Optimize Observability Costs
How Splunk helps with this use case
Splunk helps teams manage large telemetry volumes more efficiently and only pay for what they need, without ever needing to worry about punitive overages based on peak usage. Because OpenTelemetry is natively built in as one of the ways we enable teams to collect data, teams can unlock real-time context from across their entire ecosystem using a common standard teams can agree on, giving them full control of their data. Additionally, the comprehensive set of flexible and scalable data management capabilities with Splunk Log Observer Connect, Splunk Edge Processor, and Splunk Ingest Processor helps teams understand what data is actually valuable and utilized, manage their telemetry volume more effectively, keep control of their costs, and optimize their performance.
Explore actionable guidance for this use case
Application Performance Monitoring
Observability Cloud
- Accelerating an implementation of OpenTelemetry in Splunk Observability Cloud
- Building a self-serve and scalable observability practice
- Deploying and troubleshooting OpenTelemetry successfully
- Managing data limits in Splunk Observability Cloud
- Managing observability configurations as code with the Splunk Observability Cloud Terraform provider
- Managing various limits in Splunk Observability Cloud
- Running the Splunk OpenTelemetry Collector on Darwin
- Setting up the OpenTelemetry Demo in Docker
- Setting up the OpenTelemetry Demo in Kubernetes
- Using Kubernetes Horizontal Pod Autoscaling
Splunk Distribution of the OpenTelemetry Collector
Detect and Prioritize App Security Vulnerabilities
Monitor Critical Business Processes
Optimize Observability Costs
Optimize Performance of Apps and Infrastructure
Troubleshoot and Conduct Root Cause Analysis
Understand the Critical User Journey