CI Strategies for Large Game Repositories: Artifact Storage, Build Caching, and Cost Control

Fast builds, predictable costs: CI strategies that scale for big game repos in 2026

Hook: If you manage continuous integration for a large game repo, you already know the pain: 30–90 minute CI runs, exploding storage bills, and cache churn that makes incremental builds unreliable. This guide gives you a pragmatic, reproducible blueprint — artifact retention policies, remote build caches, incremental build patterns, and the 2026 realities of storage media (including PLC SSDs) — to cut build times and control spend.

Executive summary — what to do first

• Measure current build characteristics: median build time, cache hit-rate, artifact growth, IOPS, and egress.
• Triage where time is spent: code compile vs. asset processing vs. packaging.
• Introduce a remote, content-addressable cache for compiled outputs and game engine derived data (Bazel/Gradle/Unity/UE remote caches or sccache for C++/Rust).
• Tier storage by access pattern: NVMe for hot, PLC SSD for warm, object cold tiers for archival.
• Define retention and eviction policy based on branch type, release importance, and compliance windows.
• Monitor and iterate on hit rates, costs, and build latency.

Why conventional CI patterns fail for large game projects

Game repositories are unusual: they combine large binary assets, heavy native builds (C++/HLSL), and engine-specific derived data (Unity Cache Server or Unreal DDC). Typical CI patterns assume small text-based codebases and cached dependencies that are cheap to store and restore. For game teams, those assumptions break down:

• Artifacts are huge: single build artifacts or DDC blobs can be multiple gigabytes or tens of gigabytes.
• Builds are multi-stage: asset pipeline, shader compilation, native compilation, packaging.
• Frequent branches and PRs multiply artifact count.
• Storage performance and endurance matter: many rewrites (cache churn) can wear SSDs.

The 2026 storage landscape: PLC SSDs, NVMe, and object storage

Late 2024–2025 advances from vendors like SK Hynix made PLC (penta-level cell) SSDs commercially viable. By 2026, PLC-based drives are common for high-capacity, warm storage where cost/TB dominates. But PLC comes with tradeoffs:

• Cost advantage: PLC drives push cost-per-TB down vs. TLC/QLC — attractive for warm artifact stores.
• Endurance and performance: PLC has lower program/erase cycles and higher error rates; enterprise controllers and LDPC help, but endurance is still below NVMe TLC for heavy rewrite loads.
• Use cases: PLC is best for warm storage (infrequently modified artifacts, long-lived release archives, remote caches with low churn). Avoid PLC for hot build caches that see heavy rewrites.

Combine this with cloud object storage (S3/GCS/Azure Blob) for cold, immutable archives and CDN-backed delivery for large installers/art bundles.

Design principles for CI at scale

• Make caches content-addressable (CAS). Hash outputs and store by key to deduplicate and enable safe concurrency.
• Prefer remote caches over artifact snapshots for repeated compilations — caches reduce CPU and I/O significantly when hit rates are high.
• Tier storage by IO pattern: NVMe for ephemeral runners and hot caches, PLC SSDs for warm caches and mid-term artifact retention, object cold tiers for archives.
• Plan eviction by policy, not space: automated lifecycle rules by branch/type reduce manual intervention and cost surprises.
• Secure and sign caches and artifacts to avoid tampering and supply-chain risk.

Remote build caching: tools and patterns that work for game engines

Choose the remote cache strategy based on language and engine:

• Bazel / Gradle Remote Build Cache: Great if your codebase supports them; content-addressable and efficient for deterministic builds.
• sccache / ccache: Widely used for C/C++/Rust in game engines to cache compiler outputs. Use a server-backed sccache with Redis or GCS/S3 backend for multi-runner environments.
• Unity Cache Server / Unity Accelerator: Designed for Unity DCC and asset pipeline; can be used with object stores or warm SSD pools.
• Unreal Derived Data Cache (DDC): HTTP-based remote DDC or Perforce-integrated DDC; consider a distributed cache farm with local NVMe front-ends.
• OCI/registry for buildpacks and containers: Use registry proxies and layer caching for container images used in pipelines.

