Build Artifact CDN: Strategic infrastructure for AI-driven DevOps

by Frank Zhu and Hans Dockter

The uncomfortable truth about your build pipeline

The world’s largest financial institutions, telcos, and government contractors are investing heavily in AI-assisted development tools—GitHub Copilot, Amazon CodeWhisperer, internal LLMs—expecting transformational productivity gains. And they’re getting them. Developers are writing code faster than ever.

Then that code hits the build pipeline, and everything stops.

The traditional CI/CD model was designed around a fundamental assumption: humans write code at human speed. A developer might push changes a few times per day. The build system could afford to start fresh each time, re-downloading dependencies, re-initializing toolchains, re-computing outputs that dozens of other builds had already computed.

That assumption is now obsolete.

AI-assisted development doesn’t just make individual developers faster—it fundamentally changes the batch size and frequency of integration. Early data suggests build volumes could increase 500% within three years as AI adoption matures. More critically, the nature of what’s being built is shifting. AI tools excel at generating tests, boilerplate, integration code, and pulling novel dependencies—precisely the activities that stress CI systems the hardest.

The effects of this shift cascade downstream: 

• For CIOs: As AI increases build volume, legacy CI architectures scale poorly because the unit cost of each build remains dominated by redundant work—dependency downloads, environment setup, and recomputation—driving disproportionate spend in cloud compute, egress bandwidth, and binary repository license cost. 
• For CTOs: Validation throughput fails to keep pace, collapsing flow efficiency: code is produced faster, but feedback arrives later, leaving CI as the system-level constraint on delivery outcome velocity.
• For CISOs: More builds mean more artifact fetches from external sources, more untracked dependencies flowing through the pipeline, and more opportunities for supply chain contamination to go undetected.

The ephemeral build dilemma

Modern CI best practices mandate ephemeral environments. Every build runs in a fresh container or VM, ensuring isolation and reproducibility. In regulated environments, this is not optional but mandatory: no build artifact may leak uncontrolled from one build to the next.

This creates a fundamental dilemma. The same mechanisms that guarantee security and correctness also eliminate all local states. Every ephemeral build starts cold: dependencies are re-downloaded, build configuration and toolchains are re-initialized, and computations are re-executed—even when identical work was completed minutes earlier by another build elsewhere in the organization.

In large enterprise systems, this duplication is substantial. Based on our experience, 50–80% of total build time is routinely spent on redundant work: fetching artifacts that already exist internally, re-parsing unchanged build logic, and re-running tasks whose inputs have not changed.

Consider a regulated financial services firm running 50,000 builds per day across 200 microservices. If each build downloads an average of 80MB of dependencies, that alone amounts to 4 TB of daily data transfer—mostly the same libraries fetched repeatedly. At standard cloud egress rates, bandwidth costs exceed $10,800 per month.

But bandwidth is only part of the waste. Each ephemeral build also re-initializes the build environment, re-parses build logic, and re-executes compilation and test tasks whose inputs have not changed. Even a conservative average of 5–10 minutes of redundant compute per build translates into 4,000–8,000 CPU-hours per day spent redoing work the organization has already completed—driving cloud compute costs and extending feedback cycles without improving delivery outcomes.

The native caching mechanisms offered by CI platforms (GitHub Actions, GitLab CI, CircleCI, CloudBees, etc.) partially mitigate this waste by reusing local state within a single pipeline or runner, but have severe limitations when it comes to performance, efficiency governance, and auditing. The same is true for other partial solutions like Proxy Caches for dependency downloads or open source build cache backends without observability.

Why native CI caches fall short at enterprise scale

Native CI caches were not designed to operate as shared, content-aware infrastructure across an enterprise. They cannot reuse semantically equivalent build results across pipelines or heterogeneous CI systems and therefore they eliminate only a fraction of redundant setup and computation. 

Because they lack content awareness, these caches treat large directories as opaque blobs—even within a single pipeline. This leads to unnecessary storage churn and avoidable upload and download overhead. The same opacity also breaks provenance: when cached data is restored, the system cannot reliably identify which artifacts it contains, where they originated, or under which policies they were produced or consumed. As a result, redundant work persists at organizational scale, and the governance and audit requirements of regulated environments remain unmet.

Even in the one area where native caches are meant to help—avoiding repeated artifact downloads—their limitations are apparent. Updating a single dependency in a node_modules directory invalidates the cache key, forcing the entire multi-gigabyte archive to be re-uploaded and re-stored. 

