Your CDN infrastructure is the last handoff between your content and your customers. It may be tempting to just set it up and forget about it, but all the work your teams do upstream means nothing if that final connection fails.
CDN monitoring is how you make sure the handoff is working and your customers are having a good experience. But not every monitoring tool is built for the same job. This piece breaks down five platforms across five categories, what each one is actually built for, and when to use it. We’ve structured it as two lists: a quick-reference summary up front, followed by a deeper breakdown of each platform.
Solutions at a glance: The quick TL;DR
1. Outside-in synthetic monitoring
Catchpoint: Run tests from a global network of monitoring agents to measure what your users are experiencing.
When it’s the right choice: When you need to understand the end user experience, validate SLA performance, or benchmark CDN providers.
2. General observability with CDN log support
Datadog: Designed to give you observability across your stack, including infrastructure, application, and service monitoring.
When it’s the right choice: When you want a general observability platform and your data volume isn’t too high (for example, 1TB/day prices out to more than 50k/month).
3. High-scale, multi-CDN log analytics
Hydrolix: Designed for full-fidelity monitoring across all your CDN providers so you can correlate, compare, and quickly pinpoint issues.
When it’s the right choice: When you’re running major events or multi-CDN infrastructure and need high-volume (greater than 1TB/day), real-time analytics without surprise bills.
4. SIEM and security-first log analysis
Splunk: Bring CDN logs into a security workflow alongside firewall logs, endpoint telemetry, and cloud security events.
When it’s the right choice: When CDN data needs to live inside a security operations center (SOC) environment and feed into existing alerting and compliance processes.
5. Log pipeline routing and transformation
Cribl: Sits upstream of other tools on this list, filtering log data and routing it to different sources.
When it’s the right choice: When you need to route data to multiple sources (such as a SIEM, data lake, and analytics tool) and have data sources you are willing to sample.
A deeper look at each solution
1. Outside-in synthetic monitoring: Catchpoint
With most CDN monitoring tools, you measure performance from the inside. You ingest logs from your CDN providers into an analytics platform, then use insights from those tools to understand how your infrastructure is performing. Catchpoint does the opposite, measuring performance from the outside. It measures what users actually experience by running synthetic tests from a network of more than 3,000 monitoring agents worldwide.
The Benchmarks feature lets teams run CDN performance tests across a global device population and compare providers against each other on cost-to-performance ratios. For teams evaluating CDN contracts or making traffic steering decisions across a multi-CDN setup, that external benchmark provides valuable evidence that internal logs alone cannot.
Catchpoint doesn’t ingest or analyze CDN logs at volume. It’s not the right tool for drilling into raw log data during a live incident, correlating error rates across providers, or retaining years of delivery telemetry. The probe-based approach is structurally different from log analytics, and the two are more complementary than competitive.
Catchpoint is the right call when you need to validate SLAs, benchmark CDN providers against each other from an end-user perspective, or monitor the performance your users are actually experiencing rather than the performance your infrastructure reports. It’s not enough on its own because it doesn’t help you understand the why, but it works well alongside other analytical tools.
2. General observability with CDN log support: Datadog
Datadog is the observability platform of choice for many engineering teams. Datadog isn’t the most complete CDN monitoring tool—that’s not where its value lies. The real value is in the comprehensive observability that Datadog provides so that your teams can monitor CDN metrics and logs, APM traces, infrastructure health, and service dashboards without a context switch.
For a team using Datadog to monitor their origin servers and application stack, pulling CDN log data into the same environment can help make incident correlation faster. If latency spikes at the CDN layer at the same time an origin service slows down, you want those signals in the same view. Datadog’s log management pipeline can ingest CDN access logs from major providers and apply parsing, tagging, and alerting across them.
Where Datadog runs into friction is scale and cost. Datadog’s pricing is indexed to log ingestion volume, and CDN logs are high-volume by nature. Teams running a multi-CDN infrastructure at significant scale often find that the economics push them toward sampling or shorter retention windows. At the point where you’re making decisions about what data to keep in order to manage your bill, you’re already compromising the completeness that makes CDN monitoring useful.
Datadog is the right call for teams that want CDN visibility inside an existing Datadog environment and aren’t running the log volumes that make cost a controlling constraint.
