Build with Llama 3.3 - deployed in your private enviornment
SHORTER DEVELOPMENT CYCLES

Build faster, deploy with confidence.

Save 2 months per deployment whilst spending less time on development and maintenance with battle-tested, best-in-class, enterprise-grade infrastructure.

Best-in-class infrastructure
Best-in-class infrastructure, out of the box

Save approximately 2 months per deployment as TitanML provides a ready-to-use enterprise inference stack.

Instantly access industry-leading infrastructure with our ready-to-use license. Move swiftly to the next stage of your development without delay. 

Application building blocks
Application building blocks for rapid development
  • Choose from our suite of ready-to-go application building blocks, including:
  • Chat UI
  • Playground UI
  • RAG UI and RAG Engine, with integrations to leading vector databases (e.g. Weaviate), frameworks (e.g. Langchain), and embedding models
  • Prompt evaluation—rapidly compare different prompts to optimize your application
  • Model arena—quickly compare different models to find the one that is best for your use case
Wide Support
Support for all new major models and hardware
  • Our Enterprise Inference Stack supports all major open-source architectures, including Llama and Falcon.
  • We continuously update existing models and adds new ones, ensuring you never have to wait to work with best-in-class technology. 
  • TitanML ensures compatibility with all popular compute providers; with support for NVIDIA, AMD, and Intel, you can choose the ideal hardware for your applications without constraints. 
FAQ

FAQs

01
How frequently does TitanML update its platform to include new models and architectures?

New model support is typically added twice a month with every release. TitanML's research team continuously monitors and evaluates the research landscape, anticipating new trends in model architectures. This work ensures that the latest models are supported within our Enterprise Inference Stack so businesses are able to move to the next stage of development without delay. 

02
How does TitanML ensure compatibility with different hardware and compute providers?

The Enterprise Inference Stack utilises the programming language Triton which is compatible with Nvidia, Intel, and AMD GPUs - meaning unlike alternative solutions TitanML is able to support non-Nvidia hardware.  As new hardware is released, TitanML works to ensure that Titan Takeoff supports this hardware.

03
Can TitanML support the integration of my existing vector databases and embedding models?

Yes. Our Enterprise Inference Stack is used extensively to build Retrieval Augmented Generation (RAG) applications and is integrated with all popular vector databases and supports all popular embedding models.