At enterprise scale, this creates several compounding problems:

Cache thrashing

With hundreds of active branches, pull requests, and development streams, caches evict valid data faster than it can be reused. GitHub Actions enforced a 10GB limit per repository until recently. For a monorepo with substantial dependencies, this limit is reached almost immediately. Active data gets evicted before downstream builds can benefit from it.

Fragmentation

CI caches address only a narrow slice of build artifacts. Eliminating redundant work across a build pipeline requires additional caches for setup state and build outputs, increasing operational complexity.

Siloed infrastructure

Enterprise environments are heterogeneous. Different internal organizations or subsidiaries use different CI platforms due to different reasons. Jenkins clusters coexist with GitHub Actions and GitLab runners. Each platform maintains its own isolated cache with no cross-platform sharing. The library your Jenkins pipeline built yesterday cannot serve your GitHub Actions workflow today.

Governance blind spots

This is where regulated enterprises face the most acute risk. When a build restores a cached directory, the system loses chain of custody. The cache becomes a “black hole” in the supply chain audit trail. You cannot answer fundamental questions: Which repository produced this artifact? What signature verified its integrity? Was this version of Log4j pulled before or after the CVE disclosure? Did cache poisoning happen?

For organizations subject to SEC cyber disclosure requirements, SOX compliance, or federal software supply chain mandates (Executive Order 14028), these blind spots represent material compliance gaps.

Lack of observability

CI caches expose only coarse cache events, not which artifacts or build steps were actually reused, leaving redundancy opaque and hard to optimize.

The Build Artifact CDN: A new architectural model

The solution requires rethinking where and how build artifacts are cached. The concept is straightforward: apply the same architectural principles that transformed web content delivery to the software build supply chain.

A web CDN (Akamai, Cloudflare) accelerates content delivery by placing caches at the edge, physically proximate to end users. Requests are served from local points of presence, which is closest to the clients, rather than traversing the internet to origin servers.

A Build Artifact CDN applies this model to build infrastructure. Cache nodes deploy side by side with build agents—within the same LAN, availability zone, or Kubernetes cluster. Build requests resolve locally at 10-100 Gbps LAN speeds instead of competing for WAN bandwidth to remote registries.

But proximity alone doesn’t solve the enterprise problem. A viable Build Artifact CDN must deliver four capabilities:

1. Content awareness: The cache must understand the structure and semantics of what it stores. Artifacts, configuration state, and task outputs are indexed individually rather than bundled into opaque blobs. This enables surgical invalidation, reuse of equivalent results across builds, and artifact-level provenance and observability.
2. Universal protocol support: Enterprises run heterogeneous toolchains. The cache must transparently support Maven, Gradle, npm, Bazel, pip, and emerging build systems without requiring tool-specific solutions or workflow modifications.
3. Federated distribution: A single cache node creates a single point of failure. Enterprise-grade infrastructure requires mesh topology—multiple nodes that peer across regions, sharing artifacts without round-tripping to a central server. Cache topology changes must be transparent and must not require modifying CI or build configurations across repositories.
4. Full-fidelity observability: Unlike generic object storage, a Build Artifact CDN must maintain provenance metadata. Every cached artifact must retain its chain of custody: which build produced it, from which source, with what inputs. This transforms the cache from a convenience into a compliance control.

In effect, a Build Artifact CDN “caches everything, everywhere” in a semantics-aware way at the edge. A valid Build Artifact CDN implementation must offer all the above features; anything less will retain the inefficiencies or blind spots of legacy caches.

Develocity Universal Cache: Build Artifact CDN at enterprise scale

Develocity’s Universal Cache implements the Build Artifact CDN as a distributed, enterprise-grade platform. Its core is Develocity Edge, a lightweight, stateless caching node (container or VM) deployable in any cloud or on-premise environment. Edge nodes are placed “adjacent” to build agents: inside the same LAN, availability zone, or Kubernetes cluster as the CI agents. This local-first design yields dramatic speed-ups: a 50 MB dependency fetch takes milliseconds on a 10/100 Gbps LAN, compared to seconds or minutes from a remote Maven Central or Artifactory server.

