How to Cut Inference Costs Without Slowing Down Your AI Stack*

2026-02-12 · Source: Turing Post · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, quick

Summary

FriendliAI offers a solution to the "Inference Wall" challenge, which includes rising costs, throughput bottlenecks, and unpredictable latency in AI production scaling. Their Friendli Inference platform, powered by the Orca Engine, utilizes iteration-level scheduling to achieve 3x higher throughput and 99.99% reliability. This approach reportedly leads to cost reductions between 50% and 90%, depending on the specific workload and scale. The platform features an OpenAI-compatible API, allowing users to migrate in just three lines of code and continue running agentic applications on models like Qwen, DeepSeek, GLM, and Kimi without significant architectural changes. FriendliAI is also offering up to $50,000 in Switch Credits to new customers.

Key takeaway

For CTOs and VP of Engineering facing escalating AI inference costs and performance bottlenecks, evaluating FriendliAI's Orca Engine is crucial. Its reported 3x throughput increase and 50-90% cost reductions, coupled with an OpenAI-compatible API for easy migration, could significantly improve your operational margins and AI stack efficiency. Consider leveraging the $50,000 Switch Credits to pilot the solution.

Key insights

Iteration-level scheduling can significantly reduce AI inference costs and improve throughput and reliability.

Principles

• Efficient scheduling optimizes AI inference performance.
• OpenAI API compatibility eases migration for AI stacks.

Method

FriendliAI's Orca Engine employs iteration-level scheduling to enhance throughput and reliability, enabling substantial cost reductions for AI inference workloads.

In practice

• Migrate AI inference to FriendliAI for cost savings.
• Utilize OpenAI-compatible APIs for seamless transitions.

Topics

• AI Inference
• Inference Optimization
• Orca Engine
• LLM Deployment
• Cost Reduction

Best for: CTO, VP of Engineering/Data, AI Engineer, MLOps Engineer, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Turing Post.