Your developers just got faster. Your pipeline didn't.
AI coding assistants now generate code, tests, and dependencies at machine speed, and all of it lands in the same continuous integration pipeline that was designed for human-speed change.
The result isn't faster delivery — it's a pipeline that can't keep up. The same artifacts cross the network on every run, and the repository behind it strains under traffic it was never sized to handle.
Multiply that across an AI-driven surge in commits, and the cost compounds — in cloud egress, in the repository capacity you keep adding, and in the hours engineers lose waiting on builds. Most teams have never measured it, and it grows quietly as volume climbs.
The reflex is to scale the artifact repository, or to replace it. The better move is to keep it and add Develocity® Artifact Cache, a Build Artifact CDN: This local cache your CI build agents populate and serve themselves, so they stop pulling the same artifacts on every run. It takes the load off your repository and lets software delivery scale with GenAI-era volume instead of stalling under it.
For most of software's history, delivery speed was limited by how fast people could write and review code. AI removed that limit. As of June 2026, a growing share of enterprise code, tests, and configuration is machine-generated, and the volume of change flowing into CI has climbed accordingly.
That shift relocates the bottleneck. When developers were the constraint, the pipeline could keep up. Now that code arrives at machine speed, the pipeline becomes the main limit on how fast an organization can actually ship. Delivery has moved from developer-limited to pipeline-limited — and the pipeline was never sized for this.
Builds have always re-downloaded the same files. GenAI just makes it far more expensive. Industry data puts redundant work at 50–80% of total build time on ephemeral CI, where every job starts on a clean machine and re-downloads everything it needs from scratch. Sonatype reports that 83% of Maven Central's bandwidth flows to just 1% of IPs, a signature of the same artifacts being pulled over and over. That redundancy is a minor cost at low commit volumes — but a major one at GenAI scale.
One financial institution makes that concrete: it serves roughly 50,000 builds a day, peaking at 15,000 requests per second and 23 TiB of artifacts moved in a single day. Almost every one of those builds re-fetches what the build beside it pulled minutes earlier.
And the cost isn't only time. When validation throughput lags code production, every delivery metric degrades together: lead time stretches, deployment frequency drops, change-failure rates climb, and recovery slows. The companion case for a Build Artifact CDN put it plainly: in the GenAI era, pipeline acceleration "is no longer an optimization. It is a prerequisite." That's the urgency. The question is what to do about it.
When the pipeline slows down, teams reach for one of a few familiar fixes — and each solves the wrong problem.
Do nothing. Treat the waste as a cost of doing business. At today's volumes, you can. But GenAI pushes build counts higher every quarter, so the bill only grows. The instinct, then, is to spend.
Scale the repository. Add capacity, add a region, renew the license at a higher tier. But a single Artifactory or Nexus deployment can't sit close to a build fleet spread across many offices, regions, and cloud availability zones. One global bank runs its engineering across more than 14 offices and 32 data centers; no single repository deployment sits close to all of them. Distance becomes latency, and latency becomes wait time on every build. Spending more on the repository doesn't bring it any closer to your runners. If money can't close the distance, maybe the CI platform can.
Use native CI caches. They don't close the gap either. They cap out quickly: GitHub Actions' cache historically topped out at 10 GB per repository, small enough that valid entries are evicted before they're reused. Each entry is a whole folder, not individual artifacts, and the caches live in silos: the library your Jenkins job built yesterday can't serve your GitHub Actions workflow today. Worse, when a build restores a cached blob, the chain of custody for what's inside it breaks. Under GenAI volumes, that's more unverified artifacts moving through more pipelines with less human review. So teams go custom.
Build your own. A homegrown proxy, a shared network drive, or pre-baked images. These can speed things up locally, but they're opaque about where each artifact came from, they break chain of custody, and they leave your platform team running one more fragile system. Which raises the bigger question: is the repository itself the problem?
Replace the repository. The drastic option: rip out the system your teams have standardized on and migrate to something new. That's a multi-year, high-risk project that stalls roadmaps and rarely pays back. And it throws away something valuable: your repository is the system of record for what's approved, governed, and trusted. You don't want to lose that. You want to remove the bottleneck around it.
The real problem isn't which repository you run. It's the distance and redundancy between the repository and the build. Fix that, and the repository you already have is suddenly enough.
A Build Artifact CDN applies the content-delivery-network model to your build pipeline. Just as Akamai and Netflix cache content at the edge, close to end users, a Build Artifact CDN places lightweight cache nodes, Develocity Edge, right beside your build agents, in the same LAN, availability zone, or Kubernetes cluster. Dependencies are served locally at 10–100 Gbps instead of fetched across the WAN.
