Inference Provider Comparison Report: The Token Factory Landscape

Compare the biggest LLM inference providers across per-token pricing, throughput, deployment models, and enterprise readiness.

What’s Inside

This report cuts through the problem that makes inference pricing hard to compare: every provider serves a different mix of models at different prices. We anchor on two reference models nearly every provider serves so the comparison is apples-to-apples.

Reference-Model Pricing Tables:

• Llama 3.3 70B (mid-size dense) per-token pricing across every major host
• DeepSeek V4 Pro (leading open-weight MoE) pricing, throughput, and time-to-first-token
• Why the same model spans a ~9x price range depending only on who serves it

Throughput & Latency Benchmarks:

• Output tokens/sec and time-to-first-token across GPU and custom-silicon hosts
• How Groq, Cerebras, and SambaNova compare to GPU-based serving

Deployment Model Analysis:

• Serverless vs dedicated endpoints: where the cost crossover actually is
• Serverless GPU platforms (RunPod, Modal, Replicate) and cold-start latency
• Self-hosting economics: the real break-even and the 3-5x platform-engineering multiplier

Provider Profiles:

• Nebius Token Factory, Fireworks, Together AI, DeepInfra
• Groq, Cerebras, SambaNova, Baseten
• Amazon Bedrock, Google Vertex AI, Azure AI Foundry
• RunPod, Modal, Replicate, OpenRouter

Recommendations by Workload:

• Lowest cost, moderate volume
• Lowest latency / highest throughput
• Production serving with compliance
• Custom models or spiky traffic
• High volume or strict data control (self-host)

Who This Report Is For

• Infrastructure and platform engineers choosing an inference provider
• ML engineers deciding between per-token APIs and self-hosted serving
• DevOps teams building AI applications on open models
• CTOs making build-vs-rent-vs-host decisions for inference

Download the report to make an informed decision backed by verified pricing, throughput benchmarks, and deployment analysis.