3. High-scale, multi-CDN log analytics: Hydrolix
For major enterprises, CDN infrastructure can generate terabytes of telemetry data per day. Major events like the Super Bowl can generate 200 terabytes of data over the span of a few hours. The challenges of multi-CDN visibility compounds the problem: there’s no common schema across Akamai, CloudFront, Cloudflare, and Fastly, so correlating their logs in a single view normally requires heavy preprocessing or a platform built to do it natively.
Hydrolix was designed to solve these problems. It standardizes all data as it arrives into a single queryable table, making correlation across providers much easier. CDN Insights gives engineering teams a unified dashboard across all CDN providers while Bot Insights uses CDN data to give you deeper intelligence on how bots are interacting with your web properties. Hydrolix also offers full-fidelity data with long-term retention (15 months by default), giving teams both real-time and historical analytics without the typical tradeoffs of most observability platforms (such as sampling, high costs, and short retention periods).
The tradeoff is scope: Hydrolix is not a full-stack observability suite like Datadog or Splunk. For complete observability use cases, Hydrolix complements tools like Splunk and Datadog, allowing you to offload high-volume CDN log data and reduce costs alongside any other observability platform you’re using.
4. SIEM and security-first log analysis: Splunk
Splunk is the tool of choice for many security teams. You can use Splunk to correlate security events, run SIEM workflows, and maintain compliance audit trails. CDN logs contain information that’s genuinely useful for security analysis such as bot traffic patterns, geographic anomalies, unusual request rates, and access patterns that don’t match normal behavior.
For a team running a SOC that’s responsible for both security monitoring and infrastructure observability, keeping CDN log data in Splunk alongside firewall logs, endpoint telemetry, and cloud security events makes sense.
The limitations are similar to Datadog’s. Splunk’s cost model at high log ingestion volumes is well known in the industry, and CDN logs, which can run into terabytes per day, can quickly become expensive. Many Splunk users address this by routing lower-value logs to cheaper storage and only sending high-priority data to Splunk itself. This is part of why Cribl, also covered in this post, has grown alongside Splunk’s customer base.
Splunk can be the right call when CDN logs need to live inside a security workflow and the team is already operating in a Splunk environment, but you’ll likely need a complementary solution such as Cribl or Hydrolix to help manage costs.
5. Log pipeline routing and transformation: Cribl
Cribl sits upstream of the other tools on this list. Cribl Stream is a log routing and transformation layer that collects telemetry from sources, filters and enriches it, and routes it to one or more destinations. For CDN log pipelines specifically, Cribl lets teams decide which logs go to a SIEM, which go to a log analytics platform, which get dropped, and which get archived to low-cost object storage.
The practical value for CDN-heavy environments is cost control. Sending all of your data to platforms like Datadog or a SIEM tool is expensive. Cribl lets teams sample lower-value access logs, route security-relevant events to Splunk while sending full-fidelity data to a log analytics platform, and archive raw logs to object storage for later replay.
However, Cribl is infrastructure, not analytics. It doesn’t provide dashboards, doesn’t answer questions about CDN performance, and doesn’t run queries against log data. Instead, it manages the flow of data to the tools you use for these jobs. If you don’t have a clear picture of where your CDN logs should go and why, Cribl adds complexity rather than reducing it. The value is highest when the downstream tool costs are well understood and the routing logic is clearly defined. One of the major cost control levers that Cribl offers is sampling, leading to data gaps. It’s often not the right choice if you’re prioritizing full-fidelity data.
Cribl is the right call when managing log pipeline costs is itself a problem, and when you’re operating at a scale where data routing decisions have significant financial implications.
How to choose the right solution
Before committing to a platform, here are some questions you should ask prospective vendors.
- How does the pricing model behave as log ingestion volume grows, and what happens to retention when costs get high?
- For teams running multiple CDN providers, does the platform handle that natively, or does it require preprocessing before data lands?
- How quickly is data available for querying after ingestion, and does that hold during peak traffic?
- Is CDN monitoring a core use case for the platform, or one of many supported integrations?
- What does query performance look like against months of full-resolution data, not a sampled subset?
Disclaimer: GeekWire newsroom and editorial staff were not involved in the creation of this content..
