Beam vs Database Mart: GPU Compute Price Comparison
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Beam and Database Mart. Updated July 2026.
Provider Overview
Strengths & Best For
Beam is a serverless GPU platform that lets developers deploy AI models and run H100, A100, and T4 compute jobs with automatic scaling and per-second pay-per-use billing — no infrastructure management required. A Python-native SDK and fast cold starts make it easy to build and ship LLM inference APIs, batch ML pipelines, and AI model serving endpoints quickly. A strong choice for Python-first teams that want serverless GPU infrastructure with predictable, usage-based pricing.
- Serverless model
- Auto-scaling
- Simple SDK
- Fast cold starts
Database Mart provides dedicated bare-metal H100, A100, and RTX GPU servers alongside cloud instances for AI, ML, and HPC workloads, with no virtualization overhead for maximum hardware performance. Monthly and on-demand billing options are available, making it suitable for both long-running training jobs and shorter inference workloads that need dedicated GPU hardware. A practical bare-metal GPU option for teams that need consistent, dedicated performance without shared-tenancy concerns.
- Dedicated hardware
- Flexible configurations
- Competitive pricing
Live GPU Pricing
Region Coverage
Popular Comparisons
Beam — specialist provider
Beam is a serverless GPU platform that lets developers deploy AI models and run H100, A100, and T4 compute jobs with automatic scaling and per-second pay-per-use billing — no infrastructure management required. A Python-native SDK and fast cold starts make it easy to build and ship LLM inference APIs, batch ML pipelines, and AI model serving endpoints quickly. A strong choice for Python-first teams that want serverless GPU infrastructure with predictable, usage-based pricing.
Database Mart — bare-metal provider
Database Mart provides dedicated bare-metal H100, A100, and RTX GPU servers alongside cloud instances for AI, ML, and HPC workloads, with no virtualization overhead for maximum hardware performance. Monthly and on-demand billing options are available, making it suitable for both long-running training jobs and shorter inference workloads that need dedicated GPU hardware. A practical bare-metal GPU option for teams that need consistent, dedicated performance without shared-tenancy concerns.
Billing model comparison
Beam uses a Per-second usage billing model with a minimum commitment of None. Database Mart uses Monthly / On-demand billing with a None minimum. Both providers offer flexible billing options — compare the live pricing table above to find the best rate for your specific GPU model and workload duration.
Which workloads each provider suits best
Beam is best suited for: Serverless AI inference, Batch processing, Python-first teams. Its key strengths are serverless model, auto-scaling, simple sdk. Database Mart is best suited for: Dedicated GPU workloads, Long-running training, HPC. Its key strengths are dedicated hardware, flexible configurations, competitive pricing. Both providers target similar workload profiles — the live pricing table above is the most reliable way to determine which offers better value for your specific GPU model and region requirements.
Support tiers and region coverage
Beam offers Standard support across 1 region (US). Database Mart offers Standard support across 1 region (US). Both providers have comparable region coverage — choose based on which specific regions overlap with your user base or data residency requirements.
Provider background: Beam vs Database Mart
Beam was founded in 2022 and is headquartered in New York, NY. Database Mart was founded in 2015 and is headquartered in United States. Database Mart has 7 years more operational history than Beam, which may matter for teams evaluating provider stability and long-term contract risk. Use the live pricing table above to compare current on-demand and spot rates for specific GPU models, and the region map to verify coverage in your target geography.