Why do AI teams write their own C and C++ inference engines?

When a company needs to run an AI model in production, the default choice for most teams is a well-established framework: PyTorch for research and increasingly production use, TensorFlow Serving, ONNX Runtime, or one of several managed inference platforms offered by cloud providers. Yet a recurring pattern among AI infrastructure teams, documented recently in a widely shared engineering blog post, is the decision to instead write a custom inference engine from scratch in C or C++ — languages that sit much closer to the hardware than the Python-based tooling most machine learning engineers use day to day.

The decision seems counterintuitive at first. General-purpose frameworks exist precisely because building an inference engine is hard: it requires handling model formats, memory management, hardware-specific optimization, batching, and numerical precision correctly, all without introducing bugs that silently corrupt a model's output. Writing that machinery in C or C++, rather than reusing a framework that has already solved these problems, means taking on months of additional engineering work.

The case for doing it anyway usually starts with performance. Frameworks built for flexibility and ease of use carry overhead — abstraction layers, dynamic dispatch, memory allocation patterns optimized for developer convenience rather than raw speed — that a purpose-built engine can strip away. For teams running inference at large scale or with tight latency budgets, shaving milliseconds off each request by eliminating unnecessary layers of abstraction can translate directly into lower infrastructure costs or a materially better user experience.

Memory footprint is the second major driver. General-purpose frameworks are designed to support a wide range of model architectures and use cases, which means they carry code paths and dependencies that any single deployment doesn't need. A custom engine built for one specific model or family of models can be stripped down to exactly what that use case requires, producing a dramatically smaller binary and lower memory usage — a difference that matters enormously when a model needs to run on constrained hardware.

That constraint becomes decisive in edge and local-inference deployment scenarios: running a model on a phone, an embedded device, a laptop, or any environment without a full data-center GPU stack available. In these settings, a multi-gigabyte framework dependency with a large runtime footprint may simply not fit, or may leave too little headroom for the rest of the application. A lean C or C++ engine, compiled specifically for the target hardware, can run in a fraction of the memory and disk space.

Dependency management is a related but distinct motivation. General-purpose ML frameworks tend to pull in large dependency trees — other libraries, specific versions of CUDA or other hardware drivers, Python runtime requirements — that can complicate deployment, create version conflicts, or simply make an application harder to ship as a single, self-contained binary. A custom-built engine with minimal external dependencies avoids much of that complexity, at the cost of having to reimplement functionality those dependencies would otherwise provide.

Portability across hardware is another factor teams cite. Writing close to the metal in C or C++ makes it more practical to target unusual or resource-constrained hardware directly — different CPU architectures, specialized accelerator chips, or embedded processors — without waiting for a general-purpose framework to add official support. Teams building for hardware that mainstream frameworks don't prioritize sometimes find that writing their own engine is faster than waiting for, or working around gaps in, someone else's.

The trade-off, and it is a real one, is engineering cost and ongoing maintenance burden. A custom inference engine has to be built, tested, and kept correct across every model update, every new hardware target, and every numerical edge case a general-purpose framework would otherwise have already handled. Teams that take this path are, in effect, taking on the job of a small framework-maintenance team as a permanent cost of doing business, which only makes sense when the performance, footprint, or deployment gains are large enough to justify it.

This is why the practice tends to appear at a specific point in a company's growth: after the constraints of general-purpose tooling have become a measurable, recurring cost — in compute spend, in what hardware a product can support, or in latency that affects the product itself — rather than as a starting point for a new project. Most teams are well served by existing frameworks for as long as those frameworks' overhead doesn't matter to their specific use case.

The broader lesson for developers outside AI infrastructure is less about C and C++ specifically and more about a general principle in systems engineering: general-purpose tools optimize for the common case and flexibility, and that trade-off is usually the right one — until a specific deployment target, performance requirement, or resource constraint makes the overhead of that flexibility a cost worth eliminating by building something narrower and closer to the hardware instead.