When AI Inference Cost Becomes a Routing Problem, Who Decides Where Each Request Goes?

The Problem

AI service teams are solving two problems at once. First, inference now eats 30–60% of revenue. Second, “where to send each request” has outgrown the old OpenAI-vs-Anthropic binary. Cerebras’s IPO expanded the menu, Groq, SambaNova, Nvidia Inference Cloud, Together AI, and Fireworks AI all offer the same models with different latency, cost, and throughput profiles.

Existing API gateways handle routing statically. Rules written during low-traffic days survive into high-traffic seasons. Meanwhile, unit prices shift hourly, chip supply tightens unpredictably, and routing decisions are still tweaked by a human once a quarter.

Why Now

Cerebras’s $66B market cap isn’t just a successful IPO, it’s institutional validation that inference is a multi-vendor market. OpenAI, G42, MBZUAI, and AWS already run on Cerebras. Anthropic and Google are opening their own silicon (TPU, Trainium) to outside customers. Within six months, routing decisions will be the single biggest cost lever.

The other signal: OpenAI depends on Cerebras while building its own datacenters. The largest LLM company in the world refuses to single-source. For smaller teams, the operational complexity of multi-vendor inference is exactly what a tool should abstract away.

How to Build It

flowchart LR
    A[App request] --> B[Router SDK]
    B --> C{Policy engine}
    C -->|cost-optimal| D[Cerebras]
    C -->|low latency| E[Groq]
    C -->|quality SLA| F[Anthropic Claude]
    C -->|batch/bulk| G[OpenAI Batch]
    D & E & F & G --> H[Observability]
    H --> I[Cost/Quality dashboard]
    I --> C

MVP has three components:

1. OpenAI-compatible SDK: Drop-in replacement, one line of code: client = OpenAI() → client = MultiRouter(). Zero migration friction.
2. Policy engine: Routes based on request metadata (model, max_tokens, latency budget, SLA tier) and live unit pricing. Start rule-based, evolve to predictive ML.
3. Observability layer: Records actual latency, cost, and quality score (via eval API) per request. Quarterly auto-reports.

Pricing: $0.05–$0.10 per million tokens routed (1–5% of underlying spend). For teams spending $100K+/month, the ROI is obvious.

Success Criteria

• Core assumption: Price gaps between backends stay ≥30%. Holds if Cerebras and Groq’s aggressive launch pricing sticks.
• Validation: Five beta customers, free for one month, then 20% of measured savings as commission. If three out of five save $5K+/month, the market exists.
• Main risk: A major LLM vendor releases a unified routing API and absorbs this layer. But the demand to avoid single-vendor lock-in is a structurally different market.