sccache vs ccache vs Icecream: Self-Hosted Build Cache & Distributed Compilation Guide 2026

Build times are one of the biggest productivity drains in software development. As codebases grow, recompiling unchanged code wastes developer time, CI minutes, and compute budgets. Build caching and distributed compilation tools solve this problem by reusing previously compiled artifacts instead of rebuilding from scratch.

This guide compares three open-source tools that accelerate compilation at different levels: sccache (Mozilla’s cloud-ready compiler cache), ccache (the original fast compiler cache), and Icecream (distributed compilation network). Each takes a fundamentally different approach — local caching, remote storage backends, or networked compilation sharing — and the right choice depends on your team’s scale, language stack, and infrastructure.

Why Self-Host Build Caching

Commercial CI platforms charge per build minute. A large Rust or C++ project can consume hundreds of minutes per pull request. Self-hosted build caching eliminates redundant compilation by:

• Storing compiled object files keyed by source content, compiler flags, and environment
• Sharing cache across CI runners so the first build populates the cache and all subsequent builds hit it
• Distributing compilation across idle machines in a build cluster
• Reducing CI costs by 50-90% on cache hits

For teams running self-hosted CI runners (GitHub Actions, GitLab CI, Jenkins), a shared build cache is one of the highest-ROI infrastructure investments you can make. If you’re already running a self-hosted CI pipeline, pairing it with a build cache multiplies the benefit — check our Woodpecker CI vs Drone CI vs Gitea Actions guide for runner setup options.

sccache: Cloud-Ready Compiler Cache by Mozilla

GitHub: mozilla/sccache | Stars: 7,198 | Language: Rust | Last Updated: April 2026

sccache is Mozilla’s answer to ccache with one key differentiator: remote storage backends. While ccache stores objects on local disk, sccache can push compiled artifacts to S3, Google Cloud Storage, Azure Blob, Redis, Memcached, or any HTTP endpoint. This makes it ideal for CI environments where builds run on ephemeral containers.

Key Features

• Multi-language support: C, C++, Rust, Go, NVCC (CUDA)
• Cloud storage backends: S3, GCS, Azure Blob, Redis, Memcached, HTTP, GitHub Actions Cache
• Compiler wrapper: Drop-in replacement for gcc, clang, rustc, go
• Local fallback: Can use local disk cache when no remote backend is configured
• Active development: Maintained by Mozilla, pushed as recently as April 2026

Installation

Linux (Ubuntu/Debian):

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# Install from prebuilt binary
SCCACHE_VERSION="v0.9.0"
curl -fsSL "https://github.com/mozilla/sccache/releases/download/${SCCACHE_VERSION}/sccache-${SCCACHE_VERSION}-x86_64-unknown-linux-musl.tar.gz" | tar xz
sudo mv "sccache-${SCCACHE_VERSION}-x86_64-unknown-linux-musl/sccache" /usr/local/bin/
sccache --version

macOS (Homebrew):

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brew install sccache

From source (Rust):

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cargo install sccache

Using sccache as a Compiler Wrapper

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# Set as default compiler
export RUSTC_WRAPPER=sccache
export CC="sccache gcc"
export CXX="sccache g++"

# Build your project
cargo build
# or
make -j$(nproc)

Docker Deployment with Redis Backend

The most common production setup runs sccache with a Redis backend for fast, shared caching across CI runners:

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version: "3.8"

services:
  sccache-redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - sccache-redis-data:/data
    command: redis-server --maxmemory 4g --maxmemory-policy allkeys-lru
    restart: unless-stopped

  # Optional: sccache server mode (for direct TCP connections)
  sccache-server:
    image: ghcr.io/mozilla/sccache:latest
    environment:
      - SCCACHE_REDIS_URL=redis://sccache-redis:6379
      - SCCACHE_DIR=/cache
    volumes:
      - sccache-data:/cache
    ports:
      - "4222:4222"
    depends_on:
      - sccache-redis

volumes:
  sccache-redis-data:
  sccache-data:

S3 Backend Configuration

For persistent, durable caching that survives container restarts:

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export SCCACHE_BUCKET=my-build-cache
export SCCACHE_REGION=us-east-1
export SCCACHE_S3_KEY_PREFIX=team-alpha
export AWS_ACCESS_KEY_ID=AKIA...
export AWS_SECRET_ACCESS_KEY=secret...

# Start sccache server
sccache --start-server

# Check cache status
sccache --show-stats

ccache: The Original Fast Compiler Cache

GitHub: ccache/ccache | Stars: 2,829 | Language: C++ | Last Updated: April 2026

ccache is the original compiler cache, created in 2002. It works as a drop-in wrapper around C/C++ compilers, storing compiled objects in a local directory keyed by a hash of the source file, compiler options, and relevant environment variables. It’s the most widely used build cache in the open-source world and is pre-installed on many CI images.

