Team size: 45 engineers, 20/80 split between Java (Spring Boot) and Go (microservices). Stack context is AWS EKS, GitHub Actions for CI. We considered self-hosting both tools to avoid vendor lock-in, which is a major concern for us.
Everyone's talking about Claw being the "modern" SonarQube killer, but I think that's naive. The real question isn't which "catches more" bugs—it's which catches the *meaningful* ones for your specific languages without drowning you in noise or tying you to a platform.
From our 3-month PoC:
* **Java analysis**: SonarQube is still deeper, leveraging years of rules for Spring and complex OO patterns. Claw's Java analysis felt like a decent subset.
* **Go analysis**: Here, Claw was noticeably better. Its understanding of Goroutine leaks and channel pitfalls was more actionable. SonarQube's Go support still feels like a bolt-on.
* **The integration tax**: SonarQube's self-hosted setup is a beast (we dedicated a full-time SRE for maintenance). Claw's cloud service is trivial to run, but you're now locked into their analysis pipeline and pricing model. Their "bring-your-own-runner" promise has significant limitations in practice.
So, which catches more? For a mixed shop, the answer is unsatisfying: neither. You're choosing between superior Java coverage with heavy ops overhead (SonarQube) or superior Go coverage with a different kind of vendor risk (Claw). The open source alternatives we looked at (like Gosec for Go, SpotBugs for Java) required stitching together and lacked a unified dashboard, which management vetoed.
Has anyone else run both in parallel on the same codebase and quantified the overlap vs. unique findings? I'm skeptical of the marketing claims from both sides.
Trust but verify.
I'm Bob Williams, a principal engineer for an 80-person fintech shop where we manage a similar 30/70 Java-to-Go split on EKS, and I've had both Claw and SonarQube in our pipeline over the last two years. We currently run SonarQube self-hosted for Java services and Claw's cloud offering for our Go code, a split-brain setup that came from hard lessons.
1. **Meaningful Findings vs. Noise Tax**: SonarQube's Java rule set is exhaustive, which is the problem. You'll get 300+ issues on a fresh Spring Boot scan, with maybe 15 being critical. Tuning it down to signal requires disabling 40% of its default rules. Claw's Java is less noisy but because it's shallower; it misses complex inheritance and Spring data injection issues we've seen in prod. For Go, Claw catches the stuff that actually causes midnight pages: Goroutine lifetimes in HTTP middleware and unbuffered channel deadlocks. SonarQube's Go checks are mostly basic style.
2. **Total Cost of Ownership - Price is a Lie**: SonarQube's $0 self-hosted license cost us ~$1,800/month in EC2/RDS for a HA setup and, critically, 20% of one senior SRE's time for upgrades, scaling, and DB optimization. Claw's $15/developer/month cloud list price is predictable but their "unlimited" scans throttle after 500K lines/day, which our monorepo hits. Their on-prem "bring-your-own-runner" is $45/dev/month and requires you to manage the analysis cluster yourself, negating the cloud ease.
3. **Integration and Maintenance Reality**: SonarQube setup is a multi-week project. The Helm chart works but tuning PostgreSQL for large deployments is not documented. Cache warming is mandatory for PR checks under 5 minutes. Claw's cloud integrates into GitHub Actions in 10 minutes, but you're locked into their analysis schedule and security posture. Their API doesn't allow exporting raw issue data for our custom dashboards, a hard lock-in.
4. **Where It Breaks Under Load**: SonarQube's scanner memory usage for large Java monoliths required us to run 8-core, 16GB runners; it died on OOM errors otherwise. Claw's cloud queues scans during peak commit times, adding 20-30 minute delays that broke our "gate on PR" policy. Their support admitted their backend scales per-organization, not globally.
My pick is Claw, but only if your Go code is the primary source of complexity bugs and you can accept the vendor lock-in and scan queue delays. If your business critical logic lives in Spring Boot services and you have the SRE capacity, SonarQube's depth is worth the pain. To decide, tell us your PR gate latency requirement and what percentage of your production incidents are traced back to static analysis catchable code defects.
Migrate once, test twice.
You've perfectly captured the integration tradeoff. The SRE cost for SonarQube is real, but I'm curious how you quantified it. Was it truly a full-time equivalent, or was it more about the recurring deployment headaches?
On the lock-in point with Claw's cloud, I'd be worried about their analysis pipeline becoming a black box. If they change their rule taxonomy or deprecate a check, you can't just fork an old version like you could with SonarQube. For a team your size, does that loss of control outweigh the maintenance savings?
Also, when you say Claw's Go analysis was more actionable, could you give a concrete example of a finding it caught that SonarQube missed? I'm trying to understand if it's a difference in the rules themselves or just better prioritization of the results.