Splunk Observability: Key capabilities and differentiators
The Splunk Observability architecture is purpose-built to help organizations achieve digital resilience, accelerate innovation, and control costs in increasingly complex, distributed environments. The platform’s unified design delivers on three core differentiators that set Splunk apart and ensure teams can focus on what matters most.
1. Deeper business context to prioritize what matters
Splunk Observability enables organizations to move beyond infrastructure and application health, providing visibility into the business impact of every performance problem. The architecture is designed to correlate telemetry from applications, infrastructure, and both owned and unowned networks — making it easy to map technology health to business processes, user experiences, and outcomes.
- Curated business insights: Group backend services and visualize business processes (e.g., checkout, order fulfillment, loan processing) to monitor what matters most.
- Business journey mapping: Track multi-step workflows and user flows across the stack, identifying and prioritizing issues by business impact.
- Custom KPI support: Add business context to telemetry on-the-fly, leveraging flexible tagging (such as user or store ID) and custom metrics for granular visibility into how incidents affect revenue, customer segments, and key operations.
- Comprehensive environment coverage: Full visibility and correlated insights across all environments — networks, infrastructure, and applications — regardless of deployment model.
This deep business alignment means teams can prioritize issues by real-world impact, accelerate decision-making, and ensure resources are focused on outcomes that drive value.
2. AI-powered detection and investigation of business-impacting issues
At the core of Splunk Observability is a real-time, AI-powered analytics engine that streamlines detection, investigation, and remediation of incidents across the digital landscape. The architecture integrates high-speed telemetry processing, schema-on-read flexibility, and advanced machine learning to eliminate noise and surface what matters most.
- Real-time analytics at scale: Stream and analyze telemetry data from across the stack in seconds, supporting modern, high-velocity environments.
- AI/ML-driven anomaly detection: Leverage agentic AI and built-in machine learning to spot early signs of trouble, detect patterns, and predict incidents before they escalate.
- Root cause analysis and guided workflows: AI-guided troubleshooting quickly isolates sources of complex, cascading issues — including those spanning applications, infrastructure, networks, and AI/ML workloads.
- Unified incident response: Correlate related alerts from any source into a single, actionable view and automate workflows for faster recovery.
By integrating AI-powered insights throughout the platform, Splunk Observability helps teams minimize alert fatigue, reduce time spent in war rooms, and resolve business-critical incidents with speed and confidence.
3. Predictable pricing and control over your data and costs
The Splunk Observability architecture is designed to scale efficiently, ensuring organizations only pay for what they need while maintaining complete control over their data. Open standards, flexible data management, and native pipeline controls deliver transparency and choice.
- OpenTelemetry-native ingestion: Collect and instrument telemetry data using open standards, eliminating the need for proprietary agents and reducing technical debt.
- Flexible data pipeline management: Transform, filter, aggregate, and route telemetry data at ingestion, enabling organizations to efficiently manage growing data volumes without runaway costs.
- Federated analytics and storage: Analyze data wherever it resides, even in low-cost storage, without centralizing everything.
- Predictable billing models: Simple, scalable pricing (including host-based and flexible usage options) avoids punitive overages and budget surprises, supporting cloud, on-premises, and hybrid deployments.
With these architectural foundations, Splunk Observability ensures organizations can scale their observability practice confidently, maximize ROI, and maintain control over both data and spend.
Popular use cases of Splunk Observability
Use case: Troubleshooting and root cause analysis (RCA)
Definition: Splunk Observability empowers organizations with AI-driven detection, diagnosis, and rapid response to performance problems across applications and infrastructure.
Technical overview: Splunk consolidates high-volume, heterogeneous machine data, including unstructured logs, metrics, and traces, into actionable insights using a schema-on-read approach. Splunk Platform (Enterprise/Cloud) and IT Service Intelligence (ITSI) provide at-scale ingestion, filtering, and transformation of virtually any data source, including third-party and Cisco integrations. Advanced AI/ML models correlate alerts, identify root causes, and guide teams to resolution with business context and automation.
Key capabilities
- Alert centralization and reduction
- Unified ingestion and correlation of alerts from Splunk, third-party, and event management tools.
