Migrated from Akamai Kona to Cloudflare - 6 month DDoS performance report

Having maintained a substantial real-time data ingestion pipeline behind Akamai Kona's WAF and DDoS mitigation for several years, our recent migration to Cloudflare presented a unique opportunity for a longitudinal, data-driven comparison. The catalyst was a combination of cost restructuring and a desire for more granular, programmable control at the edge. Now, with six months of operational data and having weathered several volumetric and application-layer events, I can share a detailed performance analysis focused purely on DDoS mitigation efficacy.

The core of our setup involves protecting a suite of JSON-over-HTTP APIs that feed our internal event streaming platform (Kafka), alongside our customer-facing web application. Attack surfaces are typical: API endpoints susceptible to layer 7 floods, and the general IP space for layer 3/4 attacks. My evaluation criteria were:
* **Mitigation Transparency:** Latency added to legitimate traffic during an attack.
* **Configuration Granularity:** The specificity with which rules can be crafted to minimize false positives during high-volume legitimate traffic (e.g., product launches).
* **Insight & Forensics:** The detail and usability of attack analytics for post-mortem and tuning.
* **Automatic vs. Manual:** The effectiveness and "hands-off" reliability of the managed rulesets.

**Performance During Volumetric Attacks**

We experienced three significant UDP-based amplification attacks and one TCP SYN flood in the observation period. Cloudflare's autonomous edge network performed admirably in absorbing these, with zero packet loss to our origin. The key differentiator from our Akamai experience was the observable latency. Under similar magnitude attacks with Akamai, we saw a 45-60ms increase in P95 latency for legitimate requests. With Cloudflare, this increase was typically contained to 15-25ms. This is almost certainly due to the sheer density of Cloudflare's network, allowing traffic to be scrubbed closer to the source.

**Application-Layer Mitigation & Tuning Nuances**

This is where the most interesting comparative analysis lies. Akamai's Kona rules, while robust, often felt like a black box. Fine-tuning required support tickets or navigating a complex proprietary UI. Cloudflare's WAF, with its direct integration into the dashboard and a more expressive rules language (using the Wirefilter syntax), allows for surgical adjustments.

For instance, during a coordinated HTTP GET flood targeting our `/api/v1/events` endpoint, we were able to quickly deploy a custom rate limiting rule that keyed on a combination of the request path, the presence of a valid API key, and the source ASN. Crafting this rule was straightforward:

```sql
(http.request.uri.path contains "/api/v1/events" and not http.request.headers contains "x-api-key")
or
(http.request.uri.path contains "/api/v1/events" and cf.bot_management.score lt 30)
```

We then applied a stringent rate limit action to this filter. The ability to combine Bot Management scores, geographic data, and request characteristics in a single, self-service rule is a substantial operational advantage. The trade-off, however, is a steeper initial learning curve to reach the same "set-and-forget" confidence we had with Akamai's managed service.

**Areas of Note and Minor Grievances**

* **Analytics Depth:** Cloudflare's analytics are real-time and visually excellent, but for deep forensic analysis (e.g., extracting a specific sample of malicious payloads over a 24-hour attack), I still find myself preferring the raw log delivery we had configured with Akamai. Cloudflare's Logpush is capable, but the mental model for querying attack data is different.
* **Threshold Tuning:** The sensitivity of the managed DDoS rules, particularly for layer 7, required more manual calibration than anticipated. We encountered a few instances of false positives during legitimate traffic surges, which necessitated adjusting thresholds per endpoint. This is not a con, per se, but it underscores the shift from a fully-managed model to a co-managed one.
* **Cost Predictability:** While not a performance metric, the move from a committed monthly minimum to a usage-based model (for advanced services) changes financial forecasting. A massive, sustained attack carries a direct operational cost beyond just resource exhaustion.

In conclusion, the migration has been a net positive from a technical performance standpoint. The raw mitigation capacity is superior for our use case, and the programmable edge offers a powerful toolset for those willing to invest in tuning. The transition represents a shift from a "full-service" vendor model to a "platform" model, where greater control also demands greater internal expertise and vigilance.

testing all the things