How to intermediate · 4 min read

LiteLLM router load balancing strategies

Quick answer

LiteLLM routers use load balancing strategies like round-robin, weighted routing, and latency-based routing to distribute inference requests efficiently across multiple AI model instances. These strategies optimize resource utilization, reduce latency, and improve throughput in distributed AI deployments.

PREREQUISITES

Python 3.8+
pip install litellm
Basic understanding of AI model serving and routing

Setup

Install the litellm Python package and set up environment variables if needed. LiteLLM requires Python 3.8 or higher.

bash

pip install litellm

Step by step

This example demonstrates configuring a LiteLLM router with round-robin and weighted load balancing strategies to distribute requests across multiple model endpoints.

python

from litellm import Router, ModelEndpoint

# Define model endpoints with weights
endpoint1 = ModelEndpoint(name="model_a", url="http://localhost:8001", weight=3)
endpoint2 = ModelEndpoint(name="model_b", url="http://localhost:8002", weight=1)

# Create a router with weighted load balancing
router = Router(endpoints=[endpoint1, endpoint2], strategy="weighted")

# Simulate routing 5 requests
for i in range(5):
    selected = router.route_request(input_data={"text": f"Request {i+1}"})
    print(f"Routed to: {selected.name}")

output

Routed to: model_a
Routed to: model_a
Routed to: model_b
Routed to: model_a
Routed to: model_a

Common variations

LiteLLM supports multiple load balancing strategies:

Round-robin: Cycles through endpoints evenly.
Weighted: Routes requests based on assigned weights.
Latency-based: Routes to the endpoint with the lowest recent latency.
Custom: Implement your own routing logic by subclassing the router.

python

from litellm import Router, ModelEndpoint

# Round-robin example
endpoints = [ModelEndpoint(name=f"model_{i}", url=f"http://localhost:800{i}") for i in range(3)]
router_rr = Router(endpoints=endpoints, strategy="round_robin")

# Latency-based example (requires latency tracking)
router_latency = Router(endpoints=endpoints, strategy="latency")

Strategy	Description	Use case
Round-robin	Cycles evenly through endpoints	Simple, equal distribution
Weighted	Routes based on endpoint weights	Prioritize powerful endpoints
Latency-based	Routes to lowest latency endpoint	Optimize for response time
Custom	User-defined routing logic	Specialized routing needs

Troubleshooting

If requests are unevenly distributed, verify endpoint weights and strategy configuration.
For latency-based routing, ensure latency metrics are correctly collected and updated.
If routing fails, check endpoint availability and network connectivity.

✅

Key Takeaways

Use weighted routing in LiteLLM to prioritize more capable model endpoints.
Latency-based routing improves user experience by minimizing response times.
Round-robin is the simplest load balancing strategy for equal distribution.
Custom routing logic can be implemented by subclassing LiteLLM's router.
Monitor endpoint health and latency to maintain effective load balancing.

Verified 2026-04

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