If you're looking for another TrueFoundry alternative, MLflow is worth a look. It's an open-source end-to-end MLOps platform that spans the entire machine learning project lifecycle. It supports many deep learning and traditional machine learning libraries and can run in Databricks, cloud services and local environments. MLflow is free to use and has abundant documentation and tutorials, so it's a good option if you want to improve ML workflow collaboration and efficiency.
Another option is Modelbit, an ML engineering platform to deploy custom and open-source ML models to autoscaling infrastructure. Modelbit has built-in MLOps tools for model serving and a Git integration to keep models in sync automatically. It has several pricing tiers and supports a broad range of ML models, so it's a good option for deploying and managing machine learning models.
If you're interested in large language models specifically, Humanloop has a platform for managing and optimizing LLM development. It's got a collaborative prompt management system, evaluation and monitoring tools and support for popular LLM providers. Humanloop is geared for product teams and developers trying to get LLM apps up and running and improve collaboration and efficiency.
Last, you could look at Anyscale, which offers a platform for developing, deploying and scaling AI applications. It's built on the open-source Ray framework, but Anyscale supports a broad range of AI models. It's got smart instance management and heterogeneous node control for efficient use of resources. It's got native integrations with popular IDEs and persisted storage, too. It's a good option for teams that need to deploy AI models at scale.