Key features of the Edge layer include:

• Egress shielding: If 1,000 concurrent builds request the same dependency (e.g. spring-boot-starter-web), the Edge node stores it from a Build Agent once and then serves it locally to all 999 other Build Agents. This can cut external data transfer costs by over 95% and relieve pressure on external or internal registries.
• Peering and mesh distribution: Develocity Edge nodes automatically peer across regions. Artifact transfers between peered nodes happen over private links (e.g. VPC peering) with LAN-like throughput, avoiding cross-region internet egress.
• Checksum-based storage: Each unique artifact is stored only once per node (or shared across the mesh) using checksum calculation. This eliminates duplicate storage of common libraries and reduces cache thrashing, dramatically increasing cache hit efficiency.

Three-layer acceleration

Universal Cache operates across three distinct acceleration layers:

1. Artifact Cache (Dependency Acceleration)

When a build needs a dependency, the local Develocity Edge rehydrates the requesting build agent if the dependency artifact is already cached; if not, the build process fetches the artifact from origin repositories as usual. The dependency will be cached at the Edge at the end of the build process. Develocity Edge nodes use HTTP/2 multiplexing to stream many dependencies over a single persistent connection, avoiding the per-file TCP handshakes common in naive scripts. This layer alone eliminates 95% and more dependency download latency and duplicate traffic.

2. Setup Cache (Toolchain Acceleration) 

This layer caches the result of build environment initialization (parsing build scripts, resolving the build graph). For example, a medium-sized Gradle Build Tool project initialization will take minutes. The Setup Cache fingerprints the entire configuration; if the build logic hasn’t changed, the CI agent can download a pre-computed configuration state from the cache instead of re-running it. This makes a “cold” CI agent start up as fast as a warm local daemon, minimizing the fixed cost of ephemeral environments.

3. Build Cache (Computation Acceleration)

This is the most transformative layer. It stores actual build outputs (compiled classes, test results, packaged artifacts) keyed by a cryptographic fingerprint (checksum) of all inputs (source code, dependency versions, compiler flags, etc.). Before executing each build task, the build agent queries the cache. A “cache hit” means the exact same task outputs have already been produced elsewhere; the build agent then skips executing the task and retrieves the outputs from the cache. Crucially, this cache is shared across the organization: if Developer A’s machine or a CI job built a library or ran tests, that output can serve any equivalent task by any developer or CI job later. In effect, the engineering organization forms a “compute hive mind,” reusing prior work and avoiding duplication of expensive operations.

Together, these layers turn CI pipelines from a series of cold starts into an incremental process. Most builds become “warm” in terms of dependency and config, and many tasks execute instantly from cache.

Governance and compliance controls

For regulated enterprises, Universal Cache transforms caching from a liability into a control mechanism:

• Provenance tracking: Every cached artifact is tagged with its origin—which registry, repository, or pipeline produced it, with what signature. Audit questions that previously required forensic investigation become simple queries: “Which builds consumed Log4j 2.14? Where did they source it?”
• Policy enforcement at ingress: Security policies apply at cache ingress. The platform can block artifacts from unapproved sources, require signature verification, or generate alerts when builds attempt to fetch components lacking verified metadata.
• Granular isolation: Artifacts produced in feature branches should not be able to contaminate main branch caches. This level of compartmentalization control is essential for zero-trust security models and is entirely absent from flat CI caches.
• SLSA compliance: By caching the build environment and inputs with full provenance, the system creates non-falsifiable records of binary provenance—a mechanism for achieving SLSA (Supply-chain Levels for Software Artifacts) Level 3.

Toolchain observability and analytics

Universal Cache is not just a passive cache; it is an active intelligence platform rooted in observability. Develocity integrates build data (via Develocity Build Scan®) with the cache, providing data-driven insights into the entire toolchain. Traditional cache metrics (hit/miss) are too coarse for decision-making. Instead, Develocity Universal Cache surfaces metrics such as:

• Traceability: Every cached artifact is linked to the specific build, commit, and the origin of the repository that produced or first fetched it. This creates a complete audit trail for the supply chain.
• Time-lost metrics: The system can compute “wasted minutes”—how much time was spent re-downloading or re-running tasks that could have been cached. This quantifies the ROI of the cache in real time.
• Dependency analytics: Leaders get a “supply chain dashboard” that shows, for example, which projects pull in the largest or riskiest dependencies. Heatmaps highlight versions of libraries across the organization, revealing concentration or vulnerability exposures.

Develocity 360 and Develocity Model Context Protocol (MCP) Server add natural-language querying to this data. Executives and architects can ask questions like “Which teams have the lowest cache hit rates?” or “How much did we save on egress costs this month?” without writing SQL. Even security analysts can query: “List all builds that are still downloading Log4j 2.14.”. This makes the cache an audit and governance tool so that platform leads can continuously monitor the health and policy compliance of the pipeline.

