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Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
Developer-focused GPU cloud offering on-demand pods, serverless inference and multi-node clusters at per-second pricing for AI workloads.
GPU rental marketplace with per-second billing across thousands of GPUs, aimed at AI training, inference, and fine-tuning workloads.
Security platform that guards GenAI apps and AI agents against prompt injection, data leaks and misuse for enterprise teams.
AI-driven network detection and response platform that identifies and stops identity-based and lateral-movement cyberattacks in real time.
No public pricing
No public pricing
No public pricing
Free trial available
No public pricing
- ✦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
- ✦On-demand GPU pods across 30+ GPU types and 31 regions
- ✦Serverless GPU endpoints with sub-200ms cold starts
- ✦Zero idle cost billing for inference workloads
- ✦Multi-node clusters for distributed training
- ✦Persistent network storage for full pipelines
- ✦Real-time logs, monitoring and autoscaling from 0 to hundreds of workers
- ✦On-demand GPU cloud with per-second billing
- ✦Interruptible instances at discounted rates for batch/fault-tolerant jobs
- ✦Reserved capacity with 1, 3, or 6-month terms for steady workloads
- ✦Serverless deployment with autoscale-to-zero for inference endpoints
- ✦Dedicated multi-node clusters with InfiniBand for large-scale training
- ✦Python SDK and CLI plus REST API for programmatic provisioning
- ✦Access to 68+ GPU types across 40+ data centers
- ✦Pre-configured templates for popular open-source models
- ✦Runtime protection for AI agents and apps
- ✦Prompt-injection and jailbreak prevention
- ✦Data-leakage detection in prompts
- ✦Shadow-AI discovery across apps and browsers
- ✦Policy controls by user, app and action
- ✦AI red-teaming and adversarial testing
- ✦Real-time AI-driven threat detection beyond traditional EDR
- ✦Detection of identity-based attacks and lateral movement
- ✦360 Response for enforced containment across identity, devices, and network
- ✦Exposure management and security posture improvement tools
- ✦Managed detection and response (MXDR/MDR) services
- ✦Integrations across existing security tool ecosystems
- ✦Attack Labs research sharing threat intelligence and techniques
- →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
- →Renting GPUs for model training and fine-tuning
- →Deploying low-latency real-time inference APIs
- →Running AI agents that need to scale instantly
- →Processing compute-heavy batch or distributed workloads
- →ML engineers training or fine-tuning models on rented GPUs
- →Startups running inference at scale without owning hardware
- →Developers needing quick, low-cost access to specific GPU types
- →Teams building AI agents that autonomously provision compute
- →Securing conversational and RAG agents
- →Governing employee use of AI tools
- →Adversarial testing before deploying GenAI
- →Meeting AI compliance requirements
- →Security operations teams needing detection beyond EDR/SIEM gaps
- →Enterprises defending against identity-based and hybrid cloud attacks
- →Organizations needing managed threat detection and response services
- →Finance, healthcare, and public sector teams meeting compliance-driven security needs