Replicate + LiteLLM Integration Is Broken — Here's a Reliable Alternative for Developers (2026)

Replicate + LiteLLM Integration Is Broken — Here's a Reliable Alternative for Developers (2026) Your inference pipeline is silently failing. Here's why — and what to do about it. If you've been using LiteLLM as a unified API gateway with Replicate as a backend , you may have hit a frustrating wall: your pipeline breaks mid-inference with cryptic errors, and you can't figure out why. You're not alone. This is a real, documented bug — and it's been affecting developers since late 2025. Section 1: What Is the Replicate + LiteLLM Bug? The root cause is a non-terminal state handling failure in LiteLLM's Replicate handler. When you send a request to Replicate via LiteLLM, Replicate's API returns a prediction object with a status field. For fast models, the status quickly reaches "succeeded" . But for slow-starting models (especially reasoning models, large video models, or cold-booted containers), the status goes through intermediate states like "starting" and "processing" before completing.