- Event iQ and Adaptive Thresholding use AI/ML for dynamic alert grouping, noise reduction, and seasonality adjustment.
- Custom Threshold Windows allow proactive tuning for known business events (e.g., Black Friday).
- Automated root cause analysis and incident response
- AI-directed troubleshooting surfaces probable causes and affected services in unified dashboards.
- Episode Review provides context-rich timelines, historical remediations, and links to related tickets.
- Automation via email, scripts, and Splunk SOAR; bi-directional ticketing and custom runbooks accelerate response.
- Application and infrastructure troubleshooting
- Real-time, sub-3-second telemetry refresh for metrics, logs, and traces.
- Business Transactions, Service Maps, Tag Spotlight, Trace Analyzer, and Call Graphs for workflow visualization and deep-dive analysis.
- Unified telemetry (RED metrics, infra dashboards, service-centric views) with instant cross-linking via Related Content.
- AI-directed troubleshooting
- Guided workflows across logs, metrics, traces, and entity health, prioritized by business impact.
- AI-generated summaries for grouped alert “episodes,” with actionable insights and next steps.
How it works
- Ingests metrics, logs, and traces from cloud, on-prem, and third-party sources with OpenTelemetry and Splunk-native connectors.
- Correlates and groups alerts with AI/ML to reduce noise and identify critical incidents.
- Surfaces probable root causes and impacted services in a unified interface.
- Guides engineers through investigation and remediation using contextual data, historical episodes, and visualizations.
- Automates response actions and enables cross-team collaboration with real-time shared data.
Example use cases
- Diagnosing service degradation in a Kubernetes-based microservices environment.
- Tracing application latency to a specific backend dependency in a hybrid cloud deployment.
- Investigating failed business transactions across distributed workflows by correlating logs and traces.
Outcomes
- Reduced alert fatigue and faster incident triage.
- Shortened MTTD and MTTR for critical incidents.
- Enhanced collaboration between IT operations, SRE, and engineering teams.
- Improved reliability and uptime for business services.
Why it matters: Rapid, accurate detection and resolution of issues minimizes downtime, reduces operational overhead, and helps teams maintain service reliability and customer trust.
Edge cases and considerations
Proactively detects and preempts alert storms using ITSI Content Packs. Supports hybrid, multi-architecture (n-tier, microservices, COTS) environments. Log Observer Connect enables advanced cross-platform troubleshooting without redundant log ingestion.
Use case: Monitoring critical business processes
Definition: With Splunk Observability, teams gain real-time visibility into the impact of performance issues on business processes, KPIs, and mission-critical workflows.
Technical overview: Splunk Platform and ITSI deliver live, customizable dashboards (Glass Tables) that correlate IT, application, network, and business service data. These dashboards ingest both digital and non-digital metrics, supporting a wide range of stakeholders. AppDynamics and Content Packs provide deep monitoring and rapid onboarding for commercial and SaaS apps (e.g., SAP, M365), mapping technical performance to business impact.
Key capabilities
- Unified business service visibility
- Glass Tables visualize real-time health of assets, KPIs, and business entities, spanning owned/unowned networks and diverse architectures.
- Service Analyzer offers color-coded, topological health views of services and infrastructure.
- Service health analytics
- Rapid correlation of logs, metrics, and traces enables fast dependency and impact analysis.
- Drill-down to KPI/entity level for issue isolation; historical baseline comparison highlights trends.
- COTS & SAP application monitoring
- SAP monitoring via AppDynamics (deep code-level via Java/ABAP agents) and ITSI (PowerConnect for ABAP telemetry).
- Out-of-the-box Content Packs for SAP, M365, and other business apps enable fast deployment and standardized metrics.
- Continuous improvement and reporting
- Built-in analytics for baselining and tracking MTTD, MTTR, and alert noise.
- Tracks progress on custom KPIs for IT and business stakeholder reporting.
- Business Performance Analytics Dashboards and Release Validation connect technical and business metrics (e.g., conversion, revenue) for executive oversight.
- Business process mapping and KPI customization
- Business Journeys in AppDynamics map end-to-end workflows, correlating KPIs to user experience and business outcomes.
- Unlimited custom metrics/tracking (e.g., user/store ID, customer segment) for granular business impact analysis.
