Best LLM Deployment 2026

Frequently Asked Questions

What is the difference between using an LLM API and self-hosting a model?▾

LLM APIs (OpenAI, Anthropic, Google) provide instant access to frontier models without infrastructure management - you pay per token with no GPU overhead. Self-hosting open-source models (Llama 3, Mistral, Qwen) on GPU servers gives cost advantages at very high volume, data privacy, and customization through fine-tuning. Most applications use APIs; high-volume or privacy-requiring applications explore self-hosting.

What platforms are best for hosting open-source LLMs?▾

Replicate and Modal are the easiest managed platforms for deploying open-source models - pay-per-inference with no GPU management. Together AI and Anyscale offer optimized inference for popular models. For self-managed GPU deployments, RunPod and Lambda Labs provide affordable GPU cloud instances. On-premise setups use Ollama (local), vLLM (production inference server), or TGI (HuggingFace's Text Generation Inference).

What is model fine-tuning and when does it make sense?▾

Fine-tuning adapts a pre-trained model to your specific domain or task by continuing training on your curated dataset. It makes sense when: you need specific output formats consistently, the model performs poorly on your domain-specific language, you need to reduce prompt length for cost savings, or RAG alone is insufficient for your accuracy requirements. Fine-tuning requires curated training data, GPU access, and model deployment infrastructure.

What is LLM deployment?▾

LLM deployment is the process of taking a language model from development into production so it reliably serves real users at scale. It involves serving the model as an accessible endpoint, optimizing inference for speed and cost, scaling to handle demand, managing latency, and monitoring performance and reliability. For self-hosted or open-source models especially, this requires GPU infrastructure and serving optimization, which deployment tools and platforms handle. Proper deployment ensures an AI application performs well under real traffic rather than only in testing. It is a distinct, technically demanding step between having a working model and running a dependable production AI service.

How do I deploy an open-source LLM?▾

You typically deploy it on infrastructure with GPUs, using serving frameworks and tools that optimize inference, or use a managed AI hosting platform that handles the infrastructure for you. Options range from self-managing GPU servers with serving software to using platforms like Replicate, Modal, or Hugging Face that provide model hosting and scaling. Considerations include GPU requirements for your model size, latency, cost, and expected traffic. For many teams, a managed hosting platform is the practical path since it handles the complex GPU serving and scaling, while self-hosting offers more control at the cost of managing the infrastructure yourself.

Is it cheaper to self-host an LLM or use an API?▾

It depends on your usage volume and model. Using a hosted model API is simpler and cost-effective at low to moderate volume, since you pay per use with no infrastructure to manage. Self-hosting an open-source model can be cheaper at high, consistent volume, since you avoid per-call fees, but you bear the cost and complexity of GPU infrastructure, which is significant and often underutilized at lower volume. Self-hosting also offers privacy and control benefits. Evaluate based on your volume, technical capacity, and whether privacy or customization matter, since APIs win on simplicity and low volume while self-hosting can win on cost at scale.