The full-stack platform for LLM applications
Get the freedom to build, deploy and monitor LLM applications with a single platform. Easily manage prompts, trace requests, deploy AI workflows and use any hosted or API-based AI models.
Manage Prompts
Manage prompts, track versions, and collaborate with your team.
- Create, manage and deploy prompts outside code
- Collaborate with your entire team - data scientists, prompts engineers and developers.
- Experiment with models, track versions, and compare results in our prompt playground.
- A single prompt registry for prompt engineering and in-depth experiments on large datasets.
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Orchestrate Workflows
Create and visualize complex LLM flows. Implement shadow prompts deployments to thoroughly test and refine them before rolling out your workflow into production.
LLM Tracing
Trace any workflow to debug and inspect LLM applications.
- See all requests in one place
- Gain visibility and debug your LLM workflow in seconds.
- Track prompt content, model inference calls and latency
All Your RAG Needs
Deploy Retrieval-Augmented Generation (RAG) pipelines using embedding models and a Vector database on JFrog ML. Seamlessly integrate and manage your RAG processes and vector storage, in an all-in-one user friendly platform.
Deploy any Large Language Model
Streamline your Large Language Model deployment with the JFrog ML model library. We make the process quick, easy, and efficient, allowing you to focus on AI while we handle the engineering.
Fine-Tune LLMs
Enhance the accuracy and relevance of your Large Language Models with JFrog ML fine-tuning capabilities. Tailor your LLMs to specific tasks and datasets, ensuring optimal performance and results that truly align with your business objectives
Connect Data Pipelines
AI applications need your organizational data right on time to make the magic happen. Connect any data source to prompts or create complex vector ingestion pipelines tailored to your needs.
Vector Store
Store embedding vectors at scale to supercharge your ML & AI applications.
- Ingest data from any source, convert to embedding vectors, and perform vector search manually or automatically.
- Easily find similarities for applications like recommendation engines and image retrieval.
- Use RAG pipelines to fetch up-to-date or context-specific data, enhancing LLM performance and accuracy in text generation tasks.
JFrog ML GPU Cloud
Achieve top model performance and save costs with effortless GPU autoscaling and simple model deployment on any of the various GPUs available on the JFrog ML GPU cloud.
Don’t just take our word for it
In the rapidly evolving landscape of property management technology, optimizing data processes remains paramount. Guesty, a leading player in this domain, faced challenges in streamlining its data science operations and hastening model deployment. This case study delves into Guesty's unique challenges and highlights how a strategic partnership with JFrog ML provided innovative solutions.
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