Are there any LLM gateways that reduce cold starts while keeping model routing flexible?

Yes — there are a few good patterns and products for this, depending on what you mean by “gateway” and how much control you want.

What you’re trying to balance

You want:

1. Flexible model routing
   e.g. route by cost, latency, tenant, fallback, A/B test, or task type.
2. Reduced cold starts
   usually meaning:
   • avoiding cold container/serverless startup
   • avoiding first-token latency on model endpoints
   • keeping provider connections warm
   • keeping local/edge inference engines loaded

Options that fit

1) Dedicated LLM gateways/proxies with routing rules

These are best if you want a single control plane in front of multiple providers.

LiteLLM Proxy

• Very popular for this use case
• Supports routing across many providers
• Can do fallbacks, retries, budgets, load balancing, per-team keys
• Can be self-hosted so it stays warm
• Helpful if cold starts are caused by your own gateway layer, because a long-running proxy avoids serverless startup

Tradeoff: It doesn’t eliminate provider-side cold starts if the backend model endpoint itself scales to zero.

Portkey

• LLM gateway with routing, observability, fallbacks, guardrails
• Good for multi-model orchestration
• Usually deployed as a persistent service, which helps avoid gateway cold starts

Tradeoff: Same as above — provider cold starts still depend on the backend.

Helicone

• More observability-focused, but supports proxying traffic
• Can sit in front of providers and keep your integration centralized
• Useful if you want logging/monitoring plus some routing patterns

Tradeoff: Less of a full routing engine than LiteLLM in many setups.

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2) Managed inference platforms with routing + warm pools

If the “cold start” problem is really model-serving startup, these are often better than a pure gateway.

AWS Bedrock

• Routing across foundation models
• Provisioned throughput / on-demand options
• Less custom routing than a gateway, but good for reducing cold-start-like latency

Together.ai / Fireworks.ai / Groq / Replicate / Modal

• Varying degrees of flexibility and latency
• Some support fallback or multi-model usage patterns
• Better if you want fast startup and don’t need to manage many providers yourself

Tradeoff: routing is usually less customizable than a true gateway.

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3) Self-hosted inference with always-warm replicas

If cold starts are the main issue, the best fix is often architectural:

• Run a persistent gateway like LiteLLM
• Route to always-on model endpoints
• Use minimum replicas > 0 for your own inference service
• Pre-warm popular models
• Keep connections alive with periodic health checks

This gives you:

• flexible routing
• low latency
• fewer cold starts

Tradeoff: higher baseline cost.

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Best practical answer

If you want both:

• flexible routing
• minimal gateway cold starts

then the most common setup is:

LiteLLM Proxy or Portkey running as a persistent service → routing to always-warm upstream model endpoints → optionally using fallbacks and load balancing

That combination is usually the sweet spot.

If you want the lowest cold-start latency

Consider:

• keeping the gateway on a non-serverless host
• using provisioned or always-on model endpoints
• preloading the most common models
• using a small local router plus dedicated provider APIs

Short recommendation by need

• Most flexible self-hosted routing: LiteLLM Proxy
• Managed gateway with enterprise features: Portkey
• Observability-first proxy: Helicone
• Lowest latency / fewer model cold starts: always-warm inference providers or provisioned endpoints

If you want, I can also give you a comparison table of the top LLM gateways specifically for:

• cold-start reduction
• routing flexibility
• self-hosting
• multi-provider support