Can you stabilize our AI feature without pausing development?

Yes. In this engagement we shipped guardrails (queues, circuit breakers, autoscaling) in parallel with feature work. We isolate the stabilization changes behind flags and roll them out with canaries.

Do we need to replatform our LLM stack?

Usually not. We prefer least-change approaches: cap concurrency, add backpressure, and make outputs deterministic. If vendor limits are the bottleneck, we add smart fallbacks rather than wholesale migrations.

Which vendors and tools do you work with?

Azure OpenAI, OpenAI, Anthropic (via Bedrock), Vertex AI; Node/TypeScript, Python; Redis/BullMQ, Kafka; Prometheus/Grafana; OpenTelemetry/Tempo/Jaeger; ArgoCD/Rollouts; KEDA; Postgres/pgvector; Elastic/OpenSearch; Langfuse; LaunchDarkly.

How fast can we see results?

We target tangible improvements in 72 hours (fewer 429s, lower median latency) and SLO-grade stability in 2–6 weeks depending on constraints and traffic patterns.

Case-studies · Oct 20, 2025 · 10 minute read

The AI Feature That Buckled at 4 p.m.—And How We Kept It Standing

A B2B SaaS rolled out an AI assistant, then real users showed up. Rate limits, token burn, and 12s p95s. GitPlumbers stabilized it under live fire—no replatform, no pause button.

Alex P.

Principal Engineer, GitPlumbers

20 years taming outages and debt at scale: led SRE at a unicorn during their microservices split, built incident response at a fintech, and lately fixes AI-assisted systems so they don’t melt at 4 p.m.

> “We were hemorrhaging tokens and user trust. GitPlumbers didn’t sell magic—they put in guardrails. Two weeks later, demos stopped being scary.” — CTO, mid-market B2B SaaS

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The AI Feature That Buckled at 4 p.m.—And How We Kept It Standing

Key takeaways

Implementation checklist