As the demand for compute, storage, and system operations grows, DevOps, engineering, and security teams face increasing pressure to manage, review, and interpret massive volumes of operational and security logs. This surge in data creates significant noise—from infrastructure alerts to security events—which can obscure true signals and increase the likelihood of missing critical performance issues or threats. High-volume sources such as firewalls, intrusion detection systems, CDNs, and proxies can quickly overwhelm both systems and budgets, making it harder to maintain visibility and respond effectively. This article explores the rise in log volumes, the unique optimization challenges faced across security and engineering workflows, the importance of proactive planning, and key use cases for tools and solutions that help clarify, isolate, and prioritize the events that matter most.
What is log volume control and optimization?
Log volume control focuses on shaping log data before it reaches storage and analytics systems. By filtering, sampling, enriching, and routing logs at the source, teams can reduce ingestion costs while keeping the signals that matter for troubleshooting, security, and reliability. As systems grow more distributed and log volumes rise, controlling logs earlier in the pipeline has become a core observability and security practice—helping teams preserve visibility, respond faster to issues, and keep spend predictable without dropping critical data.
Why is log volume optimization important?
Log volume optimization is essential to reduce storage and processing costs and avoid performance bottlenecks. It also enables faster, more effective troubleshooting by emphasizing relevant data so systems and personnel won’t be overwhelmed by noise from alerts and lower-priority issues. This approach can also help ensure security, compliance, and resource efficiency. Without optimization, excessive logging can lead to wasted expenses, system slowdowns, and missing vital security threats or operational problems.
Some specific key pain points include:
Excessive costs: Ingestion-based pricing models incur high costs to store and analyze large volumes of logs.
Operational noise: Redundant or low-value events can hide or obscure important insights.
Manual tuning: Creating and maintaining filters for each log source can require specialized expertise and can slow response times to issues.
Inconsistent formats: Logs from disparate vendors are difficult to standardize for analysis.
The benefits of applying log volume optimization techniques include:
Cost efficiency: Filter or sample data before ingestion to reduce SIEM and storage expenses.
Noise reduction: Eliminate repetitive or low-value events while preserving vital security logs.
Operational simplicity: Use preconfigured observability pipeline packs from solution providers to begin data optimization right away.
Standardization: Ensure uniform formatting and enrichment across various log sources.
Scalability: Seamlessly update configurations as log sources or policies change.
Another benefit of log volume optimization is managing security and compliance, which helps organizations stay compliant with data residency laws, support data loss prevention, and protect sensitive data (such as personally identifiable information [PII] and credit card data).
What are the key components for log volume optimization?
Telemetry pipelines are vendor-neutral tools that gather, process, and forward logs, effectively managing and streamlining log flow within the logging ecosystem. These pipelines enable teams to have detailed control over how observability data, including logs, is gathered, transformed, directed, and archived.
The key components for telemetry pipelines include:
Processors parse, enrich, and filter logs using rules (for example, removing redundant traffic).
Routing routines send logs to multiple destinations, such as SIEMs, S3, or data lakes. Logs can be routed directly to inexpensive object storage—for example, S3 buckets—for long-term retention, enabling later rehydration or access.
Packs consist of preconfigured sets of processors and filters tailored for specific log sources (e.g., Amazon Web Services Virtual Private Cloud Flow Logs, Palo Alto Firewall, and Cloudflare).
Metrics monitor reduction ratios, dropped log volume, and savings in downstream ingestion to evaluate their impact on the log volume ecosystem.
For more information about log-volume optimization processes and components, refer to Datadog’s article, “How to optimize high-volume log data without compromising visibility.”
Which teams gain from optimizing log volume, and what are some of the most relevant use cases?
Log-optimization practices can greatly benefit teams associated with DevOps, site reliability engineering (SRE), and security, and development teams responsible for observability, compliance, or SIEM cost control. Examples include:
DevOps and SRE teams can deploy log optimization processes to quickly pinpoint root causes of failures (mean time to detection [MTTD]/mean time to recovery [MTTR] reduction), monitor system health in distributed environments, ensure application stability, and automate issue responses.
Security teams improve their ability to identify intrusion attempts, track user activity, generate audit trails for incident response, and ensure compliance with security policies and data regulations.
Development teams can debug complex application behaviors, understand user flows, pinpoint performance bottlenecks in the code, and receive real-time feedback on new releases.
