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Build real-time applications with TitanML

Build low latency, high throughout Enterprise RAG applications with TitanML. Our Enterprise Inference Stack reduces latency by 3-12x through state-of-the-art inference optimization. Gain the ability to build, deploy, and run real-time applications.  

Inference optimization
Cutting-edge optimization techniques

Build enterprise-grade RAG applications using TitanML's unique inference optimization strategies.

Maximize your application’s output speed without sacrificing accuracy.  Delight users and fulfill your projects' potential.  

Throughput
High throughput for enterprise-grade scaling
01
TitanML thrives under heavy loads. Our throughput optimizations provide consistent, high-speed performance even with the most demanding data influx (e.g. when processing millions of documents).
02
Ensure your AI application can handle intense workloads with the agility and reliability your enterprise requires.
Real-time applications
Build real-time applications
  • Speed is of the essence when building real–time applications.   
  • Gain a 3-12x latency improvement with Titan Takeoff. 
  • Seamlessly develop real-time applications like chatbots and RAG applications. 
FAQ

FAQs

01
What is inference optimization?

Inference optimization is the process of making machine learning models run quickly at inference time. This might include model compilation, pruning, quantization, or other general purpose code optimizations. The result improves efficiency, speed and resource utilization. Our Enterprise Inference Stack has been built by experts in inference optimization and includes the best-in-class inference optimization methods as standard.

02
What optimization techniques does TitanML use to accelerate AI inference times?

The inference optimization techniques can be found on our technology page.

03
How much can TitanML's optimization techniques speed up my current ML model inference?

Our clients report speed-ups of 3-12x, turning previously bad user experiences into real-time applications.