Enhancing UX and AI Initiatives through Observability Trends 2025

Originally published at ssojet Insights from Honeycomb at KubeCon During KubeCon Europe, Christine Yen, CEO and co-founder of Honeycomb, highlighted how observability can improve the user experience in systems that incorporate Large Language Models (LLMs). Yen emphasized that while software engineers are familiar with "black boxes," LLMs introduce new complexities due to their unpredictable behavior. Yen noted that the introduction of LLMs changes software development methodologies. Current practices focus on deterministic properties, but LLMs capture the unpredictable nature of language. She stated, "...most of us here today have been building software before this LLM boom... It’s testing, maintenance, tuning and debugging that comes afterwards... should we all give up and embrace prompt engineering? **No! That’s why we are here…" Key methodologies for adapting to these changes include: Continuous Deployment and feature flags for rapid feedback loops Testing in production to embrace chaos High cardinality metadata to reflect complexity High dimensionality data for parameterized experiments Service Level Objectives to prioritize user experience Observability enables teams to manage unpredictability and incorporate feedback into development processes. As LLMs generate an infinite set of inputs and outputs, traditional testing methods become less effective. Instead, evaluations are necessary to understand LLM behavior under various conditions. /filters:no_upscale()/news/2025/04/llm-observability/en/resources/10image2-1743679288254.png) Yen expressed optimism about the future of software engineering and the critical role observability will play, especially with the rise of Generative AI. Dynatrace's AI Observability Enhancements Dynatrace announced advancements in AI observability to support Generative AI initiatives. Their updated platform provides comprehensive insights into AI applications, enhancing reliability, performance, security, and compliance. Key features include: Enhanced LLM Model Analytics to monitor KPIs like input and output errors and predict cost changes LLM Input and Output Guardrails to safeguard AI application quality Multi-model Tracing for end-to-end observability of complex systems Responsible AI Integrations for comprehensive audit trails Dynatrace emphasizes the need for observability in AI systems to ensure they are implemented securely and provide a high return on investment. Companies like FreedomPay utilize Dynatrace to enhance their analytics capabilities. Stephen Elliot from IDC reinforced that AI observability is essential for deploying reliable and cost-effective AI solutions. Observability Trends in 2025 Observability has evolved significantly, driven by the increasing complexity of cloud-native environments. Modern observability frameworks integrate AI, automation, and security, moving beyond traditional monitoring methods. Key trends identified include: Cost Reduction through Data Management: Companies are adopting smarter data collection methods to minimize storage costs. By sampling key traces and reducing unnecessary data, organizations can cut costs by 60-80%. AI-Driven Predictive Operations: Traditional monitoring reacts to issues post-factum, whereas AI systems predict potential failures, preventing downtime and optimizing resources. Integration of AI-Driven Intelligence: AI systems provide insights and corrective actions before issues escalate, enhancing operational efficiency. AI-Powered Full-Stack Observability: AI correlates logs, traces, and metrics to detect anomalies across multiple data sources. Flexible Pricing Models: New observability providers are offering pay-as-you-go models to increase cost control. Observability Automation: AI-powered automation simplifies troubleshooting processes, making them faster and more accurate. AI Observability for AI Workloads: As AI adoption increases, observability platforms are being enhanced to effectively monitor AI workloads. OpenTelemetry as the Default Standard: OpenTelemetry provides a unified framework to simplify observability in multi-cloud systems. Security-Integrated Observability: As cyber threats evolve, security measures are being integrated into observability tools. Multidimensional Observability: Organizations are expanding their observability frameworks to include cost, compliance, and security metrics. As organizations continue to embrace observability, SSOJet offers secure Single Sign-On (SSO), Multi-Factor Authentication (MFA), and user management solutions to enhance security protocols. SSOJet’s API-first platform features directory sync, SAML, OIDC, and magic link authentication, ensuring secure access across various applications. Explore SSOJet’s services or contact us for more information at https://ssojet.com.