Develocity Artifact Cache is a standalone CLI your build agents run, the build-side of that CDN. It runs as a quick step before and after each build, restoring dependencies from the local Edge rather than the remote repository. Adoption is non-invasive: it runs as a pre- and post-build step across Jenkins, GitHub Actions, GitLab, and custom runners. A 50 MB fetch that took seconds or minutes from a distant repository now takes milliseconds. And it's precise, not just fast: because Artifact Cache is content-aware (it indexes every artifact individually rather than as one opaque blob), one new dependency doesn't invalidate the rest. Granular caching, served locally at the edge of a repository you don't replace — that combination is what sets Artifact Cache apart.
At multi-region scale, Edge nodes peer with one another over private links, so an artifact fetched once in one region can serve builds in another without crossing the public internet again. Each unique artifact is stored once per node and served many times. The more builds you run, the more the model pays off. For an organization adding builds by the month under GenAI pressure, that scaling behavior is exactly the point: the system gets more efficient as load grows, instead of breaking down under the load.
Crucially, none of this displaces your repository. JFrog Artifactory stays the system of record. The Edge serves more than 95% of artifacts locally; only the remaining under 5% travels upstream to Artifactory to fetch something genuinely new. Artifactory governs what should be used for your build; Artifact Cache ensures it reaches the build process efficiently. Better together — and in the GenAI era, that pairing is what keeps the pipeline ahead of the code (Figure 1).
Figure 1 — Before and after: every build crossing the WAN, versus a local Edge serving most of them.
Running both together isn't a compromise. It's how you get speed, cost control, and provenance at the same time — exactly the three things GenAI-era CI strains hardest.
When dependencies come from a node on the same network, the whole build finishes faster. On controlled benchmarks, full build times drop sharply: one AndroidX project went from nearly 50 minutes to 6, an Apache Polaris build from 3 minutes to 1. That reclaimed time flows straight into faster feedback and more builds per agent — the throughput a GenAI-era pipeline needs.
Here's the part a CFO cares about. When 1,000 builds request the same dependency, the Edge fetches it from the internet once and serves it locally to the other 999. That single behavior cuts external data-transfer volume by up to 95% — a realized result across production deployments, not a projection. The payoff doesn't stop at bandwidth. Because the Edge serves repeated traffic locally, you also defer the next round of repository capacity and CI-compute spend, and metered Artifactory traffic and storage pressure ease. Put numbers to it: a Develocity cost model for a 20,000-developer organization projects roughly $7M a year in operating savings and another $5M in deferred capital expenditure. The strategic point: you fund acceleration out of the bandwidth and capacity bills you're already paying, rather than adding new spend.
Speed and savings can't come at the cost of control, and under GenAI, control gets harder as more artifacts move with less human oversight. Because Artifact Cache is content-aware, each artifact can carry its origin, signature, and lineage, preserving a verifiable chain of custody even as builds reuse work across teams and regions. That visibility flows into Develocity, where Build Scan® and the supply-chain dashboards in Develocity 360 trace every dependency download back to the build and commit that used it. Artifact Cache can also integrate with Provenance Governor to enforce policy at ingress, stopping unapproved artifacts before they enter a build. You don't have to assemble that picture by hand: ask the MCP server in plain language and an AI assistant renders it as a live supply-chain dashboard (Figure 2). Those tamper-proof records of provenance are the foundation for SLSA-level supply-chain assurance — now a procurement and compliance requirement, not a nice-to-have, as auditors ask harder questions about AI-generated code.
Figure 2 — The result of a Develocity MCP query: cache hit rate, egress saved, provenance coverage, and artifacts blocked at ingress (representative).
By now the case largely makes itself: as build volume keeps climbing, pipeline acceleration has moved from a nice-to-have to a basic requirement for sustaining delivery performance. For engineering leaders, the question is no longer whether to act, but how soon.
Three questions are worth asking of your own pipeline today. What is redundant fetching actually costing you in wait time and egress? Could your CI absorb a 5× jump in build volume without breaking? And can you still prove chain-of-custody for every artifact you ship? If the answers are uncomfortable, the fix doesn't have to be a migration. Keep the repository your teams trust, and add a Build Artifact CDN.
The timing matters. Many enterprises hit this wall just as repository licenses come up for renewal and AI-adoption mandates push build volume higher — the moment when paying more for the same redundant traffic is hardest to justify. That's the opening to fund acceleration from budget you already carry, leave the system of record in place, and give your pipeline room to absorb whatever GenAI sends next.
The clearest way to see the impact is on your own numbers. Read Build Artifact CDN: Strategic infrastructure for AI-driven DevOps for the full argument, then request a guided trial of Develocity: we'll show you where your CI is spending its time, what Artifact Cache and Artifactory deliver together, and how its other caches, Build Cache and Setup Cache, can deliver even more.