The economic case: Beyond cost avoidance

When we present the Build Artifact CDN concept to CFOs and CIOs, the conversation typically begins with infrastructure cost reduction. That’s a valid starting point—the hard savings are real and quantifiable:

• Egress cost reduction of 95+% through local-first caching
• Effective capacity doubling of existing agent fleets through reduced build times, deferring hardware CapEx for 18-24 months
• Repository licensing relief as traffic shifts from metered artifact repositories to the edge cache

But the strategic value extends beyond cost avoidance. 

GenAI has shifted software delivery from being developer-limited to pipeline-limited. When build and test throughput fails to keep pace with change volume, feedback slows and batch sizes grow. As batch sizes grow, all four DORA metrics degrade together: lead time increases, deployment frequency drops, change failure rates rise, and recovery slows. Teams adapt rationally—by deferring validation, batching changes, and pushing checks later in the pipeline. Over time, DevOps practices regress not by choice, but by necessity.

This dynamic explains why GenAI investments often fail to translate into delivery gains. AI accelerates code production, but legacy pipelines absorb the increase as queueing delay, redundant computation, and contention. Validation becomes misaligned with development, and fast iteration breaks down.

That saturation produces three independent but reinforcing system effects:

1. Collapsed flow efficiency: Commit-to-result latency exceeds developer attention span. Productivity is lost to waiting and context-switching, and feedback arrives too late to support rapid iteration.
2. Risk expansion: Slower or deferred validation extends vulnerability windows, delays fixes, and increases blast radius when failures occur.
3. Operational instability: As concurrency increases, pipelines become less predictable—queues lengthen, flakiness rises, and retries multiply. At the same time, reliance on external artifact sources introduces single points of failure: registry throttling, outages, or repository maintenance can halt validation entirely. When pipeline availability directly impacts revenue or risk exposure, this fragility becomes a material business concern.

Build Artifact CDN addresses these effects at their root. By eliminating redundant setup, dependency resolution, and recomputation—and by insulating pipelines from external dependency failures— accelerated pipelines restore fast, reliable feedback, make small batch sizes viable again, and allow GenAI-driven code generation to translate into shipped value.

In the GenAI era, pipeline acceleration is no longer an optimization. It is a prerequisite for sustaining delivery performance and quality as change-volume scales. Organizations that treat build and test infrastructure as strategic systems, rather than utility plumbing, will out-learn and out-deliver those that do not. The alternative—investing in AI tools while retaining legacy build infrastructure—is the worst of both worlds: incurring the cost of AI adoption without capturing its productivity benefits.

A deeper treatment of how feedback latency and pipeline behavior affect productivity, risk, and delivery outcomes is covered in Why pipeline acceleration is now a strategic imperative in the GenAI era.

The decision framework

For enterprise leaders evaluating build infrastructure modernization, we suggest framing the decision around three questions:

• What is the true cost of your current build infrastructure? This requires honest accounting beyond direct cloud spend. Include developer wait time (hours × fully-loaded compensation), opportunity cost of delayed releases, compliance costs associated with supply chain audit gaps, and incident costs when external dependencies fail.
• What would your CI/CD need to look like to absorb a 5x increase in build volume? AI adoption is accelerating. The build volume increases are coming. Does your current architecture scale linearly with volume (costs increase proportionally), or can it scale sublinearly (costs increase slower than volume)?
• Can you demonstrate chain-of-custody for every artifact in your software supply chain? For regulated industries, this is increasingly a compliance requirement, not a nice-to-have. If the answer is “not fully,” the gap represents both risk exposure and audit liability.

What comes next

This post outlines the architectural case for the Build Artifact CDN and positioned Develocity Universal Cache as an enterprise-grade implementation. But architecture alone doesn’t deliver outcomes—implementation does.

In the next installment of this series, we examine deployment patterns for the Build Artifact CDN in regulated enterprise environments: reference architectures for financial services, healthcare, and federal contexts; integration patterns with existing CI/CD toolchains; migration strategies that deliver incremental value without requiring big-bang platform replacements; and the observability capabilities that transform build data into actionable intelligence for platform engineering teams.

The organizations that treat their build infrastructure as a strategic asset—rather than a utility to be minimized—are positioning themselves for the AI-accelerated development era. The Build Artifact CDN is the foundation of that strategic infrastructure.

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