Example: GitHub Actions + sccache + S3 remote cache

# Simplified outline
- name: Restore sccache
  run: |
    aws s3 cp s3://game-ci-caches/sccache/${{ matrix.os }}.tar.gz /tmp/sccache.tar.gz || true
    tar -xzf /tmp/sccache.tar.gz -C $HOME/.cache/sccache || true

# Build steps use sccache automatically

- name: Upload sccache
  if: always()
  run: |
    tar -czf /tmp/sccache.tar.gz $HOME/.cache/sccache || true
    aws s3 cp /tmp/sccache.tar.gz s3://game-ci-caches/sccache/${{ matrix.os }}.tar.gz

This pattern is simple but has limits at scale: tarball restore/upload is slow and non-incremental. Prefer a native sccache server or object-backed CAS where clients push/pull individual cache keys.

Artifact storage & retention: rules that save money

Artifacts stored without policy are the largest recurring cost driver. Create deterministic retention rules tuned to developer workflows.

Retention policy templates (practical)

• PR builds: keep artifacts for 7–14 days. Delete automatically if PR closed or merged.
• Branch builds (long-lived feature branches): keep last N artifacts (N=10–30) or 30 days, whichever comes first.
• Main/release branches: keep 90–365 days depending on compliance and hotfix needs.
• Release tags/production installers: keep indefinitely or move to immutable cold storage (object archival like Glacier Deep Archive) with checksums and signatures.
• Build logs: keep 30–90 days; keep longer only for audited builds.

Practical dedupe & compression

• Store artifacts as content-addressed chunks (restic-like) to deduplicate across builds and branches.
• Compress large assets using engine-supported bundle compression; store deltas for incremental updates (asset bundle diffs).
• When storing installers, keep both full and delta packages to balance restore cost vs. storage cost.

Cost-optimizing the storage stack

Apply these cost levers in order of ROI:

1. Reduce artifact count: prune non-actionable CI artifacts automatically.
2. Deduplicate with content-addressable chunk stores.
3. Tier to PLC SSDs for warm caches; use NVMe for hot caches.
4. Archive older releases to deep object tiers.
5. Compress and delta-package engine assets to reduce storage and egress.

PLC SSDs: where they make sense

In 2026, PLC SSDs are a cost-effective choice for:

• Warm artifact caches where writes are moderate and reads are common.
• Large remote caches that are read-mostly (e.g., nightly snapshots, DDC stores used by many runners).
• Storage nodes for deduplicated chunk stores where overwrites are infrequent.

Do not use PLC for:

• Local ephemeral caches that see thousands of rewrites per day.
• High IOPS metadata stores without enterprise-grade controllers.

Eviction, lifecycle and cache warmup strategies

Eviction policies should be deterministic and aligned with build importance. Example approach:

• Implement multi-tier eviction: LRU for hot NVMe nodes, TTL for warm PLC nodes, lifecycle to cold object storage.
• Pin keys for release branches or nightly gold builds to avoid eviction.
• Warmup caches ahead of major events: populate build farm caches prior to daily studio syncs or release nights using scheduled jobs.

CI pipeline architecture patterns

Pattern A — Fast PR feedback (developer-focused)

• Trigger: PR.
• Goals: quick smoke tests, compile subset, run unit tests, validate assets.
• Cache strategy: restore minimal cache (compiler headers, shader cache), per-PR cache TTL short (7d).
• Retention: artifacts auto-delete after 7 days.

Pattern B — Nightly integration & QA

• Trigger: nightly or scheduled.
• Goals: full build, integration, bake DDC, run large test suites.
• Cache strategy: heavy remote cache use, pre-warmed using scheduled cache repopulation jobs; store snapshots in PLC-backed warm tier for 30–90 days.
• Retention: retain last 30 nightlies, archive once per week to cloud cold tier.

