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Pricing
Essential: $29 per seat/mo (billed annually) + $0.99 per Fin resolution
Advanced: $85 per seat/mo (billed annually) + $0.99 per Fin resolution
Expert: $132 per seat/mo (billed annually) + $0.99 per Fin resolution
Fin AI Agent (on existing helpdesk): $0.99 per Fin resolution (50 resolutions per month minimum)
Copilot: $29 per agent/mo (billed annually)
Proactive Support Plus: $99 /mo
NVIDIA H200 GPU: $2.30
NVIDIA H100 GPU: $2.00
NVIDIA Blackwell Platforms: Pre-order
NVIDIA H200 GPU (On-demand): $3.50
NVIDIA H100 GPU (On-demand): $2.95
NVIDIA L40S GPU with AMD (On-demand): from $1.82
NVIDIA L40S GPU with Intel (On-demand): from $1.55
No public pricing
Core features
- ✦Fin AI Agent for automated customer support
- ✦Omnichannel support (inbox, tickets, phone, help center)
- ✦AI-enhanced inbox for agent productivity
- ✦AI Insights & Reporting for performance optimization
- ✦Workflow automation with a visual builder
- ✦Flexible architecture for scaling AI workloads
- ✦Pre-configured NVIDIA GPU accelerators with high-performance InfiniBand
- ✦AI Studio for fine-tuning AI models
- ✦Managed Kubernetes and Slurm-based clusters
- ✦Competitive pricing for NVIDIA GPUs
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
Use cases
- →Providing instant customer service with Fin AI Agent
- →Managing customer inquiries across multiple channels
- →Automating support workflows to improve efficiency
- →Empowering support agents with AI-powered tools
- →Gaining insights into customer service performance
- →Fine-tuning open source models into specialized AI solutions
- →Revolutionizing drug discovery, biotechnology, genomics, and healthtech
- →Orchestrating and scaling AI environments using Managed Kubernetes or Slurm
- →Deploying MLflow, PostgreSQL, and Apache Spark with zero maintenance effort
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
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