Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Banana provides GPU inference hosting for AI teams. The platform scales GPUs up and down automatically and includes monitoring, business analytics, a CLI, SDKs, and an open automation API. Backends can be written with Potassium, an open-source HTTP framework. Paid Team and Enterprise tiers are offered.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Dominodatalab supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Helicone AI supports deploying, serving, monitoring, or operating AI and machine-learning systems.
MLflow is an open source AI engineering platform for agents, LLMs, and ML models. It provides tools for debugging, evaluating, monitoring, and optimizing AI applications, including tracing, evaluation metrics, prompt management, an AI gateway, and an agent server. The project is released under the Apache 2.0 license.
MLOps Now supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Nimblebox supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
OpenLIT is an open-source agent harness engineering platform built on OpenTelemetry. It provides tracing, evaluations, guardrails, prompt and context management, and cost and GPU monitoring for the harness around AI agents and coding agents. It is free to self-host under Apache 2.0 and is described as working with many models, frameworks and harnesses.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
The open source solution for monitoring your AI models in production.
Tecton supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Wallaroo.AI supports deploying, serving, monitoring, or operating AI and machine-learning systems.
Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
BentoML is a unified model serving framework. It enables users to build model inference APIs, job queues, LLM applications, and multi-model pipelines. The framework supports local execution and deployment via Docker.
Eurybia is an open-source tool hosted on GitHub under the MAIF organization. It is designed to monitor model drift over time and secure model deployment processes through data validation functionality, with public documentation covering overview, quickstart, installation, features, and project background.
GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment. It decouples deployment from framework-specific model implementations, allowing models to be served in a consistent manner across different environments.
Self-hosted control plane that sits between connected AI clients, model providers, and tools. It applies access rules to users and agents, routes supported model requests through a gateway, checks governed tool calls before execution, and records policy decisions, execution records, and usage in the operator's environment. It deploys a Rust runtime with PostgreSQL on infrastructure the operator controls.
TrackingAI compares AI model results on two IQ tests, Mensa Norway and an Offline Test whose questions are not published. It presents charts of AI IQ scores, model rankings, answer speed, cost, question-by-question accuracy and changes over time.
Package, configure, and iterate on a model with Truss at whatever level of control your model needs.
A Hugging Face Space that classifies whether given text was written by a human or a language model. Users enter text in an input box and receive a classification result. The Space is shown as sleeping due to inactivity.