Choosing the best Cribl deployment model means methodically evaluating your organization’s needs, technical limitations, and business goals. This guide offers a framework to help you make that decision.
The following table provides a high-level overview of the key differences between the available Cribl deployment models.
| Criterion | Cribl.Cloud | Customer-managed | Hybrid |
|---|
| Setup Time | Minutes to hours | Weeks to months | Days to weeks |
| Operational Overhead | Minimal | High | Medium |
| Data Sovereignty | Limited control | Full control | Configurable |
| Scalability | Provision extra Worker Nodes via the UI | Manual scaling includes adding new infrastructure | Mixed approach |
| Infrastructure Investment | Operating expense model | Capital expenditure + Operating expenses | Mixed model |
| Compliance Flexibility | Provider-dependent (AWS/Azure) | Full customization | Selective control |
| Multi-Cloud Support | Native AWS/Azure supported regions | Any environment | Best of both |
| Update Management | Automatic | Manual coordination | Mixed approach |
This table details the key criteria for selecting a deployment model, outlining which option is best for specific scenarios.
Criteria | Cribl.Cloud | Customer-Managed | Hybrid |
|---|
| Compliance Requirements | Choose when:- Standard compliance frameworks (e.g., SOC 2, ISO 27001) are sufficient.
- Data residency is permitted within approved cloud regions.
- The priority is to reduce compliance management overhead.
| Choose when:- Data sovereignty laws mandate local processing.
- Regulatory frameworks prohibit cloud data processing.
- Air-gapped environments are required, or custom compliance controls are necessary.
| Choose when:- There are mixed compliance requirements across data types or business units.
- A gradual migration to cloud compliance is being pursued.
- Selective data sovereignty requirements exist.
|
| Performance & Operational Needs | Choose when:- Rapid deployment and time-to-value are critical.
- Operational resources for infrastructure management are limited.
- Ease of scaling and updates are preferred.
- Network latency to cloud regions is acceptable.
| Choose when:- Ultra-low latency processing is required.
- Existing infrastructure investments need to be maximized.
- Full operational control and custom hardware acceleration are necessary.
| Choose when:- Performance requirements vary across different use cases.
- Local data processing with additional cloud monitoring and control.
- A gradual migration from on-prem to cloud is in progress.
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| Cost Optimization Factors | Choose when:- A predictable OpEx model aligns with budgeting preferences.
- The costs of managing internal infrastructure exceed cloud service premiums.
- Rapid scaling without significant capital investment is a priority.
| Choose when:- Existing infrastructure can be leveraged effectively.
- The long-term total cost of ownership favors capital investment.
- Data volumes make cloud processing costs prohibitive.
| Choose when:- Cost optimization is possible by placing workloads in the most economical environment, as close to the source of data as possible to reduce egress costs.
- Different business units have varying cost models. Risk distribution across cost models is desired for stability.
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