How it works
- Ingests telemetry and business data from apps, infrastructure, and third-party tools.
- Maps services and business processes using Glass Tables, Service Analyzer, and Business Journeys.
- Correlates IT metrics with business KPIs/SLOs for comprehensive business impact analysis.
- Surfaces real-time alerts and trends relevant to both technical and business stakeholders.
- Enables continuous improvement by baselining, tracking, and reporting on key metrics.
Example use cases
- Monitoring the impact of IT incidents on revenue-generating workflows.
- Tracking the health of SAP business transactions and identifying process slowdowns.
- Analyzing service-level performance against SLA commitments for critical business units.
Outcomes
- Faster identification of business-impacting incidents.
- Enhanced reporting for operational and executive stakeholders.
- Improved alignment between IT performance and business results.
Why it matters: Understanding how IT and application performance impacts business outcomes enables teams to prioritize the most important issues, protect revenue, and ensure seamless user experiences.
Edge cases and considerations
Supports both digital and non-digital KPIs (e.g., hospital bed availability, physical asset status). Integrates with legacy (3-tier) and modern (cloud-native, microservices) environments. Rapid onboarding and best-practice metrics via Content Packs for SaaS and COTS apps.
Use case: Understanding critical user journeys
Definition: Splunk Observability provides end-to-end visibility into every step users take across web and mobile apps, APIs, networks, and backend services.
Technical overview: Splunk Observability Cloud and AppDynamics unify Real User Monitoring (RUM), Synthetic Monitoring, Application Performance Monitoring (APM), and network observability, including ThousandEyes, to deliver correlated insights into technical health and business impact. This approach enables teams to understand, monitor, and optimize every stage of digital user journeys, spanning front-end, back-end, external APIs, and network paths.
Key capabilities
- Complete digital experience monitoring
- Combines RUM, Synthetic Monitoring, APM, and network observability for a comprehensive view of user journeys.
- Captures telemetry from browsers, mobile apps, APIs, backends, and cloud infrastructure in real time.
- User journey mapping and visualization
- Experience Journey Maps in AppDynamics visualize user flows and friction points.
- Session replay, heatmaps, and path analytics reveal where users succeed or struggle.
- Proactive detection and network path analysis
- Synthetic Monitoring validates user journeys 24/7 from global/private locations, detecting regressions before deployment.
- ThousandEyes integration maps hop-by-hop network health (packet loss, DNS, BGP) to user transactions.
- Root cause analysis
- No-sample distributed tracing and ML-driven anomaly detection enable rapid identification of issues across the full stack.
- AI-assisted RCA pinpoints whether problems stem from code, microservices, CDN, or external events.
- Business outcome correlation and collaboration
- Dashboards tie technical health to business KPIs (conversion, revenue, satisfaction).
- SLO/SLA tracking and a unified workspace support cross-team collaboration (ITOps, SRE, NetOps, product).
How it works
- Collects telemetry from all app/network tiers using OpenTelemetry, RUM, APM, and synthetic tests.
- Correlates frontend/backend performance with user interactions and business KPIs.
- Visualizes user journeys and friction points through dashboards, journey maps, and session analytics.
- Enables root cause analysis by tracing user transactions across distributed systems and network paths.
- Supports ongoing optimization by identifying and prioritizing issues affecting key user segments.
Example use cases
- Diagnosing slow checkout flows in an e-commerce platform spanning multiple APIs and network hops.
- Identifying how network latency or third-party API failures impact user experience in a SaaS application.
- Prioritizing fixes for workflows impacting high-value or gold-tier customers.
Outcomes
- Faster resolution of user-impacting issues.
- Optimized digital experiences and improved customer satisfaction.
- Enhanced ability to tie technical performance directly to business results.
Why it matters: End-to-end visibility into user experiences empowers organizations to quickly identify and address friction points, optimize digital journeys, and increase customer satisfaction and retention.
Edge cases and considerations
Supports troubleshooting in hybrid/public cloud and across third-party APIs. Embedded network visualizations isolate root cause outside the user’s perimeter. Handles highly distributed, complex user journeys across digital and physical touchpoints.
Definition: Splunk Observability enables proactive improvement of application and infrastructure reliability, resource efficiency, and user experience across hybrid and cloud-native environments.
