Cohere vs Lambda Pricing (2026)

How do these two stack up on price? Here's what each one costs, what you get, and where the value sits.

Cohere Lambda
Starts at Custom Custom
Number of plans 3 22
Free plan
Free trial
Pricing model custom usage-based

North

Custom
  • Intuitive interface
  • Purpose-built generative models
  • Intelligent search
  • AI agents for routine tasks and complex workflows

Compass

Custom
  • Pre-built data connectors
  • Intelligent search
  • Document parsing
  • Managed index

Model Vault

Custom
  • Fully managed model deployment
  • No shared resources or multi-tenancy overhead
  • Seamless integration with Cohere North
  • Simple startup and self-serve model access
  • Fixed or Flex pricing plans available

NVIDIA B200 SXM6 (8x)

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

NVIDIA H100 SXM (8x)

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

NVIDIA A100 SXM 80GB (8x)

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

NVIDIA A100 SXM 40GB (8x)

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

NVIDIA Tesla V100 (8x)

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

NVIDIA B200 SXM6 (4x)

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

NVIDIA H100 SXM (4x)

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

NVIDIA A100 PCIe (4x)

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

NVIDIA A6000 (4x)

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

NVIDIA B200 SXM6 (2x)

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

NVIDIA H100 SXM (2x)

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

NVIDIA A100 PCIe (2x)

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

NVIDIA A6000 (2x)

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

NVIDIA B200 SXM6 (1x)

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

NVIDIA GH200 (1x)

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

NVIDIA H100 SXM (1x)

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

NVIDIA H100 PCIe (1x)

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

NVIDIA A100 SXM (1x)

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

NVIDIA A100 PCIe (1x)

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

NVIDIA A10 (1x)

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

NVIDIA A6000 (1x)

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

NVIDIA Quadro RTX 6000 (1x)

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

Cohere vs Lambda FAQ

Which one is cheaper?
One or both use custom pricing, so it depends on your specific needs.
Can I use either one for free?
Neither has a free plan. But Cohere offers a free trial.
How do they charge?
Different approach here. Cohere uses custom pricing, while Lambda goes with usage-based. That changes the math depending on your team size and usage.
Which one is a better deal?
Depends on what you need. Cohere: Cohere is squarely targeting enterprise and developer teams that can't or won't send data to OpenAI or Anthropic — data residency, security, and deployment flexibility are the pitch. They're not the cheapest option, but they're positioning as the serious infrastructure play for regulated industries and large orgs that need control. Lambda: 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.

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