Provider Overview
Strengths & Best For
Corelink is a GPU cloud offering H100, H200, A100, and L40S instances with flexible billing, spot availability, and competitive on-demand pricing for AI training and inference workloads. The self-serve platform makes it easy to provision GPU capacity for LLM fine-tuning, distributed training, and model deployment without enterprise contracts. A straightforward on-demand GPU cloud for startups and research teams that need H100 access at competitive rates.
- Competitive H100 pricing
- Spot availability
- Flexible billing
- Wide GPU selection
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
Live GPU Pricing
Region Coverage
Popular Comparisons
Corelink — specialist provider
Corelink is a GPU cloud offering H100, H200, A100, and L40S instances with flexible billing, spot availability, and competitive on-demand pricing for AI training and inference workloads. The self-serve platform makes it easy to provision GPU capacity for LLM fine-tuning, distributed training, and model deployment without enterprise contracts. A straightforward on-demand GPU cloud for startups and research teams that need H100 access at competitive rates.
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.
Billing model comparison
Corelink uses a On-demand, Spot billing model with a minimum commitment of None. Beam uses Per-second usage 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
Corelink is best suited for: Cost-sensitive AI training, Spot-tolerant workloads, Startups needing H100 access. Its key strengths are competitive h100 pricing, spot availability, flexible billing. Beam is best suited for: Serverless AI inference, Batch processing, Python-first teams. Its key strengths are serverless model, auto-scaling, simple sdk. 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
Corelink offers Community → Standard support across 1 region (US). Beam 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: Corelink vs Beam
Corelink was founded in 2023 and is headquartered in United States. Beam was founded in 2022 and is headquartered in New York, NY. Beam has 1 years more operational history than Corelink, 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.