Technical overview: Splunk provides observability and optimization across both traditional (n-tier, COTS) and cloud-native (microservices, containers) environments. Combining AlwaysOn Profiling, real-time infrastructure monitoring, SLO-based alerting, and predictive analytics, Splunk enables continuous performance optimization and cost management.
Key capabilities
- Continuous profiling (AlwaysOn Profiling)
- Captures per-function/line CPU and memory usage in production, pinpointing bottlenecks and memory leaks.
- Infrastructure optimization
- Monitors CPU, memory, storage, and network usage for servers, containers, and cloud resources.
- Highlights under/over-provisioned resources and correlates infra metrics with app performance for right-sizing.
- SLO-based performance monitoring
- Defines and tracks Service Level Objectives (SLOs); uses burn-rate analytics to forecast and prevent service degradation.
- Synthetic monitoring
- Continuously tests availability and performance from multiple global locations, catching issues before users are affected.
- Cost-effective: $1/10,000 API tests, scalable for enterprise use.
- ML-driven analytics (AppDynamics & ITSI)
- Adaptive thresholding and predictive analytics forecast and prevent performance degradations.
- Reduces false positives and surfaces early anomalies for preemptive remediation.
How it works
- Continuously profiles application code and infrastructure resource usage with AlwaysOn Profiling and real-time infra monitoring.
- Sets baselines and adaptive thresholds using ML-driven analytics.
- Monitors SLOs and alerts on deviations from reliability targets and performance baselines.
- Integrates synthetic and real user testing data for end-to-end validation.
- Provides actionable recommendations for workload right-sizing and application optimization.
Example use cases
- Detecting and resolving memory leaks in a Java microservice.
- Optimizing cloud resource allocation to reduce infrastructure spend.
- Forecasting and preventing performance degradation before a high-profile product launch.
Outcomes
- Increased application and infrastructure efficiency.
- Reduced operational costs and improved scalability.
- Enhanced user experiences through consistently high performance.
Why it matters: Proactive performance tuning and resource optimization reduce costs, prevent outages, and ensure consistently high-quality experiences for users and customers.
Edge cases and considerations
Supports hybrid application stacks (n-tier, COTS, microservices). OpenTelemetry-native — no vendor lock-in or proprietary agents required. Scalable for both legacy and cloud-native environments.
Use case: Optimizing observability costs
Definition: Splunk Observability gives organizations the tools to efficiently manage telemetry volumes and spend, supporting open standards and ensuring predictable, flexible pricing.
Technical overview: Splunk’s platform and flexible pricing models help organizations manage data at scale, avoid vendor lock-in, and optimize the value of observability. Advanced data management, pipeline control, and cost optimization tools enable granular oversight of telemetry collection, storage, and spend.
Key capabilities
- OpenTelemetry-native data ingestion: Unified collection via SDKs, APIs, and tools; eliminates the need for proprietary agents and supports one-time ingestion for multi-use telemetry.
- Metrics pipeline management: Aggregates, filters, archives, and drops unwanted metrics; pipeline automation identifies unused/low-value metrics for archival (archived metrics cost 10x less).
- High-cardinality control: Token limits per team/service; analytics to identify high-volume tokens and optimize metric storage/usage.
- Histogram metrics: Compresses high-volume metrics into granular, actionable insights for efficient trend analysis.
- Data routing, filtering, and transformation: Ingest Processor and Edge Processor enable SPL2-based filtering, masking, enrichment, and routing at ingest and at the network edge.
- Retention and federated search: Fine-grained controls for retention; unified search across multiple Splunk environments without central ingestion.
- Cost monitoring and optimization tools: Built-in AWS EC2 Cost Optimizer, dashboards, and alerts for billing thresholds.
- Predictable, transparent pricing: Flexible models (by host, workload, ingestion, entity, activity) with no punitive overages.
How it works
- Ingests, processes, and routes telemetry using OpenTelemetry and Splunk-native data management tools.
- Applies pipeline automation to aggregate, filter, and archive metrics and logs based on usage and value.
- Enables cost monitoring and optimization via dashboards, alerts, and built-in cost analysis tools.
- Provides visibility and governance for storage, retention, and compliance with policies.