One solution for log optimization and noise reduction at scale is Datadog Observability Pipelines, referred to as packs. Packs apply best-practice rules to control log flow and optimize telemetry. Packs greatly simplify log volume management, are scalable, and are customizable.
Observability Pipelines packs can be deployed for the following use cases:
Firewall noise management can use the Datadog Palo Alto pack’s observability pipelines to filter out low-signal threat logs, process log data near their source, convert logs into uniform formats for easier analysis, and minimize data ingestion costs.
Web-proxy optimization deploys observability pipelines to filter routine Zscaler logs, thereby lowering ingestion costs, enriching telemetry data with context such as application, credential, or other tags to facilitate faster troubleshooting, and retaining suspicious activity for investigations.
Cloud telemetry control involves observability pipelines to eliminate unnecessary AWS VPC Flow Logs for denied connections while preserving critical information. The pipeline pack filters out low-value, noisy, or redundant data, transforms logs into a uniform, structured format, and incorporates centralized, intelligent routing.
Data lake optimization can utilize an observability pipelines pack that routes normalized logs downstream to minimize transformation costs, standardize formats, and mask sensitive information. The pipeline pack also employs pre-aggregation and dynamic scaling to ensure cost efficiency by paying only for actual processing time, not idle periods.
Compliance management can use a pre-built, reusable set of rules and configurations to automatically collect, transform, and mask sensitive data (such as PII), route logs, metrics, traces, and archive data. This helps meet regulatory standards such as General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the Payment Card Industry Data Security Standard (PCI DSS). Deploying observability pipeline packs also reduces costs, provides audit trails, and maintains data governance across cloud and hybrid environments, supporting security teams enforcing policies and managing risks.
What challenges are associated with optimizing log volumes?
Organizations encounter log-optimization challenges such as large volumes of data, irrelevant or redundant data, disparate formats, high storage and processing costs, integration difficulties, and alert fatigue. These issues can impair effective monitoring, security, and troubleshooting in complex hybrid environments, making it difficult to identify critical signals amid the clutter. Other challenges include:
Explosive telemetry growth: All network and application layers now emit logs continuously, driven by AI and large language model (LLM) adoption that generates high log volumes. Modern distributed systems, such as microservices and cloud environments, can produce petabytes of logs, often overwhelming traditional systems.
Cost-sensitive observability: Expenses for SIEM and data lakes have become significant operational hurdles. Large volumes of data lead to rapidly rising cloud storage and processing costs due to ingestion-based pricing. Meanwhile, limited budgets restrict investment in advanced tools and hardware.
Shift left in observability: Teams now preprocess data before ingestion rather than relying solely on downstream filtering.
Adoption of open standards: Expanding use of standards such as Open Cybersecurity Scheme Framework (OCSF) and Elastic Common Schema (ECS) increases the need for standardized, structured logs. Integrating log solutions across diverse IT environments, whether on-premises or multi-cloud, can introduce complexity. Ensuring consistent instrumentation (for example, OpenTelemetry) across these environments remains a difficult task.
What log volume–optimization features should users look for?
To optimize log volume, users should control ingestion and ensure only valuable data is stored and analyzed, preventing unnecessary costs and complexity. Additionally, teams should look for the following capabilities:
Prebuilt packs provide convenient, ready-to-use logic for popular sources: Cloudflare, AWS, Zscaler, and Palo Alto firewalls.
A visual configuration user interface (UI) allows teams to create and personalize pipelines easily without needing YAML or coding.
Field normalization guarantees uniformity across all subsequent systems, facilitating quicker queries and investigations.
Flexible routing distributes filtered logs to multiple destinations (for example, SIEMs, data lakes, and S3) at the same time.
In-line customization provides edit filters and rules directly in the UI to meet organizational policies.
Metrics and monitoring track dropped event percentages and cost savings to demonstrate return on investment (ROI).
In summary, log volume optimization is the practice of reviewing and filtering log data, separating repetitive low-level alerts from critical operational and security issues, while reducing log noise and data storage costs. Observability Pipelines enable teams to collect, transform, and route logs anywhere: on-premises, in the cloud, or across hybrid environments. Datadog provides Observability Pipeline packs that apply Datadog’s best-practice logic, enabling teams to preview, customize, and extend log data and alerts directly in the UI.
Learn more
Discover more about controlling log volume while reducing noise with Datadog’s guide to controlling log volumes with Datadog Observability Pipelines.