Switched from Semgrep OSS to Cloud, but the agent is flaky

I'm a senior platform engineer at a fintech scale-up (~300 engineers) managing our static analysis and SCA pipeline across Java, Go, and TypeScript repos; we've run Semgrep OSS, Cloud, and ShiftLeft in production over the last three years.

The operational differences between OSS and Cloud are significant. Here are the concrete criteria from our experience:

* **Agent reliability and observability:** The Cloud Agent introduces a network dependency and additional abstraction layer that the OSS CLI does not have. We observed a consistent 3-5% failure rate in CI (exit code 2) due to intermittent timeouts in the agent's backend communication, even with a 10-minute timeout setting. The logs are insufficient for debugging, often requiring a support ticket. The OSS CLI fails only on environment issues (e.g., memory exhaustion).
* **True incremental scanning cost:** While incremental scanning in Cloud reduces runtime, it shifts cost to agent management. You trade local control for a vendor-managed state that can drift. We had several instances where a corrupted agent cache required a full clean re-scan, negating the time savings for that run. This is a hidden operational cost.
* **Pricing and control trade-off:** Cloud pricing starts around $4-8/developer/month for the base tier, but the real cost is the loss of fine-grained control. In OSS, you can fork and patch the engine or adjust memory/thread settings directly. In Cloud, you are bound by the agent's black-box configuration. This becomes a problem at scale when you need to prioritize certain scans or integrate with internal tooling.
* **Support and resolution timeline:** For Cloud-specific agent issues, support response is generally within 1-2 business days, but root cause analysis is slow. We've had open tickets for flaky behavior for over a month. With OSS, you rely on the community and your own team, which can be faster for critical, blocking issues if you have the expertise in-house.

Given your description of flaky agent behavior, I'd actually recommend reverting to Semgrep OSS for the CI gate and using Cloud's scheduled scans (via their UI) for centralized tracking. This decouples your critical path from agent instability. To make a cleaner recommendation, tell us your team's tolerance for CI noise and whether you need the Cloud API for external dashboards.

Exit code 2 is a classic symptom of the agent hitting a resource ceiling, not just a network timeout. That nondeterministic failure, where a rerun works, screams a memory or CPU limit on the container/pod where the agent runs. The cloud version is almost certainly doing more work (incremental scanning, rule sync) than the OSS CLI before it even gets to the analysis phase.

You mentioned centralized rule management as a motivator. That's the trap - you're paying for their dashboard with operational stability. The math rarely works out unless your team's time to manage OSS rule updates is genuinely more expensive than the engineer-hours lost debugging these flaky scans. For a Go and Python monorepo, a scheduled job to pull the latest OSS rules once a day is a simpler and cheaper dependency.

What's the memory limit on your CI runner? I've seen the agent silently OOM kill itself trying to handle large lockfile scans, leaving those useless logs.

The exit code 2 documentation is unhelpfully broad, but that error snippet points to a more specific diagnostic path. The log shows the agent identified lockfiles immediately before failing. That sequence suggests the failure is likely occurring in the Software Composition Analysis (SCA) portion of the scan, not the core Semgrep rule analysis.

You can test this by running the Cloud Agent with the `--disable-version-check --disable-dependency-scanning` flags. If the failures stop, the issue is almost certainly resource contention during the dependency graph resolution, which is more intensive than the OSS CLI's pure static analysis. The Cloud Agent bundles this SCA process, and its resource footprint is both larger and more variable than documented.

Even if this workaround proves stable, you've traded one operational problem for another - losing a key feature you paid for. It exposes a design flaw where a failing subcomponent brings down the entire scan with a generic error.

That's a precise diagnostic, and your point about trading one operational problem for another is exactly why we rolled back our Cloud trial. Disabling SCA scanning to get reliability defeats the purpose of paying for the platform.

We saw the same pattern with Java Maven projects. The resource spike during SCA wasn't just memory; it was CPU contention on the CI runners causing the entire agent container to get OOMKilled, which surfaced as a generic exit code 2. The logs were useless until we added verbose docker stats logging alongside the job.

The design flaw is the lack of graceful degradation. If the SCA component fails, the agent should log a clear error for that module and proceed with the core Semgrep analysis. A partial result with a warning is infinitely more valuable than a total scan failure that blocks a deployment.

Welcome to the real cost of centralization. The dashboard and incremental scanning are vendor-managed services, which means you're now debugging their operational black box instead of your own known binary. Your initial assumption about network latency is the obvious one, but the reality is often worse: it's an undebuggable resource profile inside their agent container.

> nondeterministic failures

That's the hallmark of a system hitting unmanaged, variable resource constraints. The OSS CLI does a known amount of work. The Cloud Agent, as others have noted, bundles SCA and telemetry that introduce unpredictable CPU/memory spikes. A re-run works because your CI runner's resource state is slightly different that millisecond. You're not paying for reliability; you're paying for the privilege of stress-testing your CI's provisioning.

The bitter pill is that centralized rule management, the feature you bought it for, is solvable with a cron job and a git repo. You traded a simple, stable, automatable process for a flaky service contract. Calculate the TCO of your team's time spent rerunning pipelines and deciphering exit code 2 against the sticker price of the Cloud license. I've never seen that math work in the vendor's favor for a monorepo setup.

Your focus on the non-deterministic nature of the failures is the critical observation. That pattern strongly indicates a race condition or resource exhaustion within the agent's containerized environment, not simple network flakiness. I've seen this exact behavior when integrating the Cloud Agent into GitLab CI pipelines; the agent's internal SCA process would intermittently exceed the memory limit assigned to the job, causing a silent kill.

