Alright, I’ve been putting HeyGen through its paces for the last three weeks on our team’s real pipeline. We build and deploy a bunch of internal tools, mostly Go and Python services with a React frontend, all running on EKS.
The marketing says “40% faster production.” That’s a big number. I was skeptical too—most of these tools give you a 5–10% boost at best once you account for real-world complexity.
So I decided to measure it. I took one of our mid-sized services, `notification-service`, and ran it through our old Jenkins pipeline and then through a HeyGen-powered GitLab CI setup. The goal: same code, same tests, same container image destination.
Here’s what I compared:
* **Build + unit test stage** (Go modules, linting, unit tests)
* **Container build + push** (multi-stage Dockerfile to AWS ECR)
* **Integration test stage** (spin up a test namespace in k8s, run a Helm test)
* **Deploy to staging** (Helm upgrade via ArgoCD, but triggered post-success)
The key wasn't just the total time, but *where* the time was saved. With HeyGen, the big wins came from two places:
1. **Parallelization of independent steps** they set up automatically. Our old pipeline ran everything sequentially. HeyGen’s config analyzed our Dockerfile and test structure and split things like so:
```yaml
# Simplified view of what it generated for the build stage
stages:
- prepare
- test
- build
prepare:
parallel:
- job: download_deps
- job: security_scan_base_image
test:
parallel:
- job: unit_tests
- job: lint_go
- job: lint_dockerfile
```
This seems obvious, but we never got around to optimizing it manually.
2. **Caching layers aggressively across pipelines.** Their caching for Docker layers and Go modules between feature branches was way more effective than our home-brewed solution. Hit rates went from ~40% to over 85%.
**The numbers?**
Our Jenkins pipeline averaged **22 minutes**. The HeyGen pipeline averaged **14 minutes**. That’s about a **36% reduction** in total pipeline time. Not quite 40%, but damn close in our case. The biggest time save was in the build/test stage, which got cut almost in half.
**The catch:** Your mileage will vary. If your pipeline is already highly optimized and parallelized, the gains will be smaller. The value for us was that HeyGen did this optimization automatically—we didn’t have to spend a week rewriting pipelines.
Has anyone else run similar comparisons? I’m curious if the savings hold up for more complex, multi-artifact pipelines or if they’re mostly for simpler single-service flows.
-jk