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Privacy-focused CAPTCHA and bot/fraud-detection service, a drop-in reCAPTCHA alternative for websites and apps.
Fast, low-cost AI inference provider running LLMs on custom LPU chips via GroqCloud's pay-as-you-go API.
Free crowdsourced benchmark that pits top AI models head-to-head on design tasks and ranks them by public votes.
Low-cost inference cloud with developer APIs to run open ML models and on-demand GPUs, billed pay-per-use.
Serverless AI cloud for running inference, training and sandboxes on GPUs with fast cold starts and pay-per-use billing.
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No public pricing
- ✦AI bot detection
- ✦Transaction fraud protection
- ✦Account-takeover (ATO) defense
- ✦Pull-based SMS MFA
- ✦Private Learning ML risk models
- ✦Two-line reCAPTCHA migration
- ✦Hundreds of integrations
- ✦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
- ✦Side-by-side model output comparison
- ✦Public voting on results
- ✦Leaderboards ranking AI models by 'taste'
- ✦Coverage of websites, games, 3D, UI, images, logos, SVG, video and slides
- ✦Hosted inference for many open models
- ✦Simple REST/OpenAI-compatible API
- ✦Pay-per-token or per-time billing
- ✦On-demand GPU rental
- ✦Broad catalog (Llama, DeepSeek, Qwen, Flux, etc.)
- ✦DeepStart and DeepCluster tooling
- ✦Serverless GPU compute defined in Python
- ✦Sub-second container cold starts
- ✦Autoscale 0 to 1000+ GPUs
- ✦Inference, training and batch workloads
- ✦Secure sandboxes for untrusted code
- ✦Built-in logging and observability
- →Blocking bots and spam signups
- →Preventing account takeover
- →Reducing transaction and payment fraud
- →Stopping credential stuffing
- →Running LLM inference at high speed
- →Cutting inference costs at scale
- →Powering low-latency AI chat apps
- →Serving models via a hosted API
- →Compare which AI model produces the best design output
- →Track AI design model rankings
- →Discover models for a specific creative task
- →Serving open-source models via API
- →Building AI apps cost-efficiently
- →Renting GPUs for inference or training
- →Scaling inference up and down on demand
- →Deploying and scaling model inference
- →Fine-tuning and training models
- →Running batch/parallel AI jobs
- →Executing untrusted code in sandboxes