Pattern C — Release pipelines

• Trigger: release tag.
• Goals: deterministic full build, QA gating, packaging, release artifacts.
• Cache strategy: pin caches, use NVMe for sensitive build steps, store final artifacts in immutable object storage with signatures.
• Retention: move to archival cold storage with checksum and_signed manifests_.

Monitoring and KPIs you must track

• Cache hit rate (overall and per-cache): target >70% for effective remote caches.
• Median/95th build time: track regressions when changing cache tiers or eviction policies.
• Storage cost per month and per artifact set.
• IOPS and latency on cache nodes to detect PLC-related slowdowns.
• Build flakiness tied to cache inconsistencies.

Security and governance

• Enforce ACLs for cache access; restrict upload to CI service principals.
• Sign artifacts and cache manifests. Use provenance metadata to map artifact -> commit -> CI job.
• Audit retention for compliance; export logs of deleted artifacts when required.
• Protect against cache poisoning: validate cache keys against trusted build graphs and signer certificates.

Cost example and decision framework (worked example)

Scenario: 200 developers, average 5 CI runs/day each, average artifact + cache growth 50 TB/year. You must decide what to keep hot on NVMe vs. move to PLC vs. archive to cold object storage.

Decision steps:

1. Measure hot working set: e.g., 2 TB of frequently accessed cache across active branches.
2. Keep that on NVMe to maximize build speed.
3. Move remaining warm caches (e.g., 20 TB) to PLC-backed nodes with enterprise controllers; monitor IOPS and replace with NVMe if rewrite rates spike.
4. Archive older artifacts (rest 28 TB) to cold object tier with lifecycle; only restore them on-demand.
5. Implement retention rules to reduce yearly growth from 50 TB to 20–25 TB stored long-term.

This approach balances cost while protecting build latency for the critical hot set.

Common pitfalls and how to avoid them

• No metrics: don’t guess hit rates. Instrument caches and CI runners before major investments.
• One-size-fits-all storage: using only object cold storage adds latency; tier instead.
• Ignoring rewrite patterns: PLC drives wear out fast under heavy rewrite workloads — track TBW and replace when needed.
• Unbounded artifact retention: leads to exponential costs. Automate lifecycle management.
• Security gaps: unsigned caches are a supply-chain risk. Enforce signing and provenance.

2026 trends and future predictions

• PLC SSD adoption will grow for warm, high-capacity caches, but hybrid architectures (NVMe + PLC + object cold) will be the norm in game CI.
• Content-addressable remote caches will become standard across CI providers, with better native support in hosted runners.
• Edge pre-warming for global studios: distributed cache front-ends will reduce latency in multi-studio setups.
• Cache safety and provenance will be regulated more tightly as supply-chain security gains prominence.
• Incremental asset diffs and engine-level delta bundles will become mainstream to reduce pipeline storage and egress.

Quick checklist to implement in the next 90 days

1. Collect: instrument CI to measure build time, hit rate, artifact growth.
2. Configure: enable a remote content-addressable cache (sccache/Bazel/Unity/UE remote cache).
3. Tier: move hot working set to NVMe, define PLC nodes for warm cache if present, and cold object tier for archives.
4. Retain: implement automated retention policy templates (PR:7d, branch:30d, release:365d).
5. Secure: sign artifacts and restrict cache uploads to CI principals.
6. Monitor: set alerts on cache hit-rate < 60% or storage growth > 10% month-over-month.

Closing: actionable takeaways

• Measure first — the right architecture depends on your hot working set and rewrite patterns.
• Tier storage by access pattern; use PLC SSDs for warm, NVMe for hot.
• Use content-addressable caches to maximize dedupe and enable safe multi-runner sharing.
• Automate retention to prevent uncontrolled growth and surprise bills.
• Sign and audit artifacts to secure your supply chain.

Call to action

Ready to reduce CI time and storage spend? Start with a 2-week experiment: enable a remote CAS-backed cache for one major build stage, measure hit rate and build-time delta, and then iterate on tiering and retention. If you want a checklist or a one-page audit template for your repo, download our free CI audit guide for game teams (includes retention policy templates and sample cache configs optimized for Unity and Unreal).

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