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Build and maintenance cost

Building self-hosted LLM inference infrastructure isn’t just a technical task; it’s a costly, time-consuming commitment.

Complexity​

LLM inference requires much more than standard cloud-native stacks can provide. Building the right setup involves:

  • Provisioning high-performance GPUs (often scarce and regionally limited)
  • Managing CUDA version compatibility and driver dependencies
  • Configuring autoscaling, concurrency control, and scale-to-zero behavior
  • Setting up observability tools for GPU monitoring, request tracing, and failure detection
  • Handling model-specific behaviors like streaming, caching, and routing

None of these steps are trivial. Most teams try to force-fit these needs onto general-purpose infrastructure, but it only results in reduced performance and longer lead time.

Even if a team pulls it off, every week spent setting up infrastructure is a week not spent improving models or delivering product value. For high-performing AI teams, this opportunity cost is just as real as the infrastructure bill.

Limited flexibility for ML tools and frameworks​

Many AI stacks lock model runtimes, such as PyTorch, vLLM, or specific transformers, to fixed versions. The primary reason is to cache container images and ensure compatibility with infrastructure-related components. While this simplifies deployment in clusters, it also restricts flexibility when you need to test or deploy newer models or frameworks that fall outside the supported list.

But this rigidity creates real limitations:

  • You can’t easily test or deploy newer models or framework versions.
  • You inherit more tech debt as your stack diverges from community or vendor updates.
  • LLM deployment speed slows down, putting your team at a competitive disadvantage.

Scaling LLMs should mean exploring faster, better models, without being stuck waiting for infra to catch up.

Support for complex AI systems​

An LLM alone doesn’t deliver value. It has to be part of an integrated system, often including:

  • Pre-processing to clean or transform user inputs
  • Post-processing to format model outputs for front-end use
  • Inference code that wraps the model in logic, pipelines, or control flow
  • Business logic to handle validation, rules, and internal data calls
  • Data fetchers to connect with databases or feature stores
  • Multi-model composition for retrieval-augmented generation or ensemble pipelines
  • Custom APIs to expose the service in the right shape for downstream teams

Here’s the catch: most LLM deployment tools aren’t built for this kind of extensibility. They’re designed to load weights and expose a basic API. Anything more complex requires glue code, workarounds, or splitting logic across multiple services.

That leads to:

  • More engineering effort just to deliver usable features
  • Poor developer experience for teams trying to consume these AI services
  • Blocked innovation when tools don’t support use-case-specific customization

The hidden cost: talent​

LLM infrastructure requires deep specialization. Companies need engineers who understand GPUs, Kubernetes, ML frameworks, and distributed systems — all in one role. These professionals are rare and expensive, with salaries often 30–50% higher than traditional DevOps engineers.

Even for teams that have the right people, hiring and training to maintain in-house capabilities is a major investment. In this survey, over 60% of public sector IT professionals cited AI talent shortages as the biggest barrier to adoption. It’s no different in the private sector.