Enterprise teams running open-source AI models in production get infrastructure that handles the heavy lifting without MLOps expertise. Nebius Token Factory delivers state-of-the-art open-source model inference with sub-second latency and transparent per-token costs. The service runs dedicated endpoints that autoscale throughput based on demand, with internal validation ensuring accuracy, consistency and multilingual reliability across all deployed models.
Speed matters here. Time-to-first-token arrives in under a second, with European deployments running up to 4.5 times faster than competing inference providers. Speculative decoding pushes throughput further, with DeepSeek R1 0528 hitting 248 output tokens per second and ranking in the top two worldwide for that model. That's up to three times faster than major hyperscalers on the same workload.
The service offers two distinct flavors for each model: Fast and Base. Fast prioritizes speed for latency-sensitive applications. Base optimizes for economy when response time matters less than cost per token. Teams can switch between these flavors instantly without redeployment or configuration changes, adapting to shifting workload requirements on the fly.
Multi-region routing distributes requests across geographic zones automatically. Zero-retention security means prompts and responses don't get stored, addressing compliance requirements for sensitive data processing. Volume discounts kick in as usage scales, with Prosus documenting cost reductions up to 26 times compared to proprietary models while handling 200 billion tokens daily. Independent verification from Artificial Analysis confirmed up to three times better cost efficiency compared to alternatives.
The serving pipelines get optimized at the infrastructure level, eliminating GPU and cluster management tasks. Developers work with standard APIs rather than wrestling with deployment configurations. Hugging Face integration provides access to model repositories without custom setup work.
Llama-405B runs more than 2.5 times cheaper than comparable proprietary alternatives while delivering similar quality. That cost advantage compounds across RAG implementations, contextual systems and agentic workflows where token volumes climb quickly. Autoscaling handles traffic spikes without rate throttling, with the Prosus case demonstrating production capacity reaching 200 billion tokens in a single day.
The service targets enterprises running production AI workloads with open-source models. Companies building conversational interfaces, document processing systems or autonomous agents get predictable costs without surprises. Teams avoid the infrastructure complexity that typically comes with self-hosted model serving while maintaining control over model selection and deployment patterns.
