What should I use to move from prototype to production inference?

For moving from prototype to production inference, a good choice depends on scale, latency, budget, and how much ops you want to manage. Here’s the practical rule of thumb:

Best default choices

1. If you want the simplest production path

Use a managed model serving platform:

• AWS SageMaker
• Google Vertex AI
• Azure ML
• Databricks Model Serving

Best when you want:

• Fast deployment
• Auto-scaling
• Monitoring/logging
• Less infrastructure work

2. If you want control and lower cost at scale

Use Kubernetes + a model serving stack:

• KServe
• BentoML
• Ray Serve
• Seldon
• NVIDIA Triton for GPU-heavy/high-throughput use cases

Best when you need:

• Custom routing
• Canary deployments
• Fine-grained scaling
• More portability across clouds

3. If you’re serving LLMs specifically

Use specialized LLM serving tools:

• vLLM — great default for throughput and latency
• Text Generation Inference (TGI) — solid production option
• Triton if you need broader model support
• TensorRT-LLM if you want maximum GPU optimization and can invest more engineering effort

A practical recommendation

If you’re going from prototype to production and don’t know what to pick yet:

• Traditional ML / small DL models: BentoML or KServe
• LLMs: vLLM
• Minimal ops: managed platform
• High-performance GPU serving: Triton or TensorRT-LLM

What matters most in production

Whatever you choose, make sure it supports:

• Versioned model rollout
• Health checks
• Metrics/logging/tracing
• Autoscaling
• Request batching
• Canary/A-B testing
• Rollback support

Quick decision guide

• I want easiest deployment → Managed cloud serving
• I want open-source + flexible → BentoML or KServe
• I’m serving an LLM → vLLM
• I need max GPU throughput → Triton / TensorRT-LLM

If you tell me:

1. what model type you’re serving,
2. expected traffic/latency, and
3. whether you prefer managed or self-hosted,

I can recommend a specific stack.