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Enterprise AI platform for redacting, anonymizing, and governing sensitive data across documents and AI workflows.
Developer-first security platform unifying code, cloud, runtime and AI pentesting with noise reduction and autofix.
Developer platform for fast serverless inference and training of open generative models, billed per token or GPU-second.
Fast, low-cost AI inference provider running LLMs on custom LPU chips via GroqCloud's pay-as-you-go API.
Cloud platform from the makers of PyTorch Lightning for building, training and deploying AI in browser-based GPU Studios.
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- ✦AI-powered document redaction
- ✦Real-time data anonymization
- ✦AI guardrails for generative-AI apps
- ✦Automated compliance enforcement
- ✦Industry-specific solutions for government, legal, and healthcare
- ✦SAST, SCA and secrets scanning
- ✦Cloud misconfiguration (CSPM) and container scanning
- ✦AI-powered autonomous pentesting
- ✦AutoFix pull requests and auto-triage
- ✦Runtime and bot protection (Zen)
- ✦SOC 2 and ISO compliance support
- ✦Serverless per-token inference with OpenAI/Anthropic-compatible APIs
- ✦On-demand dedicated and reserved GPU deployments
- ✦Fine-tuning and reinforcement-learning training pipelines
- ✦Large library of open LLM, vision, image and audio models
- ✦Optimized inference engine for throughput and latency
- ✦LPU custom inference hardware
- ✦GroqCloud tokens-as-a-service API
- ✦High-speed, low-latency inference
- ✦Pay-as-you-go token pricing
- ✦Free API key to start
- ✦Broad open-model support
- ✦Browser-based Lightning Studios with on-demand GPUs
- ✦PyTorch Lightning training framework
- ✦Model training, fine-tuning and deployment
- ✦Collaborative, shareable ML environments
- ✦Scalable multi-GPU/multi-node compute
- →Automating FOIA and public-records redaction
- →Protecting privileged data in eDiscovery
- →Preventing data leakage to AI systems
- →Finding and fixing code vulnerabilities
- →Securing cloud and containers
- →Running continuous pentests
- →Automating compliance evidence
- →Serving open models in production apps and agents
- →Fine-tuning models on private data
- →Powering code assistants, chatbots and RAG at scale
- →Running LLM inference at high speed
- →Cutting inference costs at scale
- →Powering low-latency AI chat apps
- →Serving models via a hosted API
- →Prototype and train ML models in the cloud
- →Fine-tune and deploy foundation models
- →Run reproducible AI experiments collaboratively