Kloudfuse vs. Datadog
Significant Cost Savings
Significant Cost Savings
Same Extensive Features
Same Extensive Features
No Vendor Lock-in
No Vendor Lock-in
Unified Observability
Experience all-in-one observability for metrics, logs, traces, digital experiences, and continuous profiling on a cost-effective, user-friendly platform that scales with your needs.
Affordable Pricing
Control your observability costs with Kloudfuse’s flat pricing. Eliminate egress fees, and benefit from efficient storage, data deduplication, and reduced retention costs.
Open Standards
Avoid vendor lock-in with OpenTelemetry support, open query languages, and embedded Grafana dashboards. Migrate using our dashboard and alert conversion kits, while keeping your existing agents.
Data Ownership & Security
Secure your observability data in your private cloud. With our data lake, your data is fully under your control, enabling AI applications, agentic workflows, and integration with other sources.
Unified Observability
100%
Unified across metrics, logs, events, traces, LLM, and more.
100%
Unified across metrics, logs, events, traces, LLM, and more.
Pricing
100%
Affordable and transparent, with a range of sizes to fit your needs.
0%
Expensive, unpredictable pricing model, and unexpended overages.
Security and Compliance
100%
Deployed in your VPC. Ideal for those with strict security, privacy, and sovereignty needs.
25%
SaaS, vendor-hosted cloud. Sensitive Data Scanner tool to identify/tag sensitive data.
Vendor Neutrality
100%
Open to collect from any agent with migration paths from any prior tool.
0%
Closed, with no migration path available.
Data Sources
100%
Over 700 sources.
100%
Over 700 sources.
Instrumentation and Collection
100%
Any agent (e.g., Datadog, NewRelic, OpenTelemetry, etc.). Does not require new instrumentation.
25%
Only Datadog agent. Requires non-standard, vendor-specific proprietary instrumentation.
Log Management
100%
Log fingerprinting and facet analytics automatically parse and deduplicate logs, enhance search speed, and reduce storage needs.
25%
High log volumes lead to increased costs, slow search and analysis, and limited retention periods.
Query Languages and Dashboards
100%
Familiar query languages (PromQL, TraceQL, LogQL, GraphQL, SQL), along with FuseQL, a rich query language for logs. Kloudfuse and open source Grafana dashboards.
25%
DDSQL. Proprietory, and requires learning and ramp time.
AI and ML Capabilities
100%
Advanced anomaly, outlier detection, forecasting, and correlation algorithms.
100%
Advanced anomaly, outlier detection, forecasting, and correlation algorithms.
Advanced Data Transformation
100%
Functions to improve query performance and reduce storage footprint (e.g., Metrics Rollup).
100%
Functions to improve query performance and reduce storage footprint (e.g., Metrics Rollup).
Data Retention
100%
Flexible, using any storage (e.g., cost-efficient S3 buckets).
50%
Datadog retention options range from 7 days to 15 months.
Ease of Deployment
75%
Simple deployment mode with built-in Control Plane to administer.
100%
SaaS-base product. Does not require hosting and administration.
Scalability
100%
Massively scalable without the associated costs.
75%
Scalable but costly at large scale.
Developer Community
25%
Starting to establish.
100%
Large developer community.
Use Cases
75%
Application, Infrastructure, Digital Experience, and LLM.
100%
Application, Infrastructure, Digital Experience, LLM, and Security.
Unified Observability
100%
Unified across metrics, logs, events, traces, LLM, and more.
100%
Unified across metrics, logs, events, traces, LLM, and more.
Pricing
100%
Affordable and transparent, with a range of sizes to fit your needs.
0%
Expensive, unpredictable pricing model, and unexpended overages.
Security and Compliance
100%
Deployed in your VPC. Ideal for those with strict security, privacy, and sovereignty needs.
25%
SaaS, vendor-hosted cloud. Sensitive Data Scanner tool to identify/tag sensitive data.
Vendor Neutrality
100%
Open to collect from any agent with migration paths from any prior tool.
0%
Closed, with no migration path available.
Data Sources
100%
Over 700 sources.
100%
Over 700 sources.
Instrumentation and Collection
100%
Any agent (e.g., Datadog, NewRelic, OpenTelemetry, etc.). Does not require new instrumentation.
25%
Only Datadog agent. Requires non-standard, vendor-specific proprietary instrumentation.
Log Management
100%
Log fingerprinting and facet analytics automatically parse and deduplicate logs, enhance search speed, and reduce storage needs.
25%
High log volumes lead to increased costs, slow search and analysis, and limited retention periods.
Query Languages and Dashboards
100%
Familiar query languages (PromQL, TraceQL, LogQL, GraphQL, SQL), along with FuseQL, a rich query language for logs. Kloudfuse and open source Grafana dashboards.
25%
DDSQL. Proprietory, and requires learning and ramp time.
AI and ML Capabilities
100%
Advanced anomaly, outlier detection, forecasting, and correlation algorithms.
100%
Advanced anomaly, outlier detection, forecasting, and correlation algorithms.
