Resource planning that learns from every AI project
Stop overspending on GPU time and underestimating training costs for your AI projects.
Backed by the best
AI projects drain resources unpredictably
You start a model training run expecting 6 hours and $200 in compute costs, only to discover 18 hours later you've burned through $800 in GPU credits with mediocre results. Your Colab Pro subscription runs out mid-experiment. You can't predict whether that new dataset will require 16GB or 64GB of VRAM. Every project feels like financial Russian roulette, and you're constantly context-switching between training monitoring, cost tracking, and resource allocation across multiple cloud providers.
- GPU costs spiral out of control during long training runs
- Colab sessions timeout right before breakthrough experiments finish
- Can't predict VRAM requirements for new model architectures
- Switching between AWS, GCP, and RunPod based on availability
AI workspace that optimizes resources across every project
- Predict training costs and resource needs before starting experiments using historical project data
- Monitor GPU utilization and costs in real-time across all cloud providers from one dashboard
- Automatically pause expensive runs and migrate to cheaper alternatives when thresholds are hit
- Build reusable resource templates from successful experiments for faster future project planning
How it works
- 01
Connect providers
Link your AWS, GCP, and GPU cloud accounts through guided authentication setup.
- 02
Track experiments
Log training runs, costs, and outcomes. AI learns your resource patterns over time.
- 03
Optimize automatically
Deploy cost controls and resource optimization scripts directly from chat conversations.
What Brainvolt brings to your workflow
Compounding Resource Intelligence
Every training run teaches the AI about your resource patterns. Failed experiments become cost prediction models. Successful architectures become reusable templates with known resource requirements.
Multi-Cloud Cost Monitoring
Connect AWS, Google Cloud, RunPod, and Lambda Labs in one dashboard. Real-time spend tracking with automatic alerts when costs exceed budgets or training stalls.
Automated Resource Optimization
Scripts that pause expensive runs, migrate workloads to cheaper instances, and queue experiments for off-peak pricing. Conversation to deployed cost controls.
Scheduled Resource Tasks
Automated daily cost reports, weekly GPU utilization summaries, and monthly budget reconciliation. Set up once, run forever with customizable thresholds and notifications.
Frequently asked questions
Connect your world.
Get things done.
Try the AI workspace that keeps context and helps you turn conversation into action.