Key Features

• C/C++ focused: Optimized specifically for C and C++ compilation
• Zero configuration: Works out of the box with sensible defaults
• Hash modes: Supports both direct mode (file content hash) and manifest mode
• Compression: Automatic gzip compression of cached objects
• Docker images: Official Dockerfiles for Debian, Ubuntu, Alpine, and Fedora
• Massive adoption: Used by Chromium, Linux kernel builds, and countless CI pipelines

Installation

Linux (Ubuntu/Debian):

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sudo apt update && sudo apt install -y ccache

Linux (RHEL/Fedora):

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sudo dnf install -y ccache

macOS (Homebrew):

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brew install ccache

Using ccache in CI

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# Prepend ccache to PATH so it intercepts compiler calls
export PATH="/usr/lib/ccache:$PATH"

# Or use symlinks
sudo ln -s /usr/bin/ccache /usr/local/bin/gcc
sudo ln -s /usr/bin/ccache /usr/local/bin/g++

# Configure cache size
ccache --max-size=5G

# Build
make -j$(nproc)

# Check cache hit rate
ccache --show-stats

Docker Integration

ccache’s official repository includes Dockerfiles for multiple distros. Here’s a practical Docker Compose setup that persists the cache across builds:

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version: "3.8"

services:
  builder:
    build:
      context: .
      dockerfile: Dockerfile
    volumes:
      # Mount ccache directory from host for persistence
      - ccache-data:/root/.ccache
      - ./src:/src
    environment:
      - CCACHE_DIR=/root/.ccache
      - CCACHE_MAXSIZE=10G
      - CCACHE_COMPRESS=1
    working_dir: /src
    command: make -j$(nproc)

volumes:
  ccache-data:
    driver: local

Dockerfile:

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FROM ubuntu:24.04

RUN apt-get update && apt-get install -y \
    ccache \
    build-essential \
    cmake \
    && rm -rf /var/lib/apt/lists/*

# Ensure ccache is first in PATH
ENV PATH="/usr/lib/ccache:${PATH}"
ENV CCACHE_DIR=/root/.ccache
ENV CCACHE_MAXSIZE=5G
ENV CCACHE_COMPRESS=1

CMD ["bash"]

Icecream: Distributed Compilation Network

GitHub: icecc/icecream | Stars: 1,790 | Language: C++ | Last Updated: March 2026

Icecream (formerly known as ICECC) takes a completely different approach. Instead of caching compiled objects, it distributes compilation across a network of machines. A central scheduler assigns individual compilation jobs to idle workers, effectively turning multiple machines into a single powerful build server.

Key Features

• Distributed compilation: Parallelize builds across dozens of machines
• Central scheduler: Dynamic load balancing across workers
• C/C++ support: GCC and Clang with toolchain distribution
• Automatic toolchain sharing: Workers receive the correct compiler/toolchain from submitting machines
• Transparent integration: Works with Make, CMake, Ninja, and any build system

Architecture

Icecream uses three components:

1. Scheduler (icecc-scheduler): Central coordinator that assigns jobs to workers
2. Daemon (iceccd): Runs on each worker machine, handles compilation requests
3. Client wrapper (icecc): Drop-in replacement for gcc/g++ that sends jobs to the scheduler

Installation

Ubuntu/Debian:

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sudo apt update && sudo apt install -y icecc icecc-monitor

RHEL/Fedora:

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sudo dnf install -y icecream

Docker Compose Setup

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version: "3.8"

services:
  # Central scheduler
  scheduler:
    image: ubuntu:24.04
    command: >
      bash -c "
        apt-get update && apt-get install -y icecc &&
        exec /usr/bin/icecc-scheduler -d -v
      "
    network_mode: host
    restart: unless-stopped

  # Build worker 1
  worker1:
    image: ubuntu:24.04
    command: >
      bash -c "
        apt-get update && apt-get install -y icecc build-essential &&
        export ICECC_SCHEDULER=127.0.0.1 &&
        exec /usr/bin/iceccd -n buildcluster -d -v
      "
    network_mode: host
    depends_on:
      - scheduler
    restart: unless-stopped

  # Build worker 2
  worker2:
    image: ubuntu:24.04
    command: >
      bash -c "
        apt-get update && apt-get install -y icecc build-essential &&
        export ICECC_SCHEDULER=127.0.0.1 &&
        exec /usr/bin/iceccd -n buildcluster -d -v
      "
    network_mode: host
    depends_on:
      - scheduler
    restart: unless-stopped

  # CI builder client
  builder:
    image: ubuntu:24.04
    command: >
      bash -c "
        apt-get update && apt-get install -y icecc build-essential cmake git &&
        export ICECC_SCHEDULER=127.0.0.1 &&
        export ICECC_VERSION=/usr/bin/icecc --build-native &&
        cd /src && icecc make -j$(nproc)
      "
    volumes:
      - ./src:/src
    network_mode: host
    depends_on:
      - scheduler

Client Configuration

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# Point to your scheduler
export ICECC_SCHEDULER=192.168.1.100

# Use icecc as compiler wrapper
export CC="icecc gcc"
export CXX="icecc g++"

# Build with maximum parallelism
make -j200

# Monitor cluster status
icecc --build-native
icemon  # Interactive monitor

Comparison Table

When to Use Each Tool

Use sccache When:

• You compile multiple languages (Rust + C++ + Go) in the same project
• Your CI runners are ephemeral containers that need remote cache storage
• You want cross-runner cache sharing without managing NFS volumes
• You need S3/GCS/Azure as the durable backend
• You use GitHub Actions and want native cache integration

For teams building container images alongside application code, combining sccache with a self-hosted container build pipeline (see our Buildah vs Kaniko vs Earthly comparison) gives end-to-end build acceleration.