Advanced Data Transformation
100%
Functions to improve query performance and reduce storage footprint (e.g., Metrics Rollup).
100%
Functions to improve query performance and reduce storage footprint (e.g., Metrics Rollup).
Data Retention
100%
Flexible, using any storage (e.g., cost-efficient S3 buckets).
50%
Datadog retention options range from 7 days to 15 months.
Ease of Deployment
75%
Simple deployment mode with built-in Control Plane to administer.
100%
SaaS-base product. Does not require hosting and administration.
Scalability
100%
Massively scalable without the associated costs.
75%
Scalable but costly at large scale.
Developer Community
25%
Starting to establish.
100%
Large developer community.
Use Cases
75%
Application, Infrastructure, Digital Experience, and LLM.
100%
Application, Infrastructure, Digital Experience, LLM, and Security.
Unified Observability
100%
Unified across metrics, logs, events, traces, LLM, and more.
100%
Unified across metrics, logs, events, traces, LLM, and more.
Pricing
100%
Affordable and transparent, with a range of sizes to fit your needs.
0%
Expensive, unpredictable pricing model, and unexpended overages.
Security and Compliance
100%
Deployed in your VPC. Ideal for those with strict security, privacy, and sovereignty needs.
25%
SaaS, vendor-hosted cloud. Sensitive Data Scanner tool to identify/tag sensitive data.
Vendor Neutrality
100%
Open to collect from any agent with migration paths from any prior tool.
0%
Closed, with no migration path available.
Data Sources
100%
Over 700 sources.
100%
Over 700 sources.
Instrumentation and Collection
100%
Any agent (e.g., Datadog, NewRelic, OpenTelemetry, etc.). Does not require new instrumentation.
25%
Only Datadog agent. Requires non-standard, vendor-specific proprietary instrumentation.
Log Management
100%
Log fingerprinting and facet analytics automatically parse and deduplicate logs, enhance search speed, and reduce storage needs.
25%
High log volumes lead to increased costs, slow search and analysis, and limited retention periods.
Query Languages and Dashboards
100%
Familiar query languages (PromQL, TraceQL, LogQL, GraphQL, SQL), along with FuseQL, a rich query language for logs. Kloudfuse and open source Grafana dashboards.
25%
DDSQL. Proprietory, and requires learning and ramp time.
AI and ML Capabilities
100%
Advanced anomaly, outlier detection, forecasting, and correlation algorithms.
100%
Advanced anomaly, outlier detection, forecasting, and correlation algorithms.
Advanced Data Transformation
100%
Functions to improve query performance and reduce storage footprint (e.g., Metrics Rollup).
100%
Functions to improve query performance and reduce storage footprint (e.g., Metrics Rollup).
Data Retention
100%
Flexible, using any storage (e.g., cost-efficient S3 buckets).
50%
Datadog retention options range from 7 days to 15 months.
Ease of Deployment
75%
Simple deployment mode with built-in Control Plane to administer.
100%
SaaS-base product. Does not require hosting and administration.
Scalability
100%
Massively scalable without the associated costs.
75%
Scalable but costly at large scale.
Developer Community
25%
Starting to establish.
100%
Large developer community.
Use Cases
75%
Application, Infrastructure, Digital Experience, and LLM.
100%
Application, Infrastructure, Digital Experience, LLM, and Security.
Customer and Industry Reviews
“The Kloudfuse unified observability data lake, deployed within GE Healthcare VPC, ensures that all metrics, events, logs, and traces (MELT) data are completely secure and compliant, while significantly reducing the overall solution cost for increased data volumes.”
“The Kloudfuse unified observability data lake, deployed within GE Healthcare VPC, ensures that all metrics, events, logs, and traces (MELT) data are completely secure and compliant, while significantly reducing the overall solution cost for increased data volumes.”
Dharam Savarala
Senior Director • CoE
GE Healthcare
“We explored several solutions, including DataDog, NewRelic, and Elastic. While they were strong contenders, we found them unsuitable due to limited support for logs and the need for consulting services. Ultimately, we selected Kloudfuse, a more flexible and cost-effective solution to accommodate our growing data volume without restrictions.”
“We explored several solutions, including DataDog, NewRelic, and Elastic. While they were strong contenders, we found them unsuitable due to limited support for logs and the need for consulting services. Ultimately, we selected Kloudfuse, a more flexible and cost-effective solution to accommodate our growing data volume without restrictions.”
Krishna Govindarajan
VP Of Engineering
Nations Info Corp
“Kloudfuse is making scalable observability available to enterprises that are currently using and paying high licensing fees for multiple monitoring and observability platforms. It does this by offering a lower license cost, while supporting large-scale data and a unified view across all observability streams.”
“Kloudfuse is making scalable observability available to enterprises that are currently using and paying high licensing fees for multiple monitoring and observability platforms. It does this by offering a lower license cost, while supporting large-scale data and a unified view across all observability streams.”
Mike Fratto
Senior Research Analyst
451 Research
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