Deploy to enterprise scale by self-hosting with TitanML
TitanML’s self-hosting solution is built for enterprise scaling. It’s 90+% more cost-effective than API-based deployments, and it’s far more robust. Scale affordably with our Enterprise Inference Stack.
Built for enterprise-level scaling
TitanML enables enterprise scalability by providing the trusted battle-tested foundational infrastructure for mission-critical systems.
Scale without the growing pains, leveraging our multithreaded server architecture, multi-GPU deployments, and batching optimizations.
Deploy dozens of models onto a single GPU with our batched LoRA adapters and optimize your hardware utilization.
Unusually, service-level agreements (SLAs) as standard
Scale without hidden costs and rate limits
- Scale without the constraints of API rate limits and unexpected costs. Unlike API-based deployments, our Enterprise Inference Stack is a self-hosted solution, which means 90+% cost savings.
- Deploy on even the smallest and cheapest hardware as TitanML's model optimizations and compressions mean you can d
- API-based models might seem cheaper in the short term—but once you start to scale, costs quickly spiral out of control. Looking to scale sustainably over the long term? Self-hosting with our Enterprise Inference Stack is the answer.
FAQs
Our Enterprise Inference Stack has been battle-tested in applications that serve millions of end users.
API based solutions, although cost effective in the short term, when deployed at scale, these costs spiral. Many enterprises have been surprised by just how costly this method of LLM deployment becomes when they begin to scale. Since TitanML is a self-hosted solution, customers save 90+% on enterprise-scale deployments.
Multi-GPU deployments allow the distributed inference of large language models (LLMs) by distributing those models across multiple GPUs. This allows for the inference of larger models and enables larger batch sizes. It is advantageous for applications which require high throughput, reduced latency, and efficient utilization of computational resources.