Software · head to head
CoreWeave vs Lambda Labs
The short version
- Each has a real cost: CoreWeave gPU nodes are sold as full 8 GPU instances rather than single cards, so the entry cost for an H100 node is $49.24 an hour on demand; Lambda Labs on demand capacity is first come access rather than guaranteed, so an instance type can be unavailable when needed
- They diverge on capability: CoreWeave covers NVIDIA H100/A100, Lambda Labs covers NVIDIA GPUs.
Where they differ
Only the attributes on which CoreWeave and Lambda Labs actually diverge.
| Attribute | CoreWeave | Lambda Labs |
|---|---|---|
| Starting price | $0.35/per-hour | $1.1/per-hour |
| Founded | 2017 | 2012 |
Identical on both: pricing model (usage-based), free tier (No), platforms (Cloud), user rating (Not yet rated), category (Unknown).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in CoreWeave
- NVIDIA H100/A100
- Kubernetes native
- High bandwidth
- Object storage
- Kubernetes
- Terraform
- Cloud APIs
Only in Lambda Labs
- NVIDIA GPUs
- Pre-installed frameworks
- Persistent storage
- SSH access
- JupyterLab
- VSCode
- SSH
Both cover
- Cloud support
What people use each for
The jobs each tool is most often brought in to do.
CoreWeave
- Renting GPU compute for model training and inferencenot Lambda Labs
- Running large scale AI workloads without buying hardwarenot Lambda Labs
Lambda Labs
- Renting GPU instances for model training and inferencenot CoreWeave
- Short term access to high memory accelerators without buying hardwarenot CoreWeave
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
CoreWeave
- GPU nodes are sold as full 8 GPU instances rather than single cards, so the entry cost for an H100 node is $49.24 an hour on demand
- Spot pricing is roughly 40% of on demand, at $19.71 an hour for the same H100 node, so predictable capacity carries a large premium
- The newest hardware carries no published price and requires contacting sales
- Discounts of up to 60% require committed usage agreements negotiated with sales
- Only the GH200 is offered as a single GPU instance
Lambda Labs
- On demand capacity is first come access rather than guaranteed, so an instance type can be unavailable when needed
- H100 pricing varies within a band, at $3.99 to $4.29 an hour per GPU, so the rate is not fixed
- Reserved capacity is arranged by contacting the team rather than self serve
- Prices are quoted before applicable tax
Pricing, plan by plan
CoreWeave
$0.35/per-hour- Standard$0.35/per-hour
- Various GPU types
- Kubernetes
- EnterpriseFree
- Dedicated clusters
- Custom solutions
Lambda Labs
$1.1/per-hour- On-Demand$1.1/per-hour
- A10 GPU
- Instant availability
- ReservedFree
- Volume discounts
- Guaranteed capacity
Which should you pick?
Choose CoreWeave if
- You need nvidia h100/a100.
- You work on Cloud.
- You also want kubernetes native.
Choose Lambda Labs if
- You need nvidia gpus.
- You work on Cloud.
- You also want pre-installed frameworks.
Questions people ask
- Is CoreWeave or Lambda Labs better?
- Neither clearly leads. CoreWeave starts at $0.35/per-hour and Lambda Labs at $1.1/per-hour, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, CoreWeave or Lambda Labs?
- CoreWeave starts at $0.35/per-hour and Lambda Labs at $1.1/per-hour.
- Does CoreWeave or Lambda Labs run on more platforms?
- Both run on Cloud, so platform support will not decide this one for you.
- What is CoreWeave best used for?
- CoreWeave is most often used for renting gpu compute for model training and inference, running large scale ai workloads without buying hardware. Of those, renting gpu compute for model training and inference and running large scale ai workloads without buying hardware are not what Lambda Labs is typically brought in for.
- What can CoreWeave do that Lambda Labs cannot?
- CoreWeave covers NVIDIA H100/A100, Kubernetes native, High bandwidth, Object storage. Lambda Labs covers NVIDIA GPUs, Pre-installed frameworks, Persistent storage, SSH access. Both handle Cloud support.