- Integrates with both cloud and on-premises environments for unified, scalable observability cost management.
Example use cases
- Reducing monitoring costs by filtering low-value metrics from ingestion pipelines.
- Managing telemetry volumes and retention for compliance with regulatory and business policies.
- Optimizing AWS EC2 resource monitoring to avoid overages and control cloud costs.
Outcomes
- Lower, more predictable observability costs.
- Scalable data management without loss of critical insights.
- Enhanced control over telemetry collection, retention, and billing.
Why it matters: Efficiently managing telemetry volumes and spend allows organizations to scale observability while controlling costs, maximizing ROI, and avoiding expensive overages.
Edge cases and considerations
Supports showback/chargeback for granular cost allocation across teams/services. Seamless log integration with Log Observer Connect. Designed for environments with high cardinality and variable telemetry growth.
Use case: Detecting and prioritizing application security vulnerabilities
Definition: Splunk Observability detects vulnerabilities and attacks in application code, prioritizing response based on actual risk and business impact.
Technical overview: Splunk Secure Application integrates application security with observability, delivering real-time vulnerability detection, protection, and risk-based prioritization. Leveraging existing APM agents and contextual analytics, Splunk enables teams to detect, prioritize, and remediate security threats with minimal operational overhead.
Key capabilities
- Integrated runtime security
- Continuous code scanning and runtime protection against exploits, leveraging existing APM/observability agents.
- Threat detection and mitigation directly within observability workflows.
- Contextual risk analysis
- Automated risk scoring based on business impact (e.g., critical payment flow vs. test environment).
- AI/ML-driven prioritization to surface actionable, high-impact vulnerabilities and minimize alert fatigue.
- Automated detection and blocking
- Real-time defense against evolving threats down to individual lines of code.
- Immediate feedback on security risk, correlated to user experience and business KPIs.
- Incident collaboration
- Shared dashboards and incident views for ITOps, Engineering, and SecOps.
- Tight integration with Splunk SIEM and SOAR for orchestrated response, escalation, and workflow tracking.
How it works
- Ingests telemetry and security data from application code, infrastructure, and business workflows using existing APM agents.
- Continuously scans for vulnerabilities and monitors runtime behavior using integrated threat intelligence and advanced analytics.
- Correlates security alerts with application context and business impact, prioritizing the most critical issues.
- Automates remediation actions and escalates incidents to security teams through SIEM/SOAR integration.
- Supports continuous improvement with ongoing monitoring and analytics.
Example use cases
- Detecting and blocking SQL injection attacks in production applications.
- Prioritizing remediation of vulnerabilities in high-value business processes (e.g., payment flows).
- Automating security event escalation and orchestrated response between IT and security teams.
Outcomes
- Faster vulnerability detection and reduced mean time to remediate (MTTR).
- Lower risk of data breaches and compliance violations.
- Improved alignment between security and operations for robust application defense.
Why it matters: Continuous, risk-based application security reduces the likelihood of breaches, speeds up remediation, and safeguards both business operations and customer data.
Edge cases and considerations
Supports both in-app and external attack vectors. Designed for minimal operational overhead (leverages existing observability agents, avoiding tool sprawl). Scales with hybrid and cloud-native architectures.
Use case: Correlating network domains
Definition: Splunk Observability and IT Service Intelligence (ITSI) assure network service health by unifying visibility and reducing alert noise across all network domains — including ThousandEyes, Catalyst Center, and Meraki.
Technical overview: Splunk Observability breaks down silos across IT, network, and application teams by providing a single, unified platform for monitoring and correlating health and performance data from owned and unowned networks, infrastructure, and business applications. With out-of-the-box integrations for Cisco and third-party sources, ITSI’s Event Analytics and content packs enable rapid onboarding, cross-domain alert enrichment, and advanced analytics, giving teams a comprehensive, real-time view of network and service health.
Key capabilities
- Unified network and service visibility
- Aggregate and correlate telemetry (metrics, logs, events, traces) from all domains — owned and unowned networks, infrastructure, and applications — in one place.
- Custom dashboards and Glass Tables visualize the health of assets, KPIs, and business-critical services for both technical and business stakeholders.
