How much does Lambda cost?

Pricing is custom — you'll need to talk to their sales team for a quote.

Is there a free plan?

No free plan. You'll need to commit to a paid plan to get started.

Can I try it before paying?

Not at the moment. There's no free trial or free plan listed on their pricing page.

Lambda Pricing (2026)

Pure usage-based, billed per GPU per hour down to the minute — so you're paying for raw compute time, not seats or features. The value metric is GPU access itself, spanning everything from a single A10 to 8-node B200 clusters.

22 plans usage-based

NVIDIA B200 SXM6 (8x): Contact Sales
NVIDIA H100 SXM (8x): Contact Sales
NVIDIA A100 SXM 80GB (8x): Contact Sales
NVIDIA A100 SXM 40GB (8x): Contact Sales
NVIDIA Tesla V100 (8x): Contact Sales
NVIDIA B200 SXM6 (4x): Contact Sales
NVIDIA H100 SXM (4x): Contact Sales
NVIDIA A100 PCIe (4x): Contact Sales
NVIDIA A6000 (4x): Contact Sales
NVIDIA B200 SXM6 (2x): Contact Sales
NVIDIA H100 SXM (2x): Contact Sales
NVIDIA A100 PCIe (2x): Contact Sales
NVIDIA A6000 (2x): Contact Sales
NVIDIA B200 SXM6 (1x): Contact Sales
NVIDIA GH200 (1x): Contact Sales
NVIDIA H100 SXM (1x): Contact Sales
NVIDIA H100 PCIe (1x): Contact Sales
NVIDIA A100 SXM (1x): Contact Sales
NVIDIA A100 PCIe (1x): Contact Sales
NVIDIA A10 (1x): Contact Sales
NVIDIA A6000 (1x): Contact Sales
NVIDIA Quadro RTX 6000 (1x): Contact Sales

Verified Jul 26, 2026 Official pricing page

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NVIDIA B200 SXM6 (8x)

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VRAM/GPU: 180 GB
vCPUs: 208
RAM: 2900 GiB
Storage: 22 TiB SSD

NVIDIA H100 SXM (8x)

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VRAM/GPU: 80 GB
vCPUs: 208
RAM: 1800 GiB
Storage: 22 TiB SSD

NVIDIA A100 SXM 80GB (8x)

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VRAM/GPU: 80 GB
vCPUs: 240
RAM: 1800 GiB
Storage: 19.5 TiB SSD

NVIDIA A100 SXM 40GB (8x)

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VRAM/GPU: 40 GB
vCPUs: 124
RAM: 1800 GiB
Storage: 5.8 TiB SSD

NVIDIA Tesla V100 (8x)

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VRAM/GPU: 16 GB
vCPUs: 88
RAM: 448 GiB
Storage: 5.8 TiB SSD

NVIDIA B200 SXM6 (4x)

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VRAM/GPU: 180 GB
vCPUs: 104
RAM: 1440 GiB
Storage: 11 TiB SSD

NVIDIA H100 SXM (4x)

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VRAM/GPU: 80 GB
vCPUs: 104
RAM: 900 GiB
Storage: 11 TiB SSD

NVIDIA A100 PCIe (4x)

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VRAM/GPU: 40 GB
vCPUs: 120
RAM: 900 GiB
Storage: 1 TiB SSD

NVIDIA A6000 (4x)

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VRAM/GPU: 48 GB
vCPUs: 56
RAM: 400 GiB
Storage: 1 TiB SSD

NVIDIA B200 SXM6 (2x)

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VRAM/GPU: 180 GB
vCPUs: 52
RAM: 720 GiB
Storage: 5.5 TiB SSD

NVIDIA H100 SXM (2x)

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VRAM/GPU: 80 GB
vCPUs: 52
RAM: 450 GiB
Storage: 5.5 TiB SSD

NVIDIA A100 PCIe (2x)

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VRAM/GPU: 40 GB
vCPUs: 60
RAM: 450 GiB
Storage: 1 TiB SSD

NVIDIA A6000 (2x)

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VRAM/GPU: 48 GB
vCPUs: 28
RAM: 200 GiB
Storage: 1 TiB SSD

NVIDIA B200 SXM6 (1x)

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VRAM/GPU: 180 GB
vCPUs: 26
RAM: 360 GiB
Storage: 2.75 TiB SSD

NVIDIA GH200 (1x)

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VRAM/GPU: 96 GB
vCPUs: 64
RAM: 432 GiB
Storage: 4 TiB SSD

NVIDIA H100 SXM (1x)

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VRAM/GPU: 80 GB
vCPUs: 26
RAM: 225 GiB
Storage: 2.75 TiB SSD

NVIDIA H100 PCIe (1x)

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VRAM/GPU: 80 GB
vCPUs: 26
RAM: 225 GiB
Storage: 1 TiB SSD

NVIDIA A100 SXM (1x)

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VRAM/GPU: 40 GB
vCPUs: 30
RAM: 220 GiB
Storage: 512 GiB SSD

NVIDIA A100 PCIe (1x)

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VRAM/GPU: 40 GB
vCPUs: 30
RAM: 225 GiB
Storage: 512 GiB SSD

NVIDIA A10 (1x)

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VRAM/GPU: 24 GB
vCPUs: 30
RAM: 226 GiB
Storage: 1.3 TiB SSD

NVIDIA A6000 (1x)

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VRAM/GPU: 48 GB
vCPUs: 14
RAM: 100 GiB
Storage: 512 GiB SSD

NVIDIA Quadro RTX 6000 (1x)

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VRAM/GPU: 24 GB
vCPUs: 14
RAM: 46 GiB
Storage: 512 GiB SSD

AI Pricing Analysis

Pricing Model

Tier Strategy

There are no traditional tiers here — the 'upgrade path' is just more GPUs or newer silicon. A solo researcher starts with a single A6000 or H100 PCIe; a team running large training runs naturally migrates toward 8x H100 or B200 SXM configs, and at that scale you're pushed into custom pricing conversations.

Competitive Positioning

Lambda positions as a serious alternative to hyperscalers (AWS, GCP, Azure) for ML teams who want raw GPU access without cloud markup or complexity. They're not the cheapest bare-metal option, but the no-egress-fees policy and 1-Click Clusters are a direct shot at teams burned by AWS networking costs.

Growth Lever

Everything above single-GPU configs and anything involving B200 or large-scale cluster access requires talking to sales — that's the paywall. Expansion is driven entirely by compute demand: more experiments, bigger models, and faster iteration cycles naturally push teams toward higher GPU counts and longer reservations, which is exactly where Lambda's enterprise capacity deals live.

Lambda Pricing FAQ

How much does Lambda cost?: Pricing is custom — you'll need to talk to their sales team for a quote.
Is there a free plan?: No free plan. You'll need to commit to a paid plan to get started.
Can I try it before paying?: Not at the moment. There's no free trial or free plan listed on their pricing page.
How does the pricing work?: Pure usage-based, billed per GPU per hour down to the minute — so you're paying for raw compute time, not seats or features. The value metric is GPU access itself, spanning everything from a single A10 to 8-node B200 clusters.
Which plan makes sense for me?: There are no traditional tiers here — the 'upgrade path' is just more GPUs or newer silicon. A solo researcher starts with a single A6000 or H100 PCIe; a team running large training runs naturally migrates toward 8x H100 or B200 SXM configs, and at that scale you're pushed into custom pricing conversations.

More AI pricing

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