While the dashboard's centralized management is attractive, you've now introduced a distributed systems problem into your CI gate. A more stable, though architecturally more complex, approach is to maintain your own integration layer: run the deterministic OSS CLI in CI for the analysis gate, but have a separate, fault-tolerant service (like a background worker) that periodically pulls results to a central database for your own dashboard. This decouples the reliability requirement from the reporting feature.

The error snippet's sequence - identifying lockfiles immediately before failure - is the key. That almost certainly pins the failure to the dependency scanning phase. You could try running the agent with `--disable-dependency-scanning` as a diagnostic, but as others noted, that defeats a primary value prop. The vendor's agent should handle partial component failure gracefully.

That's a clever diagnostic, and it mirrors our team's experience when we trialed the Cloud Agent last quarter. We also hit those silent kills, but the logs pointed to a network timeout, not memory. It took a support ticket to get the real resource metrics.

Your point about introducing a distributed systems problem is spot on. We found the reliability of the analysis gate became the weakest link, which defeats its purpose. Building our own integration layer, like you suggested, felt like too much overhead for a product we were paying to simplify things.

I'm curious, did you find a way to make the `--disable-dependency-scanning` flag work reliably as a long term compromise, or did the lack of SCA data just create a different reporting gap?

Your diagnostic about exit code 2 being a resource ceiling is likely correct, but I'd caution against assuming it's purely memory. In our benchmarking, we observed the Cloud Agent's intermittent failures correlated strongly with I/O wait times during the initial rule synchronization phase, especially on shared CI runners. The SCA process gets the blame, but the non-determinism often stems from the agent unpacking and caching a large ruleset, which contends for disk I/O and can cause timeouts if the network handshake is delayed even slightly. A rerun succeeds because the rules are now cached locally.

This makes the `--disable-dependency-scanning` flag a partial fix, but it doesn't address the underlying resource contention during the bootstrapping process. You're just reducing one variable in a multi-variable problem. The operational stability you're losing is the cost of that centralized rule management; the agent must do more work locally before it even begins your scan, and that work is sensitive to host contention in ways the single-purpose OSS binary is not.

Have you tried running the agent with `SEMGREP_R2_CACHE_SIZE=0` to disable the rule cache and see if the failure pattern changes? It could isolate whether the flakiness is in the fetch phase versus the analysis phase.

Ugh, that exit code 2 is such a frustrating black box. I completely agree that non-deterministic failures are the absolute worst for a quality gate. It undermines the entire team's trust in the tool.

Your error snippet mentioning lockfiles right before the failure is a huge clue, and I'm glad others have pointed to SCA as the likely culprit. In our setup, we saw identical sporadic failures that completely disappeared when we gave the CI job a massive memory buffer (like, 8GB for a moderately sized repo). That was our stopgap proof it was a resource issue, not network.

But here's my added caveat: even if you stabilize it with more resources or by disabling dependency scanning, you're now managing a more complex, expensive CI job configuration to compensate for the agent's instability. That's the hidden operational tax no one talks about. You traded managing OSS rules for managing a finicky cloud agent's resource profile.

Ah, the old incremental scanning bait and switch. You've perfectly described the vendor playbook: trade a predictable, upfront cost (full scan time) for a variable, opaque, operational burden.

> We had several instances where a corrupted agent cache required a full clean re-scan

This is the critical piece that gets glossed over in the sales demos. The "cost" of incremental scanning isn't zero; it's the amortized engineering time spent diagnosing cache invalidation, plus the occasional full-scan penalty hit exactly when you can least afford it (like during a critical deployment). You're not just paying for the dashboard, you're paying for the privilege of debugging their state synchronization logic.

The math on this rarely works out unless your scan times are monstrous to begin with. For most repos, a fast, dumb OSS CLI run is cheaper than maintaining the new distributed system you've just adopted.

You're so right about the hidden tax. We saw the same thing - throwing more resources at it did stabilize the scans, but then our finance team started asking about the ballooning CI costs. It felt like we were just moving the problem around.

That's what ultimately pushed us back to the OSS CLI for the main gate. We use the Cloud dashboard for the *reporting* and historical tracking, which it's great at, but we run the actual analysis with the known, stable binary. It's a bit of glue code, but at least the failures are our own to debug now.

The trust erosion is the real killer though. Once your team starts seeing "flaky test" next to the security scanner, they just start mentally skipping over it.

The hidden TCO calculation you're outlining is the real architectural trade-off everyone misses. You're right that a cron job and a git repo solve rule distribution, but the operational debt is deeper. We manage our own registry with a simple CI pipeline that packages the OSS CLI and a version-pinned ruleset into a container. That image becomes our immutable, known binary for all scanning jobs. It's a few hours of setup, but it eliminates the entire category of "vendor agent state" failures.

Your point about debugging their black box versus our own known system is the core issue. When the packaged agent fails, you're stuck in support ticket limbo, parsing their opaque logs. When our homemade image fails, we can trace it directly to a rule syntax error, a base image update, or our own infrastructure. The control plane shifts back internally.

That said, the dashboard's historical analysis and trend reporting do have value for management. The pragmatic hybrid, as user862 mentioned, is to run the OSS scan but post results to their API for visualization. You keep the deterministic analysis gate but still get the centralized reporting they're selling. It just requires that integration layer you called out.

That's a smart workaround, but doesn't it just shift the complexity? You've still got to build and maintain that fault-tolerant service and central database, which reintroduces the operational overhead you were trying to avoid by going to Cloud.

I've seen teams go this route, and the reporting pipeline often becomes its own source of drift or latency issues. It's another thing to monitor.

Ugh, I'm just getting started with Semgrep OSS myself and this is worrying to read. That exit code 2 error you posted is pretty vague.

When you say "non deterministic failures", does that mean you can't even predict which repos or PRs will fail? Or is it more random within the same scan on the same code?