SOFTWARE DELIVERY AND CI/CD
Why are your CI builds still waiting on the network?
Your pipeline shouldn't wait for the internet. Every second a build runner spends pulling a dependency it already pulled is a second your engineers aren't shipping.
Modern software delivery runs at machine speed. Automation loops, auto-scaling runners, and AI agents pull dependencies continuously across hundreds of concurrent builds. The registries behind them weren't designed for this. When Artifactory or Nexus struggles under load, your delivery pipeline waits.
The trend making this worse
CI/CD concurrency is accelerating faster than registry infrastructure can absorb. Three forces are driving it:
AI-assisted development
Commit frequency is rising fast. More commits mean more pipeline triggers, more parallel builds, more dependency pulls — all hitting the same upstream registries simultaneously.
Auto-scaling build infrastructure
Peak load is no longer predictable. A single merge to main can spin up dozens of runners in seconds, each independently requesting the same packages from the same origin.
Multi-ecosystem pipelines
Pulling from Docker, npm, PyPI, Maven, and proprietary registries in the same build multiplies upstream calls per run. Every ecosystem is a potential point of failure.
The result is a delivery infrastructure that becomes less reliable as your engineering team grows.
What slow pipelines actually cost
The impact lands differently depending on where you sit:
VP of Engineering
Every hour lost to pipeline latency is an hour not spent shipping features or fixing bugs.
DevOps and SRE lead
A surge of parallel builds during a major release becomes an outage at exactly the moment you can least afford one.
Platform Engineering Director
Delivery SLAs become impossible to guarantee when machine-to-machine traffic can outpace registry capacity at any moment.
Why standard caches don't fix this
Dropping a generic proxy in front of your registry seems like the obvious answer. It doesn't work, for four reasons specific to how artifact registries operate:
Challenge 1
They compromise security boundaries
Generic proxies cache globally without verifying permissions per request, potentially exposing private artifacts to unauthorized users.
Challenge 2
Heavy content bypasses the cache entirely
Registries serve large binaries via 302 storage redirects. Standard proxies follow the pointer and pass it to the client, the actual payload never gets cached, so every build runner fetches it directly from cloud storage.
Challenge 3
Signed URLs drop hit rates
Every storage redirect carries a unique expiring signature token. A conventional cache treats each tokenized URL as a new request, fragmenting the cache map and producing hit rates too low to matter.
Challenge 4
Metadata conflicts freeze pipelines
The same container layer pulled from two different repositories looks like two different objects to a URL-keyed cache. Storage fills up with duplicates and the deduplication benefit disappears.
"Varnish is the cornerstone of how we handle scale"
Global streaming service
How Varnish Virtual Registry solves it
Varnish Virtual Registry is a registry-aware caching, routing, and observability layer purpose-built for high-volume software delivery. It sits between your build nodes and your master repositories, acting as a vendor-neutral buffer that handles the read path at memory speed.
01
Build runners never touch the upstream |
Artifacts are served from local cache the moment they've been pulled once. Subsequent requests — from any runner, any ecosystem, any team — are answered locally. Upstream registries see a fraction of their previous traffic. |
02
Private repositories stay private |
Every request is validated against the upstream registry before anything is served from cache. Access controls are enforced per request, not bypassed by caching. Private artifacts are never exposed to unauthorized users. |
03
Binary payloads are captured at the perimeter |
Storage redirects are resolved server-side. The raw binary bytes are ingested and stored locally, with signature tokens stripped before caching. The same asset is served from local cache on every subsequent request, sustaining 99%+ hit rates from your local network. |
04
One control plane across every ecosystem |
Docker, npm, PyPI, and Maven consolidate under a single caching and routing layer. Health probes monitor upstream registries continuously and reroute traffic to mirrors or local failover caches during outages. Builds keep running even when upstream services go down. |
The numbers
Build concurrency scales. Pipelines stop failing. Engineers stop waiting.
90%+
70%+
40%+
Built for pipelines that can't afford to wait
Varnish Virtual Registry
A registry-aware caching, routing, and observability layer engineered for high-volume software delivery pipelines. Vendor-neutral, protocol-aware, and designed to sit transparently in front of your existing infrastructure. No rearchitecting your stack. No changing how your teams work. No waiting on the network.
Resources and media
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How to accelerate GitHub and reduce CI/CD bottlenecks at scale
If you are looking for ways to accelerate GitHub, improve GitHub Actions performance, or reduce CI/CD bottlenecks, the root issue is not simply scale.
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Why your DevOps pipeline is slower than you think, and how to fix it
You’ve optimized your code. You’ve automated your tests. You’re using the latest CI/CD tools. So why are your builds still slow?
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How artifact caching and virtual registries can supercharge your CI/CD
Co-hosted by Platform Engineering, this webinar breaks down how smart caching and virtual registries can help teams deliver faster with fewer headaches.
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The DevOps challenge
With many enterprises now handling petabytes of traffic through their build pipelines each month, these challenges are not just technical inefficiencies; they are critical business risks that threaten profitability, agility, and innovation.
Next steps
Talk to our team to learn more about Varnish Virtual Registry or try it for yourself for free today.
