Different Types of LLM Access

 · Gifted

Introduction

Large language models (LLMs) are becoming core components of many applications. However, developers have several distinct ways to access these models, each with its own trade‑offs in cost, latency, flexibility, and control. In this post we’ll break down three common approaches:

  1. Direct API access – calling the provider’s endpoint (e.g., OpenAI, Anthropic, Cohere) directly.
  2. Inference routing services – using a meta‑layer such as OpenRouter that aggregates many providers behind a single API.
  3. Local / self‑hosted deployments – running the model yourself on‑premise or in the cloud.

Understanding the differences helps you pick the right solution for your product’s requirements.

1. Direct API Access

  • How it works – You obtain an API key from a cloud provider and send HTTP requests with your prompt. The provider handles model loading, scaling, and hardware management.
  • Pros
  • Immediate access to the latest model versions.
  • No infrastructure to maintain.
  • Pay‑per‑token pricing keeps costs predictable for low‑volume use.
  • Cons
  • Vendor lock‑in and limited customisation.
  • Latency depends on network distance and provider load.
  • Cost can scale quickly with high‑volume usage.
  • Typical use‑cases – Chatbots, quick prototypes, and SaaS products that need the newest capabilities without investing in GPU hardware.

2. Inference Routing (e.g., OpenRouter)

  • How it works – A routing service sits between your code and multiple model providers. You call a single endpoint, and the service forwards the request to the selected backend based on configuration, pricing, or model capabilities.
  • Pros
  • Seamless model switching without code changes.
  • Ability to compare pricing and performance across providers.
  • Often includes additional safety filters or developer‑friendly features.
  • Cons
  • Adds an extra network hop, increasing latency.
  • You still depend on third‑party infrastructure.
  • Some routing services impose their own rate limits or usage caps.
  • Typical use‑cases – Multi‑model experimentation, applications that need fallback models, or teams that want to optimise cost by routing cheaper models for simple tasks.

3. Local / Self‑Hosted Deployments

  • How it works – You download a model checkpoint (e.g., LLaMA, Mistral) and run it on your own GPU or CPU hardware, usually via a framework like vLLM, llama.cpp, or Ollama.
  • Pros
  • Full control over model version, hyper‑parameters, and data privacy.
  • No per‑token fees; you only pay for the hardware you own.
  • Zero external latency – everything runs locally.
  • Cons
  • Significant upfront hardware cost (GPU memory, storage).
  • Ongoing maintenance: updates, security patches, scaling.
  • Complexity of setup and potential for bugs in inference pipelines.
  • Typical use‑cases – Enterprise environments with strict data‑privacy regulations, high‑throughput workloads where per‑token pricing is prohibitive, or research projects needing custom fine‑tuning.

Choosing the Right Approach

Factor Direct API Inference Routing Local / Self‑Hosted
Cost (small scale) Low – pay per token Low – still pay per token, but you can switch to cheaper models High – hardware purchase required
Cost (large scale) High – token costs add up Medium – you can route to cheaper models Low – fixed hardware cost
Latency Moderate – depends on provider location Higher – extra hop Low – local compute
Control / Customisation Minimal Moderate (choose model) Full
Data Privacy Provider‑dependent Provider‑dependent Full (data never leaves your premises)

TL;DR

  • Use direct API for speed, simplicity, and when you need the newest features.
  • Use routing services when you want flexibility across providers without code churn.
  • Use local/self‑hosted when privacy, cost at scale, or ultra‑low latency are critical.

Conclusion

There’s no one‑size‑fits‑all answer. Evaluate your product’s constraints—budget, latency, data‑privacy, and development resources—to pick the most appropriate LLM access method. As the ecosystem evolves, you may even combine these approaches: a routing layer that falls back to a locally‑hosted model when the primary API is unavailable.

Happy prompting!