems you can understand, predict and control.

Debunk observability myths Track top SLA metrics

The visibility gap

Cloud‑native environments are constantly changing. Microservices are redeployed, containers refresh, and cloud platforms push updates that silently reshape dependencies. What should be observable becomes fragmented, and DevOps teams lose the context they need to stay ahead.

Feels familiar? Cut through the noise and regain clarity in modern, fast-moving systems with full-stack observability.

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≥30%

of data overlap around telemetry tools

 14%

of developer challenges are security-related

 40%

of developer challenges occur while handling or managing data

 5x

more users dependent on observability tools
When everything works—until it doesn’t

Security is the most persistent drag on cloud-native delivery. Fast-moving releases collide with slow, manual checks, and the smallest certificate misstep becomes a system‑wide outage.

As architectures expand, security workflows built for static systems fall behind, causing recurring failures, costly blind spots and delayed deployments.

Illustration of scattered squares, some are red and some are gray

Cloud platforms change constantly—AWS alone introduced 47 service updates in a single month.

Each update carries new behaviors, new dependencies and new risks that traditional reviews can’t track. Teams spend more time chasing changes than improving the system. Compliance frameworks, such as GDPR, HIPAA and PCI‑DSS, add more pressure.

Without adaptive monitoring, misconfigurations surface only after they cause outages, breaches or SLA violations.

Illustration of a centered application passing through diverse, circular workflows, resembling a technological process

Cloud‑native systems now produce 100x more observability data and up to 500x more data transfer than traditional apps.

Every microservice, container, gateway and cloud update adds another signal stream—yet tools remain fragmented. Insights scatter across formats and platforms, turning expertise into guesswork and burying real issues beneath the noise.

Illustration of different workflows, coming from the same direction
An illustration of two percentage ball graphs: the smaller one displays "100x", and the larger one displays "500x".

Telemetry complexity keeps growing

Telemetry is becoming increasingly challenging to capture, manage and interpret. Each change introduces diverse and expanding data—new metrics, logs and traces with varying formats and meanings—making normalization and analysis essential for deriving insight. 

Effective observability depends on scalable tools, adaptive monitoring, integrated data and proactive alerts to ensure system health amid constant change. 

100x
more entities to monitor in cloud-native architectures
500x
more data transferred for observability

Are you blocked by your IT complexity?

Get the data behind the myths to simplify your IT and drive business results.

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AI-driven observability sets a new standard

Cloud‑native observability has outgrown human limits. As microservices scale, telemetry surges, tools multiply and teams rely on shared insight. 

AI helps reverse this trend by cutting noise, highlighting what matters and tying system behavior to business outcomes. As a result, cloud environments become observable enough to support continuous optimization for resilience, reliability and growth.

Digital rendering of a block from the Automation Tool Kit.
Take the next step

Shift from reactive monitoring to predictive, automated insight. Explore how AI-boosted observability can elevate your strategy and prepare your systems for what’s next.

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Footnotes

AI-Boosted Observability: Leveraging Generative AI for Enhanced Insight Into App Development, DevOps, Operators, and Security Challenges”, EMA Research Report, Q1 2024