The silent drag on developer velocity
Every platform team wants faster pipelines. But hidden beneath even the most optimized CI/CD system lies an overlooked performance tax: artifact retrieval.
Each build agent that pulls dependencies, such as containers, packages, modules, and binaries, adds seconds of latency. Multiply that across thousands of builds running in parallel and across regions, and a small inefficiency becomes the bottleneck no one budgets for.
At Varnish Software, we’ve spent years helping some of the world’s largest engineering organizations diagnose and solve these exact bottlenecks. What we’ve learned, and the data shared throughout this article, come directly from those real-world deployments across media, SaaS, financial, and research sectors. The results demonstrate the significant impact on performance and cost of artifact delivery.
Across customer environments, we consistently see 20–40% of total pipeline time consumed by dependency downloads. That friction compounds across hundreds of developers, creating lost productivity and rising cloud costs that quietly erode velocity.
When you look closely, the slowdown isn’t just technical. It’s cultural. Waiting on dependencies breaks flow, and that cognitive interruption ripples through your entire developer platform.
When pipelines stall for invisible reasons
Every team has felt it:
- CI jobs stalling because public registries throttle requests.
- Developers in remote regions waiting minutes for docker pull or npm install.
- Private registries straining under internal traffic during release peaks.
A global financial institution found that nearly half its CI latency came from artifact pulls alone, and backend CPU sat at 100% just to keep up. A global SaaS platform discovered that 94% of its network traffic came from repeated artifact downloads across teams.
The lesson? You can’t tune your way out of physics. If every build depends on distant registries, no amount of pipeline optimization will keep up.
The rise of the artifact platform
A virtual registry consolidates and fronts your existing artifact repositories, Docker, npm, PyPI, Maven, Helm, Git, and more. When combined with high-performance caching, it transforms artifact delivery into an acceleration layer.
Here’s how it works:
- Build agents fetch and push artifacts through a single virtual endpoint.
- Cached artifacts are served locally; misses are fetched once and stored persistently.
- Policies govern freshness, authentication, and routing across upstream registries.
- Developers keep their workflows; no tooling changes required.
As a result, build systems complete their build tasks a lot faster, deployments are quicker, and developers experience a lot less latency when pulling in dependencies to their local systems. There is a lot less friction, and less firefighting happening going on.
Virtual registries make artifact delivery evolve from background plumbing into a first-class performance layer. However, performance is not the only advantage.
From bottleneck to advantage
When you multiply artifact retrieval across thousands of builds, the potential impact is enormous. A global SaaS provider introduced a caching layer in front of its artifact managers and transformed 6.5 PB of network traffic into local cache hits, reducing origin requests by 94%. That single change halved egress spend and stabilized build times worldwide.
A major media service saw a similar shift. 99% of artifact requests never reached the origin. Git clones ran 3× faster, and critical migrations that once risked downtime became seamless. Engineers noticed a surprising side effect as well. Their build failures during registry outages disappeared.
For a global financial organization, integrating caching into CI/CD dropped backend CPU usage by 50% and accelerated artifact pulls by 30-40%. And at a research institution, NuGet restore times fell from three minutes to just over one, with latency cut 60% and backend load down 70%.
Across these environments, one pattern repeats: the fastest pipelines aren’t merely optimized, they’re localized. By bringing artifact delivery closer to workloads, platform teams reclaim control of both time and cost.
The platform engineering perspective
For platform engineers, artifact caching isn’t just a performance tweak.
1. Make it a platform capability.
Treat caching and virtual registries like source control or CI runners, core services on your internal developer platform.
2. Place caches close to workloads.
Co-locate with build clusters or cloud regions to minimize latency. Global teams benefit from regional mirrors.
3. Measure what matters.
Monitor cache hit ratios, egress savings, and latency improvements. One enterprise recorded a 92% cache hit rate within three weeks, saving $80K per month in bandwidth fees.
4. Manage freshness and security.
Automate invalidation for mutable artifacts and integrate vulnerability scanning to prevent serving outdated or compromised packages.
5. Simplify governance.
Expose a single internal endpoint (registry.company.internal) that abstracts upstream complexity, enabling self-service and consistency across teams. This could be an in-cluster endpoint that doesn’t even reach the LAN or WAN.
A modern implementation example
Modern solutions such as Varnish Orca embody this approach, combining caching and virtual registry management across artifact types like Docker images, npm packages, Helm charts, and Git repositories. With built-in observability (OpenTelemetry logs, metrics and traces) and persistence, they show what a fully realized artifact platform looks like.
But more than any tool, the principle matters: artifact delivery deserves first-class status. Once teams treat it as such, reliability, speed, and cost-efficiency become measurable outcomes—not afterthoughts.
Why this matters for platform engineering
Platform engineering exists to reduce friction and cognitive load. The less time developers spend waiting, the more time they spend creating. By internalizing artifact delivery, platform teams enable that flow, not just by speeding up builds, but by eliminating uncertainty.
Caching and virtual registries may not be flashy, but they’re among the highest-ROI capabilities in modern software delivery. They quietly turn waiting into working.
