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Cloud DLP and CASB that classifies and protects sensitive data across SaaS apps using deep learning (now Palo Alto Networks).
Developer platform serving 200+ optimized LLMs via APIs; high traffic.
Open-source desktop app for running AI chat models locally or via APIs, as a private ChatGPT alternative.
Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦Deep-learning data classification (99.5% claimed accuracy)
- ✦Cloud DLP across SaaS applications
- ✦One-click deployment across apps, devices and users
- ✦End-user self-remediation of violations
- ✦Broad SaaS integrations
- ✦Insider-threat and breach monitoring
- ✦Access over 200 optimized models, including LLMs, image, video, and audio processing.
- ✦Achieve low-latency, high-throughput inference with SiliconFlow's self-developed acceleration frameworks.
- ✦Deploy models via serverless inference, dedicated endpoints, or reserved GPUs to suit various workloads.
- ✦Customize models to your data with built-in monitoring and elastic compute resources.
- ✦Ensure data privacy and business security with dynamic scaling and fault tolerance mechanisms.
- ✦Run open-source LLMs locally
- ✦Connect to online models (OpenAI, Claude, Gemini)
- ✦Private, offline-capable AI chat
- ✦Open source and self-hostable
- ✦Model library via Hugging Face
- ✦Cross-platform desktop app
- ✦One unified, OpenAI-compatible API for 400+ models
- ✦Automatic provider failover for higher uptime
- ✦Edge routing for low latency
- ✦Custom data and provider policies
- ✦Pay-as-you-go credits usable across any model
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- →Prevent data leaks across SaaS apps
- →Classify and monitor sensitive data
- →Reduce breaches from human error
- →Give security teams cloud data visibility
- →Quickly deploy various AI models via a simple API, supporting tasks like text, image, audio, and video processing.
- →Utilize serverless GPUs to automatically scale AI applications, ensuring flexibility and cost-efficiency.
- →Access high-performance GPUs for demanding workloads, such as large-scale inference and video generation.
- →Deploy custom models with guaranteed performance and scalability, tailored to specific business needs.
- →Private local AI chat
- →Using multiple models in one app
- →Avoiding cloud data sharing
- →Experimenting with open models
- →Accessing many LLMs through one integration
- →Adding provider redundancy to AI apps
- →Comparing model price and performance
- →Powering agents and AI-native products
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects