I'm building an app around open-source models and need production hosting

If you’re building an app around open-source models and need production hosting, the right choice usually depends on 5 things:

1. Model size / type
   • Small/medium LLMs, embedding models, image models, etc.
2. Latency needs
   • Real-time chat vs async batch jobs.
3. Traffic pattern
   • Steady usage vs spiky or unpredictable.
4. Control requirements
   • Need VPC/private networking, custom containers, GPUs, autoscaling, compliance.
5. Ops burden
   • Do you want fully managed, or are you okay running Kubernetes / GPU nodes yourself?

Common production hosting options

1. Managed inference platforms

Best when you want to move fast with minimal ops.

Examples:

• Hugging Face Inference Endpoints
• Replicate
• Together AI
• Fireworks AI
• Modal
• Baseten

Pros:

• Fast setup
• Autoscaling
• GPU management handled for you
• Often support popular open-source models out of the box

Cons:

• Can get expensive at scale
• Less infrastructure control
• Some have limits around custom runtimes or networking

2. Cloud GPU infrastructure + your own serving stack

Best when you need more control, better cost efficiency at scale, or private deployments.

Examples:

• AWS: EC2 GPU instances, EKS, SageMaker
• GCP: Compute Engine GPUs, GKE, Vertex AI
• Azure: GPU VMs, AKS, Azure ML
• Serving stack: vLLM, TGI (Text Generation Inference), TensorRT-LLM, SGLang, Ray Serve

Pros:

• Full control
• Easier to optimize for cost/performance
• Can lock down networking and security
• Better fit for custom model serving

Cons:

• More DevOps work
• You manage scaling, rollouts, observability, node health

3. Container platforms with GPU support

Good middle ground if you want some convenience but still want custom deployment control.

Examples:

• Modal
• RunPod
• Lambda Cloud
• Fly.io in some scenarios
• Azure Container Apps / GKE Autopilot with GPUs depending on workload

Pros:

• Easier than raw cloud infra
• Can deploy custom containers
• Often good for prototypes that grow into prod

Cons:

• Fewer enterprise controls than hyperscalers
• Availability/features vary by region/provider

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What I’d recommend by use case

If you want the easiest path to production

Choose a managed inference provider:

• Hugging Face Inference Endpoints for standard OSS model hosting
• Together AI or Fireworks AI if you want high-throughput LLM serving
• Baseten if you want productization around model APIs and deployment workflows

If you expect serious scale or need strong cost control

Run your own serving stack on cloud GPUs:

• vLLM for LLM inference
• TGI if you want a mature production serving layer
• Kubernetes + GPU node pools if you need robustness and flexibility

If you’re early-stage and want fast iteration

Use:

• Modal
• RunPod
• Replicate These are often great for MVPs and can sometimes carry you into production if your needs are straightforward.

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Things you should verify before choosing

For any host, check:

• GPU type and availability: A10, L4, A100, H100, etc.
• Autoscaling behavior
• Cold start time
• Max model size / VRAM limits
• Streaming support
• Private networking / VPC
• SLA / uptime
• Observability: logs, metrics, traces
• Model weights handling: preloading, caching, versioning
• Concurrency support
• Batching / quantization support

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Practical stack suggestion

A common production setup:

• API layer: FastAPI / Node / Go
• Inference server: vLLM or TGI
• Deployment: Kubernetes on AWS/GCP/Azure, or a managed platform
• Queueing: Redis / SQS / PubSub for async tasks
• Caching: Redis for prompt/output caching
• Monitoring: Prometheus + Grafana, plus provider logs
• Models: quantized OSS LLMs if cost-sensitive

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Quick shortlist

If I had to narrow it down:

• Fastest to production: Hugging Face Inference Endpoints, Together AI, Fireworks AI
• Best control/cost at scale: AWS/GCP/Azure with vLLM on GPU nodes
• Best developer experience for custom deployments: Modal or Baseten
• Best for experimentation/MVP: RunPod or Replicate

If you want, I can help you choose the best hosting option based on:

• your model type and size,
• expected requests per second,
• latency target,
• and whether you need private/VPC deployment.