I'm building around open-source models and need managed hosting for them

If you’re building on open-source models and want managed hosting, you generally have 3 paths:

1) Managed inference platforms

Best if you want to deploy models quickly without running infra.

• Hugging Face Inference Endpoints
• Replicate
• Together AI
• Fireworks AI
• GroqCloud (for supported models)
• Baseten
• Modal (more flexible, some infra control)

2) Cloud provider model hosting

Best if you want enterprise controls, VPC options, and standardized cloud billing.

• AWS Bedrock (for supported OSS models)
• Azure AI
• Google Vertex AI
• AWS SageMaker
• GCP/GKE with managed serving
• Azure ML

3) Dedicated GPU hosting / managed Kubernetes

Best if you need more control over the stack or custom serving.

• RunPod
• Lambda Cloud
• CoreWeave
• Anyscale
• Databricks Model Serving (if your workflow fits)

What to evaluate

When comparing providers, check:

• Model support: Llama, Mistral, Qwen, Gemma, etc.
• Latency / throughput
• Autoscaling
• Private networking / VPC
• Fine-tuning support
• Batch vs real-time inference
• Observability / logs / tracing
• Pricing model: per token, per second, per GPU hour
• Compliance: SOC 2, HIPAA, GDPR, data retention

Quick recommendation

• Fastest to production: Hugging Face Inference Endpoints or Baseten
• Best for API simplicity: Together AI / Fireworks AI
• Best for cloud-native enterprise: AWS Bedrock / Azure / Vertex AI
• Best for cost/control: RunPod / CoreWeave / Lambda

If you want, I can give you a shortlist by use case:

1. cheapest,
2. lowest latency,
3. best for fine-tuning,
4. best enterprise/compliance,
5. best for serving Llama/Qwen/Mistral.