- Cross-domain alert correlation and noise reduction
- Group related alerts from disparate domains (Cisco, Meraki, ThousandEyes, third parties) to reduce noise and prioritize what matters.
- Enrich events with business context and automate incident prioritization to accelerate triage.
- End-to-end troubleshooting and contextual insights
- Rapidly isolate root causes and affected domains using correlated evidence, reducing MTTD and MTTR.
- Provide executive-level, real-time views that map technical performance to business KPIs and outcomes.
- Flexible, data-agnostic onboarding
- Easily integrate network, infrastructure, and application data from Splunk and external tools using Splunkbase content packs.
How it works
- Onboards and normalizes telemetry from networks (owned/unowned), infrastructure, and applications via ITSI and Splunk integrations.
- Correlates and groups alerts and events across all domains, enriching them with business and technical context.
- Surfaces unified dashboards for both technical teams and business stakeholders, displaying service and network health in real time.
- Guides teams to isolate domains, pinpoint root causes, and automate or escalate remediation.
- Supports continuous improvement by tracking reduction in alert fatigue, improved MTTD/MTTR, and business KPI impact.
Example use cases
- Reducing alert fatigue by grouping duplicate network and application alerts into a single actionable incident.
- Providing a real-time, executive-level dashboard for monitoring regulatory or operational KPIs (e.g., ambulance availability, wait times).
- Breaking down silos between network, app, and infra teams by giving everyone a unified view of service health and impact.
Outcomes
- Faster detection and resolution of incidents across the digital stack.
- Reduced operational overhead and alert fatigue.
- Clear prioritization based on business impact, not just technical symptoms.
Why it matters: Complete, cross-domain visibility and alert correlation minimize downtime, accelerate troubleshooting, and enable IT and business teams to focus on delivering resilient digital services.
Edge cases and considerations
Supports both digital and non-digital KPIs for highly regulated or critical environments. Data source agnostic — easily integrates with legacy and modern network infrastructure. Enables rapid onboarding and scaling via Splunkbase content packs and connectors.
Use case: Pinpointing network impact on app performance
Definition: Splunk Observability and ThousandEyes help teams troubleshoot application performance problems by correlating dependencies across owned and unowned networks in real time.
Technical overview: By integrating ThousandEyes with Splunk Observability Cloud and AppDynamics, organizations break down silos between ITOps, Engineering, and NetOps. Unified telemetry from application, infrastructure, and every network hop (internal and third-party) enables precise identification of root causes — whether in code, infra, or the network. Shared dashboards, end-to-end correlation, and continuous benchmarking empower teams to resolve issues faster and optimize digital experiences.
Key capabilities
- Unified end-to-end visibility
- Real-time correlation of app, infrastructure, and network telemetry, including third-party ISPs and cloud providers.
- Shared dashboards surface evidence for all teams, eliminating guesswork and siloed investigations.
- Cross-team collaboration and incident resolution
- Seamlessly bridges NetOps, ITOps, and Engineering with unified context for root cause analysis.
- Bi-directional integration with ThousandEyes enables precise network path analytics and performance benchmarking.
- Proactive monitoring and benchmarking
- Continuous monitoring detects degradations and tracks performance trends across all network domains.
- Enables vendor accountability and proactive service level management.
- Accelerated troubleshooting and MTTI
- Rapidly isolates whether the root cause is in code, infra, or external network.
- Reduces unnecessary escalations and improves mean time to innocence (MTTI).
How it works
- Integrates ThousandEyes bi-directionally with Splunk Observability and AppDynamics.
- Collects and correlates real-time telemetry from applications, infra, and all network domains (owned and unowned).
- Surfaces unified dashboards and alerts for all teams to investigate issues together.
- Provides network path analytics and continuous benchmarking to pinpoint issues and hold partners accountable.
- Enables proactive optimization and seamless digital experiences for users.
Example use cases
- Accelerating MTTI by instantly proving “network innocence” in multi-domain troubleshooting.
- Benchmarking network performance to anticipate disruptions and enforce SLAs with third-party partners.
- Identifying whether slow SaaS transactions are due to code changes, internal infrastructure, or an external ISP outage.
Outcomes
- Faster, more accurate incident resolution across app, infra, and network domains.
