# Uber Modernizes Artifact Storage _Uber modernized its artifact storage by migrating to a SaaS platform and implementing a validation proxy to cut egress costs and boost reliability._ **Updated:** 2026-08-22 **Published:** 2026-05-28 **Source:** https://www.startuphub.ai/ai-news/technology/2026/uber-modernizes-artifact-storage --- Uber's sophisticated engineering ecosystem, built on vast monorepos and thousands of microservices, hinges on reliable storage and distribution of build artifacts. For over a decade, a centralized on-prem platform managed this critical path, handling dependency resolution and artifact storage. This legacy system, deployed across two data centers, utilized local disks and a complex replication strategy. Legacy Artifact StorageDriveron-prem platform with local disks and complex replicationFrom the article 4 mentionsLatency remains comparable to the legacy system, as unchanged artifacts are served directly from the internal cache after a lightweight validation check.Growth & LimitationsDriverdisk space constraints, silent replication failures, manual rebalancingFrom the article 2 mentionsHowever, exponential growth in scale and artifact volume exposed significant limitations.Shift to SaaSCoremigrated to a managed platform for artifact storageFrom the article 6 mentionsThis Uber artifact storage modernization reflects a strategic shift towards managed services, augmented by intelligent internal infrastructure to balance cost, performance, and reliability for critical software build dependencies.leads toCloud Egress CostsDriversignificant challenge with growing artifact distribution needsFrom the article 3 mentionsThe transition to a cloud-based solution introduced a new challenge: significant data egress costs.Validation Proxy LayerCoreimplemented to control and reduce egress costsFrom the article 3 mentionsThis proxy acts as a validation layer, not a traditional cache.New ArchitectureEffectdelivers improved reliability and performance for artifact storageFrom the articleThe new proxy-based architecture, deployed across two Uber data centers for low latency and active-active resilience, has yielded substantial improvements.Enhanced ResilienceEffectensuring data integrity and availability for critical artifactsFrom the article 2 mentionsEnhanced observability provides full visibility into every request, making it easier to detect and address issues like replication gaps or failures quickly.Future OptimizationEffectplatform designed for continued scaling and efficiency gains However, exponential growth in scale and artifact volume exposed significant limitations. Disk space constraints led to manual, high-risk storage rebalancing. Inconsistent, asynchronous replication often resulted in silent failures, leaving artifacts vulnerable to data loss. Hardware failures demanded tedious manual data evacuation, a process fraught with operational risk. Software upgrades were particularly perilous, involving multi-terabyte database migrations and extensive manual coordination, with the potential for complete cluster failure. ## Shifting to a Managed Platform To overcome these hurdles, Uber opted for a managed Software-as-a-Service (SaaS) platform. This move offered built-in regional isolation, high availability, and asynchronous cross-region replication, fundamentally shifting operational burdens like upgrades and security patches to the provider. Cloud-native blob storage replaced local disks, eliminating capacity concerns and mitigating hardware failure risks. ## Tackling Cloud Egress Costs with a Proxy Layer The transition to a cloud-based solution introduced a new challenge: significant [data egress costs](/ai-news/technology/2026/mercedes-benz-cuts-data-costs-66-with-databricks). With monthly artifact downloads exceeding 5 petabytes, Uber needed a way to reduce repeated data transfers without compromising the SaaS platform's role as the source of truth. The solution was an internal proxy layer. This proxy acts as a validation layer, not a traditional cache. It forwards client requests to the SaaS origin but uses conditional headers like If-None-Match and If-Modified-Since. This prevents re-downloading unchanged artifacts, drastically cutting egress. The proxy maintains an artifact metadata store to track checksums and timestamps, ensuring correctness is always validated against the authoritative source. Building a purpose-designed validation proxy was necessary because generic HTTP caching proxies, reliant on TTLs, lacked the strong consistency guarantees and per-request validation Uber required. ## New Architecture Delivers Results The new proxy-based architecture, deployed across two Uber data centers for low latency and active-active resilience, has yielded substantial improvements. Egress costs have been slashed by over 99% by avoiding redundant downloads. Overall reliability now stands at 99.99% at the proxy layer. This approach ensures cost growth is tied to new or updated artifacts, not repeated downloads, reducing overall artifact-related egress costs by nearly 90%. Latency remains comparable to the legacy system, as unchanged artifacts are served directly from the internal cache after a lightweight validation check. Enhanced observability provides full visibility into every request, making it easier to detect and address issues like replication gaps or failures quickly. ## Ensuring Resilience and Future Optimization The proxy is designed for fail-safe operation. In case of internal dependency failures, it can fall back to direct passthrough to the SaaS origin. Load balancing handles instance failures transparently, and GeoDNS manages failover for data center or cloud region outages. Crucially, the proxy never serves stale artifacts; conditional requests ensure cached content is only used when explicitly confirmed as current by the SaaS platform. Failed downloads are discarded, preventing corrupted data. Next steps involve enhancing the cache layer to support range requests, optimizing for large objects (>8GB), introducing node-local hot caches for bursty access, and implementing request coalescing to manage peak load thundering-herd behavior. This [Uber artifact storage modernization](https://www.uber.com/us/en/blog/modernizing-artifact-storage/) reflects a strategic shift towards managed services, augmented by intelligent internal infrastructure to balance cost, performance, and reliability for critical [software build dependencies](/ai-news/tech/2026/uber-s-ai-prototype-shift). --- Original analysis from [startuphub.ai](https://www.startuphub.ai), the #1 AI startup directory. © StartupHub.ai. All rights reserved. You may not republish this article in full without a license. Search engines and AI research tools may crawl and summarize for reference. Bulk reproduction or model training on this content requires a license. See https://www.startuphub.ai/terms.