Here’s the quick decision framework:

1) Use serverless inference if:

• Traffic is spiky/unpredictable
• You want fastest time-to-launch
• You don’t want to manage infra, autoscaling, or GPU scheduling
• You can tolerate:
  • some cold-start latency
  • less control over batching/model serving stack
  • limits on custom dependencies, model size, or runtime tuning

Best for: prototypes, low/irregular traffic, internal tools, bursty workloads, PoCs.

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2) Use a managed endpoint if:

• You need reliable low latency for a real-time app
• Traffic is steady or moderately variable
• You want the platform to handle:
  • deployment
  • scaling
  • health checks
  • rolling updates
• You still want some operational simplicity, but more control than serverless

Best for: production apps with predictable usage, customer-facing APIs, latency-sensitive inference.

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3) Use self-hosted Triton or KServe if:

• You need maximum control over:
  • model runtime
  • batching
  • GPU utilization
  • custom preprocess/postprocess
  • multi-model serving
  • observability and networking
• You have:
  • platform/ML infra expertise
  • enough traffic to justify the ops overhead
  • strict cost/performance requirements
• You want to optimize for:
  • high throughput
  • low p99 latency
  • advanced deployment patterns
  • specialized hardware/software stack

Best for: mature production systems, large-scale inference, highly optimized low-latency services, teams with MLOps/platform engineering.

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Simple rule of thumb

• Need fastest setup + bursty traffic? → Serverless
• Need production reliability with low ops burden? → Managed endpoint
• Need deep optimization/control and have infra support? → Triton/KServe

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For a real-time app, my default recommendation:

If this is a customer-facing real-time app, start with a managed endpoint unless:

• your traffic is highly bursty and low-volume → serverless
• you already know you need aggressive optimization or custom serving → Triton/KServe

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A practical comparison

Option	Latency	Ops burden	Cost efficiency	Control	Best fit
Serverless	Variable (cold starts)	Very low	Good for spiky traffic	Low	Prototypes, bursty apps
Managed endpoint	Low and stable	Low-medium	Good for steady traffic	Medium	Production real-time apps
Triton/KServe	Lowest potential	High	Best at scale if tuned	High	Large/optimized deployments

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If you want, I can turn this into a decision tree based on your expected QPS, latency target, model size, and budget.