Use ccache When:

• You primarily compile C/C++ code
• Builds run on persistent machines (dedicated CI runners, developer workstations)
• You want zero configuration — install and it works
• You need maximum cache hit rates (ccache’s C++-specific optimizations are mature)
• Simplicity matters more than remote sharing

Use Icecream When:

• You have multiple idle machines that can serve as compilation workers
• Your C/C++ project is too large for single-machine compilation
• You want to scale compilation horizontally across a build cluster
• Cache hit rates aren’t your bottleneck — raw compile speed is
• Your team works on the same codebase and benefits from shared toolchain distribution

Performance Expectations

The real-world impact depends heavily on your project’s compilation patterns. Rust projects tend to benefit most from sccache’s remote caching because cargo build recompiles all dependencies unless cached. Large C++ monorepos benefit most from Icecream because the compiler parallelism is distributed across machines.

Combining Tools

These tools are not mutually exclusive. A common production setup uses:

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# Icecream for distributed compilation
# + ccache on each worker for local caching of compiled objects
export CC="icecc ccache gcc"
export CXX="icecc ccache g++"

This gives you both horizontal distribution (Icecream) and per-worker caching (ccache). Similarly, you can use sccache with --dist flag for Rust compilation while falling back to local caching for other languages.

For complete CI pipeline optimization, consider pairing build caching with dependency automation tools to minimize unnecessary rebuilds — our Renovate vs Dependabot vs UpdateCLI guide covers automated dependency management.

FAQ

What is the difference between sccache and ccache?

ccache stores compiled objects on local disk only, making it ideal for single machines and persistent CI runners. sccache extends this concept with remote storage backends (S3, GCS, Redis, Azure), allowing cache sharing across ephemeral CI containers and distributed build fleets. sccache also supports more languages (Rust, Go, CUDA) while ccache focuses on C/C++.

Can I use sccache and ccache together?

Yes. You can configure sccache as the wrapper for Rust and Go compilation while using ccache for C/C++. Alternatively, you can chain them: export CC="ccache gcc" for local caching and export RUSTC_WRAPPER=sccache for remote Rust caching. They operate on different compilers and don’t conflict.

Does Icecream work with Rust or Go?

No. Icecream is specifically designed for C and C++ compilation using GCC or Clang. For Rust projects, use sccache with a remote backend. For Go, sccache supports go build via the SCCACHE_GCS or SCCACHE_S3 backends with Go’s build cache mechanism.

How much disk space does a build cache need?

For a medium-sized C++ project, expect 2-10 GB of cache. For large projects (Chromium, LLVM), caches can exceed 100 GB. Configure CCACHE_MAXSIZE or SCCACHE_CACHE_SIZE to cap usage. Use LRU eviction policies (Redis maxmemory-policy allkeys-lru) to automatically prune old entries.

Is Icecream suitable for CI/CD pipelines?

Yes, but it requires a persistent scheduler and at least 2-3 worker machines to see meaningful speedup. For small teams or infrequent builds, sccache or ccache are simpler and more cost-effective. Icecream shines in organizations with dedicated build infrastructure and large C/C++ codebases.

Can I run sccache without a remote backend?

Yes. If you don’t configure a remote backend, sccache falls back to local disk storage, functioning similarly to ccache. However, ccache has more mature local caching optimizations for C/C++, so for local-only use cases, ccache is generally the better choice.

What happens when the cache is full?

Both sccache and ccache use LRU (Least Recently Used) eviction. When the cache reaches its configured maximum size, the oldest unused entries are automatically removed. Redis backends support allkeys-lru eviction, and sccache’s local mode has configurable size limits via SCCACHE_CACHE_SIZE.

How do I monitor cache performance?

• ccache: Run ccache --show-stats to see hit rates, cache size, and miss reasons
• sccache: Run sccache --show-stats for similar metrics including backend-specific stats
• Icecream: Use icemon for real-time cluster monitoring or icecc --status for scheduler info

Conclusion

Choosing between sccache, ccache, and Icecream comes down to your team’s language stack and infrastructure:

• sccache is the best all-rounder for modern, multi-language projects with cloud CI. Its support for Rust, Go, and CUDA alongside traditional C/C++ makes it the only tool that covers the full spectrum of compiled languages, and its cloud storage backends solve the cache-sharing problem that local-only tools can’t.

• ccache remains the gold standard for C/C++ compilation on individual machines. Its simplicity, zero configuration, and decades of optimization make it the default choice for developer workstations and persistent CI runners.

• Icecream is the right choice when raw compilation throughput is your bottleneck and you have multiple machines available. By distributing compilation across a network, it can reduce build times from hours to minutes for large C++ codebases.

For most teams starting their build caching journey, we recommend sccache with a Redis backend — it gives you remote sharing, multi-language support, and a simple Docker deployment in one package.