Beam

Beam GPU pricing overview

Beam is a specialist GPU cloud provider headquartered in New York, NY. 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 (`beam deploy`) and fast cold starts make it easy to build and ship LLM inference APIs, batch ML pipelines, and AI model serving endpoints quickly. Beam handles container builds, GPU provisioning, and scaling automatically. Free tier available. A strong choice for Python-first teams that want serverless GPU infrastructure with predictable, usage-based pricing. Billing is Per-second usage with a minimum commitment of None. Available regions include US. GPU instances can be provisioned on-demand, making Beam suitable for both short-duration experiments and sustained production workloads.

Beam vs other GPU providers

Beam competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: Python-native SDK — `beam deploy` ships a GPU endpoint in minutes; Auto-scaling with fast cold starts — no idle cost; Per-second billing — pay only for actual compute time. Use the side-by-side comparison tool above to see Beam pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 4 Beam listings alongside 91+ providers in a single sortable view.

Best use cases for Beam

Beam is best suited for: Serverless LLM inference APIs, Batch ML processing pipelines, Python-first teams wanting zero-ops GPU infrastructure, Rapid prototyping with free tier. Support tiers range from Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 4 active GPU listings on Beam, covering H100 80GB, A100 80GB, A10G, T4. H100 SXM5 instances with NVLink interconnect are commonly used for large-scale training workloads. A100 40GB and RTX 4090 instances are frequently used for fine-tuning and inference of smaller models.

Beam billing model and cost structure

Beam uses Per-second usage pricing. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing may be available for predictable long-running workloads — check the provider's pricing page for current committed-use rates. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on Beam

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — high HBM3 bandwidth and FP16 throughput make them well-suited for large transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB is a common choice on many providers. RTX 4090 instances are widely used for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How Beam pricing data is collected

Prices shown are sourced from Beam's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are kept in our history archive and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and many other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.