Ingext vs Cribl Stream
Detailed Analysis
Cribl Stream pioneered the concept of data routing for observability pipelines, enabling organizations to collect, transform, and reduce log and metric data before it reaches high-cost destinations.
Ingext builds upon that concept — designed from inception as a streaming data fabric for SIEM, observability, and compliance telemetry, combining inline transformation with continuous flow control and vendor-neutral routing.
Learn more: For a comprehensive explanation of data fabrics and their role in SIEM architecture:
Read: Why We Need a Data FabricThis page analyzes both platforms using the SIEM Data Fabric Evaluation Framework, which defines the essential characteristics of a true, real-time data fabric.
Evaluation Framework Recap
Stage 1 – Gate Requirements
Failing either gate disqualifies a platform as a streaming data fabric.
Transformation (Parsing)
Inline parsing, normalization, timestamp correction, and enrichment before storage. Ensures data becomes usable as it flows, not after ingestion.
Learn about SIEM architectureStreaming Continuity
Continuous, unpaused flow with buffering, retries, and rolling upgrades. Guarantees reliability and low latency even during bursts or outages.
Stage 2 – Technical Criteria
Routing
Conditional, multi-destination routing to SIEMs, archives, and data lakes. Enables tiered data delivery and cost-efficient control.
Learn more: Understanding the strategic decision between SIEM storage and data lakes directly impacts routing strategies.
Read: SIEM vs Data LakeFiltering / Dropping
Inline filtering or sampling to remove redundant telemetry. Reduces downstream cost and noise.
Output Versatility
Direct output to metrics systems, data lakes, and SIEMs. Allows one fabric to serve multiple analytic domains.
Processing Logic
Declarative or rule-based inline computation and enrichment. Adds real-time intelligence without post-processing.
Agnostic Deployment
Operates across cloud, hybrid, or on-prem with open interfaces. Prevents vendor lock-in and supports enterprise diversity.
Gate Evaluation
| Gate | Ingext | Cribl Stream | Commentary |
|---|---|---|---|
| Transformation (Parsing) | Inline parsing, normalization, and enrichment via declarative pipeline. | Strong transformation layer with support for data reduction, masking, and field mapping. | Both meet transformation gate; Ingext extends normalization beyond observability into SIEM and compliance formats. |
| Streaming Continuity | True continuous streaming with buffering, retries, and zero-downtime updates. | Real-time operation with internal replay and queue management. | Both maintain continuity; Ingext's flow architecture is purpose-built for multi-tenant security data. |
Gate Result: Both platforms pass.
Stage 2 Criteria Analysis
| Criterion | Ingext | Cribl Stream | Summary |
|---|---|---|---|
| Routing | Conditional multi-sink routing (SIEM, data lake, archive, metrics). | Flexible routing via YAML-based configuration and UI. | Comparable routing; Ingext adds hierarchical routing and tenant-level logic. |
| Filtering / Dropping | Inline rule-based filters and sampling with percentage or condition logic. | Data reduction and suppression rules configurable per stream. | Both handle filtering effectively; Ingext offers higher-level control with tag and tenant-based policies. |
| Output Versatility | Outputs to Splunk, Elastic, Sentinel, Prometheus, Parquet/S3. | Broad output support (Splunk, Elastic, S3, Datadog, Kafka). | Both versatile; Ingext focuses on SIEM integration, Cribl on observability tools. |
| Processing Logic | Declarative FPL-style inline logic for transforms and enrichment. | Transformation via YAML/Regex scripts; limited inline computation. | Ingext provides a richer logic model suited for security data correlation. |
| Agnostic Deployment | Full hybrid/on-prem/cloud deployment; no external dependencies. | Hybrid with reliance on Cribl.Cloud for orchestration and UI. | Ingext is fully self-contained; Cribl introduces mild cloud dependence for management. |
Derived Cost Efficiency
| Factor | Ingext | Cribl Stream | Insight |
|---|---|---|---|
| Data Reduction Ratio | 3–10 : 1 typical through pre-ingest filtering and routing. | 2–8 : 1 typical depending on rule complexity. | Comparable efficiency; Ingext favors deterministic policy control over YAML tuning. |
| Processing Efficiency | Linear scale under 5× burst; low-latency stream design. | Highly efficient, multi-threaded processing pipeline. | Both perform well; Ingext optimizes around consistent latency for security telemetry. |
| Effective Cost per Processed GB | Predictable tiered pricing; no per-module fees. | Volume-based pricing per GB ingested. | Ingext provides cost stability; Cribl costs increase with data growth. |
Summary of Findings
Ingext
- Passes both Gates and Stage 2 criteria.
- Offers full inline transformation, enrichment, routing, and filtering.
- Integrates equally across security, observability, and compliance data sources.
- Deployable on-prem, in the cloud, or hybrid with self-contained orchestration.
- Predictable cost model based on daily processing tiers, not GB expansion.
Ideal for: Enterprises and MSSPs managing diverse telemetry and multi-vendor SIEM architectures who need fine-grained control and cost predictability.
Cribl Stream / Edge
- Passes both Gate requirements with strong routing and reduction capabilities.
- Provides a polished user interface and mature ecosystem.
- Transformation and logic handled through YAML configuration rather than declarative scripting.
- Slight reliance on Cribl.Cloud for management and updates introduces cloud dependency.
- Volume-based pricing can escalate with high-throughput telemetry environments.
Ideal for: Organizations focused primarily on observability and log reduction within cloud-native environments.
Verdict
| Aspect | Ingext | Cribl Stream |
|---|---|---|
| Gate Compliance | ||
| Routing Flexibility | ||
| Filtering Control | ||
| Output Versatility | ||
| Processing Logic | ||
| Agnosticism | ||
| Overall Fit for SIEM Data Fabric |
Conclusion
Ingext and Cribl Stream both represent modern approaches to controlling data before it reaches downstream analytics.
The key difference lies in scope and governance:
Learn more: For insights on operationalizing data fabric capabilities effectively in your SOC:
Read: Run Your SOC like an MSSP- Cribl remains a leading choice for observability pipelines and log reduction.
- Ingext extends the concept into a true SIEM data fabric — continuously transforming and routing heterogeneous telemetry across hybrid environments with predictable cost control.