I'm building a customer-facing product and need fast inference endpoints - what should I use?

If you need fast, customer-facing inference endpoints, the best choice depends on your priority:

Best overall for most teams

Hosted model API with an optimized inference provider
Use a provider that offers:

• Low-latency endpoints
• Autoscaling
• Streaming responses
• Regional deployment
• Dedicated capacity / reserved throughput if traffic is steady

This is usually the fastest path to production because you avoid managing GPUs and scaling.

If you want the lowest latency at high volume

Self-host an optimized inference stack Use:

• vLLM or TGI (Text Generation Inference) for LLM serving
• TensorRT-LLM for maximum performance on NVIDIA GPUs
• KServe / Triton Inference Server for broader ML workloads

This is a good choice if you need:

• Predictable performance
• Cost control at scale
• Custom routing / batching
• Data residency or compliance requirements

Practical recommendation

• Early stage / variable traffic: managed API or hosted inference service
• Growing product with steady traffic: reserved/dedicated hosted endpoints
• Large scale / strict latency targets: self-host with vLLM or TensorRT-LLM

What matters most for fast endpoints

To get real speed, look for:

• Short network path to users
• Streaming
• Prompt caching
• Batching
• Quantized models
• Warm instances / no cold starts
• Smaller model when possible

If you’re asking specifically “what should I use?”

A solid default is: vLLM on GPU-backed infrastructure if you’re self-hosting, or a managed inference API with dedicated throughput if you want speed without ops.

If you tell me:

1. model type (LLM, vision, embeddings, etc.)
2. expected traffic
3. latency target
4. cloud preference

…I can recommend a specific stack.