- Reduced mean time to innocence (MTTI) and fewer unnecessary escalations.
- Improved digital experience and business continuity.
Why it matters: Unified visibility across the entire digital delivery chain eliminates blind spots, accelerates root cause analysis, and empowers teams to deliver reliable, high-performing digital experiences.
Edge cases and considerations
Supports hybrid environments, including cloud, SaaS, and multi-ISP architectures. Enables both proactive and reactive network performance management. Scales for organizations with globally distributed or complex digital delivery chains.
Use case: Monitoring AI apps and infrastructure
Definition: Splunk Observability enables real-time monitoring of health, performance, and security across your entire AI application stack — including agents, LLMs, and AI infrastructure — ensuring reliability, efficiency, and business alignment.
Technical overview: As AI and LLM workloads become business-critical, Splunk Observability for AI delivers comprehensive monitoring for both application and infrastructure layers. With OpenTelemetry-native instrumentation, real-time dashboards, and seamless integration with Cisco AI Pods, Splunk provides actionable insights into resource utilization, model accuracy, security, and business impact — across all frameworks, agents, and environments. Integrated AI Agent Monitoring and AI Defense provide operational and security visibility for responsible, cost-effective, and high-quality AI.
Key capabilities
- AI infrastructure health and performance monitoring
- Monitors health, availability, and consumption of AI infrastructure (Cisco AI Pods, GPUs, vector databases, etc.).
- Data-dense dashboards correlate business performance with operational metrics (utilization, error rates, bottlenecks).
- Comprehensive LLM and agentic application monitoring
- Tracks and analyzes LLM/agent workflows, token utilization, latency, errors, drift, and hallucinations.
- Specialized evaluations monitor semantic quality and technical performance of model outputs.
- Integrated security and compliance
- Cisco AI Defense detects and protects against prompt injection, PHI leakage, and related security threats.
- Connects AI security risks with infrastructure and services for holistic governance and compliance.
- OpenTelemetry-native, vendor-neutral integration
- Flexible, agentless monitoring for all AI frameworks, avoiding vendor lock-in.
- Supports monitoring of workloads running on Cisco AI Pods and other environments.
- Continuous optimization and governance
- Automated benchmarking and real-time SLO tracking enable continuous performance and risk optimization.
- Governance features enforce compliance and accountability with regulatory and organizational standards.
How it works
- Instruments AI infrastructure and LLM/agent applications with OpenTelemetry and Splunk-native integrations.
- Collects and correlates metrics, events, logs, traces with networking and security telemetry in unified dashboards.
- Tracks AI resource utilization, performance, and security, surfacing actionable alerts and detectors for anomalies.
- Enables root cause analysis and optimization for cost, reliability, and business impact.
- Supports compliance and governance by monitoring both operational and accuracy metrics, and enforcing organizational policies.
Example use cases
- Detecting and troubleshooting inference failures or resource contention in multi-tenant AI infrastructure.
- Monitoring semantic drift, bias, or hallucinations in LLM-driven applications to protect business reputation.
- Enforcing compliance by tracking PHI leakage risks and regulatory KPIs in AI workloads.
Outcomes
- Lower operational and reputational risk with proactive monitoring and governance.
- Optimized resource usage and reduced cost for AI infrastructure.
- Improved reliability, performance, and security of AI-powered applications.
Why it matters: Comprehensive, unified monitoring of AI application stacks empowers organizations to build, deploy, and operate reliable, compliant, and cost-effective AI that aligns with business goals.
Edge cases and considerations
- Supports both cloud and on-premises AI deployments, including Cisco AI Pods and third-party infrastructure.
- Scales for large, distributed, and multi-framework AI environments.
- Integrates with specialized AI/LLM agent monitoring platforms for holistic oversight.
How teams use Splunk Observability: Role-based features & benefits
Beyond the core capabilities, Splunk Observability delivers tailored insights and benefits for specific roles and teams within an organization, enabling them to achieve their unique operational and business objectives.
IT operations and site reliability engineering (SRE) teams
Splunk Observability supports the needs of ITOps, SRE, DevOps, and business leaders by providing unified visibility and intelligence across digital services. The following role-based views show how different teams apply the portfolio in practice.