Apr 4, 2025 - 19:02

Enhancing UX and AI Initiatives through Observability Trends 2025

Originally published at ssojet

Insights from Honeycomb at KubeCon

During KubeCon Europe, Christine Yen, CEO and co-founder of Honeycomb, highlighted how observability can improve the user experience in systems that incorporate Large Language Models (LLMs). Yen emphasized that while software engineers are familiar with "black boxes," LLMs introduce new complexities due to their unpredictable behavior.

Yen noted that the introduction of LLMs changes software development methodologies. Current practices focus on deterministic properties, but LLMs capture the unpredictable nature of language. She stated, "...most of us here today have been building software before this LLM boom... It’s testing, maintenance, tuning and debugging that comes afterwards... should we all give up and embrace prompt engineering? **No! That’s why we are here…"

Key methodologies for adapting to these changes include:

Continuous Deployment and feature flags for rapid feedback loops
Testing in production to embrace chaos
High cardinality metadata to reflect complexity
High dimensionality data for parameterized experiments
Service Level Objectives to prioritize user experience

Observability enables teams to manage unpredictability and incorporate feedback into development processes. As LLMs generate an infinite set of inputs and outputs, traditional testing methods become less effective. Instead, evaluations are necessary to understand LLM behavior under various conditions.

/filters:no_upscale()/news/2025/04/llm-observability/en/resources/10image2-1743679288254.png)
Yen expressed optimism about the future of software engineering and the critical role observability will play, especially with the rise of Generative AI.

Dynatrace's AI Observability Enhancements

Dynatrace announced advancements in AI observability to support Generative AI initiatives. Their updated platform provides comprehensive insights into AI applications, enhancing reliability, performance, security, and compliance.

Key features include:

Enhanced LLM Model Analytics to monitor KPIs like input and output errors and predict cost changes
LLM Input and Output Guardrails to safeguard AI application quality
Multi-model Tracing for end-to-end observability of complex systems
Responsible AI Integrations for comprehensive audit trails

Dynatrace emphasizes the need for observability in AI systems to ensure they are implemented securely and provide a high return on investment. Companies like FreedomPay utilize Dynatrace to enhance their analytics capabilities.

Stephen Elliot from IDC reinforced that AI observability is essential for deploying reliable and cost-effective AI solutions.

Observability Trends in 2025

Observability has evolved significantly, driven by the increasing complexity of cloud-native environments. Modern observability frameworks integrate AI, automation, and security, moving beyond traditional monitoring methods.

Key trends identified include:

Cost Reduction through Data Management: Companies are adopting smarter data collection methods to minimize storage costs. By sampling key traces and reducing unnecessary data, organizations can cut costs by 60-80%.
AI-Driven Predictive Operations: Traditional monitoring reacts to issues post-factum, whereas AI systems predict potential failures, preventing downtime and optimizing resources.
Integration of AI-Driven Intelligence: AI systems provide insights and corrective actions before issues escalate, enhancing operational efficiency.
AI-Powered Full-Stack Observability: AI correlates logs, traces, and metrics to detect anomalies across multiple data sources.
Flexible Pricing Models: New observability providers are offering pay-as-you-go models to increase cost control.
Observability Automation: AI-powered automation simplifies troubleshooting processes, making them faster and more accurate.
AI Observability for AI Workloads: As AI adoption increases, observability platforms are being enhanced to effectively monitor AI workloads.
OpenTelemetry as the Default Standard: OpenTelemetry provides a unified framework to simplify observability in multi-cloud systems.
Security-Integrated Observability: As cyber threats evolve, security measures are being integrated into observability tools.
Multidimensional Observability: Organizations are expanding their observability frameworks to include cost, compliance, and security metrics.

As organizations continue to embrace observability, SSOJet offers secure Single Sign-On (SSO), Multi-Factor Authentication (MFA), and user management solutions to enhance security protocols. SSOJet’s API-first platform features directory sync, SAML, OIDC, and magic link authentication, ensuring secure access across various applications.

Explore SSOJet’s services or contact us for more information at https://ssojet.com.