To operate efficiently under network constraints, design your architecture to minimize the number of bytes crossing constrained links such as WAN, between data centers, cross-region, or cloud-egress paths. Cribl is often used as a reduction and routing layer (such as for dropping noise, removing redundant fields, and sending low-value data to cheaper Destinations), so treat data reduction as a first-class design objective.

Placement and Reduction Patterns

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These principles apply across all deployment models (on-prem, Cribl.Cloud, or hybrid). Maximize bandwidth efficiency by applying reduction and compression at the earliest possible stage of the data lifecycle.

• Process locally: Deploy Cribl Edge or Worker Groups near high-volume producers (firewalls, load balancers, Kubernetes clusters). Perform reduction and enrichment locally, forwarding only the required subset over expensive or slow links.
• Reduce early: Drop unneeded events (such as debug logs and health checks) and fields (such as large payloads, and verbose JSON) at the Source. Use Sampling or Aggregations Functions for high-frequency data to ensure only statistically useful data crosses the WAN.
• Compress everywhere: For hybrid (on-prem to Cribl.Cloud data flows), compress data early. Where Destinations support it (for example, Kafka/MSK, Kinesis, and object stores), enable appropriate codecs like Gzip, Snappy, or LZ4 to improve throughput and reduce wire size.

On-Prem Bandwidth Strategy

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In on-prem deployments and environments with multiple data centers, treat WAN and inter-data center links as constrained, shared resources. Keep raw volume on fast local networks and send only curated, compressed data across high-latency links.

Enable Compression on TCP-Based Destinations

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To minimize wire size and egress costs, enable compression for all TCP-based streaming Destinations where available. This reduces the network footprint of high-volume telemetry by zipping data packets before transmission.

• Object storage and search platforms: Use Gzip or Deflate for HTTP/REST Destinations (such as S3, Splunk HEC, Elasticsearch) to significantly reduce storage and transit costs.
• Message buses: Use high-speed codecs like Snappy or LZ4 for Kafka, MSK, or Kinesis to maximize throughput with minimal CPU overhead.

Common codecs include Gzip, Snappy, or LZ4, depending on the Destination capabilities and your CPU budget. Many Destinations expose these settings on configuration modals; see the individual Destination to verify.

Size Worker Groups for Headroom and Reliability

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Build a buffer into your infrastructure to ensure that peak utilization doesn’t lead to data loss or system instability.

• Plan for bursts: Log volume is rarely a flat line. Security incidents or software deployments can cause “log storms” that double or triple your normal volume. Aim for 50-70% target utilization to leave room for these bursts. The Sizing and Scaling guidance can help you translate ingest rates into Worker CPU, memory, and disk requirements.
• Accommodate failure: Always deploy at least one more Worker Node than is strictly necessary for your volume. This ensures that if one Node fails or undergoes maintenance, the remaining cluster can absorb the load without hitting a bottleneck. For details, see Worker Node Resiliency.
• Account for processing overhead: Features like heavy regex, decryption, or complex aggregations require more “compute tax.” Size your instances based on the complexity of your Pipelines, not just the raw GB/day throughput.

Cribl.Cloud Bandwidth Strategy

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When you use Cribl.Cloud as part of your architecture, you must manage both:

• Throughput between Worker Groups and downstream Destinations.
• Egress cost from Cribl.Cloud to external services (such as object stores, SIEMs, analytics platforms, or other SaaS tools).

Use Regional Worker Groups and Local Destinations

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• Prefer Cribl-managed Worker Groups in the same cloud region as your primary Destinations to avoid cross-region transfer charges and reduce latency.
• When possible, terminate high-volume Pipelines directly into regional object storage or analytics platforms (for example, S3, Azure Blob, or a regional SIEM endpoint) to keep traffic inside the region.

For more about Worker Group behavior and regional placement, see Manage Cribl.Cloud Worker Groups.

Compress High-Volume Destinations

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For any high-throughput Destination (such as object stores, Kafka, SIEMs, or observability platforms), enable compression to minimize egress bytes. Many streaming Destinations expose codec and batch-size options alongside PQ configuration; use these in combination to balance throughput, cost, and latency.

Hybrid Bandwidth Strategy

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Most enterprises converge on a hybrid model that mixes on-prem data centers, Cribl.Cloud, and multiple downstream platforms.

In hybrid topologies, the goal is end-to-end byte minimization across the entire data lifecycle:

1. From the Source to a local Edge Fleet or Worker Group: At this stage, raw volume is permissible because it stays on high-speed, low-cost local LAN links. This is the ideal place for high-fidelity parsing. You can apply initial normalization (timestamps, hostnames, parsing) and fast filters, but allow relatively higher volume on cheap LAN links.
2. From On-Prem to Cribl.Cloud: As data prepares to traverse the WAN or Internet, it must be reduced, enriched, and heavily compressed. This transition point is the most critical for cost control.
3. From Cribl.Cloud to SaaS Destinations: Once in the cloud, use compressed data streams to forward only the specific subsets required by each downstream platform (such as security logs to a SIEM or metrics to an observability tool).
4. Backpressure management: Use Destination-level backpressure settings (Block vs. Drop) to protect your cloud infrastructure from being overwhelmed by slow downstream SaaS endpoints. For details, see About Destination Backpressure Triggers.

Disaster Recovery and Archive Pipelines

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Disaster recovery (DR) and long-term retention often have different bandwidth and cost constraints than operational analytics. Treat these workloads as separate, explicitly designed Pipelines:

• Use cloud or on-prem object storage (such as Amazon S3, Azure Blob, Google Cloud Storage, or NFS) in whichever location minimizes the combined storage and bandwidth cost for your retention requirements.

• Create dedicated “archive” Pipelines with:

  • More aggressive reduction (field trimming, summarization, deduplication).
  • Longer batch windows and high compression ratios to maximize write efficiency.
  • Potentially different Destinations than your online analytics platforms (for example, cheap object storage rather than costly search indexes).

• In hybrid deployments, you can keep disaster recovery copies in-region with your Cribl-managed Worker Groups or in an on-prem object store, depending on where egress is cheapest and restore workflows are simplest.

The “archive” Pipelines can also be valuable for replay and investigations. For example, using Cribl Replay patterns, while keeping bandwidth and storage costs under control over multi-year horizons.