- Browsing many hosts versus renting from the owner
- When the same card comes from different hosts
- Six differences in one grid
- Hardware range and the billing clock
- Growing from one GPU to a cluster
- Who's doing the ordering: you or an agent?
- Five questions to settle first
- Our verdict for builders and teams
- Other GPU clouds on the shortlist
- Frequently asked questions
Pick Vast.ai if you want to browse a huge range of GPU types from many hosts, provision through an API and use interruptible or reserved options. Pick Lambda if you'd rather rent from one provider that runs its own NVIDIA machines, with its software stack preinstalled and a clear route to dedicated clusters. It comes down to choice versus consistency.
Here's the question that settles most of this: do you want to browse other people's machines and pick the one that suits the job, or rent from a company that owns every GPU it hands you? Get clear on that and the rest of the comparison is detail.
To save repeating names, I'll call the first one the exchange, because many hosts list hardware on it, and the second one the owner-run cloud, because it sells only its own capacity.
Browsing many hosts versus renting from the owner
The exchange works like a big shop window. You search and filter by GPU model, VRAM and availability, then deploy through a console, a CLI or an API. Its site says rates move in real time with supply and demand across more than 20,000 GPUs, and that you're billed by the second.
The owner-run cloud is a single provider. It sells on-demand instances, 1-Click Clusters, superclusters and supercomputers, all on its own hardware. Self-serve instances come with one to eight NVIDIA GPUs, and they run its preinstalled software stack, with an API and CLI for the people who'd rather script it.
Neither approach is wrong. They suit different temperaments. If you enjoy comparing options and want to match the card to the job, the exchange gives you room. If you'd rather not think about who's behind the machine, the single provider is the calmer choice.
When the same card comes from different hosts
This is the part that's easy to miss. Because the exchange pulls in hardware from many hosts and data centres, the same GPU model can come from very different setups, and reliability can vary from one host to the next. That's the trade for the breadth. The owner-run cloud controls its machines itself, so what you get is more uniform.
There's a housekeeping point too. The owner-run cloud's site announces a new address, lambda.com, so older reviews, tutorials and links may still point at its former domain. If you're reading an older guide, check you're looking at the current site before acting on it.
And one more difference that tells you a lot about each business: the exchange has a Hosting section where you can rent out your own GPUs, complete with an earnings calculator. The owner-run cloud has nothing like it, because it only sells what it runs.
Six differences in one grid
| Area | Vast.ai | Lambda |
|---|---|---|
| Model | Many hosts and data centres list GPUs; rates set by supply and demand | One provider selling its own instances, clusters and supercomputers |
| Billing style | On-Demand, Interruptible or Reserved, billed per second | Instances billed by the minute; the vendor says there are no egress charges |
| Hardware range | 68+ GPU types, from consumer cards to Hopper and Blackwell | NVIDIA HGX B200, H100, A100 and GH200 instances, with B300 and GB300 in larger systems |
| Scaling up | Clusters, Serverless and a Model Library of templates | 1-Click Clusters, Superclusters and AI factories for enterprise scale |
| Supplier side | Hosting section to rent out your own GPUs, with an earnings calculator | No equivalent; sells only its own capacity |
| Pitch | Agent-ready infrastructure where AI agents provision compute through an API | The 'Superintelligence Cloud', with its own software stack preinstalled |
Hardware range and the billing clock
If you need something unusual, the exchange's range is hard to beat on paper. It lists 68 or more GPU types, from consumer cards right up to Hopper and Blackwell. That's handy for smaller experiments where a top-end data centre card would be overkill.
The owner-run cloud keeps a tighter list: HGX B200, H100, A100 and GH200 instances, with B300 and GB300 reserved for its larger systems. It's a shorter menu, but it's all serious training and inference hardware.
On billing, the exchange charges by the second and offers On-Demand, Interruptible and Reserved options. Interruptible is worth knowing about if your jobs can pause and pick up again. The single provider bills by the minute and says it doesn't charge for egress, which matters if you move a lot of data out.
Growing from one GPU to a cluster
Both have a path beyond a single instance, but they point at different buyers. The exchange offers Clusters and Serverless products, plus a Model Library of ready templates, which suits developers who want to go from a notebook to an endpoint without much setup.
The owner-run cloud reaches further up. Its 1-Click Clusters, Superclusters and what it calls AI factories are aimed at enterprises and government buyers who want dedicated capacity. If you can see yourself needing a reserved cluster later, that route is already built in.
Who's doing the ordering: you or an agent?
The exchange now pitches itself as agent-ready infrastructure, meaning AI agents can provision compute through its API without a person clicking around. If you're building tools that spin up their own GPUs, that's a real point in its favour.
The owner-run cloud talks about being the 'Superintelligence Cloud' and leans on its preinstalled software stack, so you land on an instance that's ready to work. It's less about agents shopping for hardware and more about a dependable place to run the work.
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Five questions to settle first
- Can your jobs survive a pause? If yes, interruptible options on the exchange are worth a look.
- Does host-to-host variation worry you? If it does, the single provider's uniform hardware is the safer bet.
- Do you need an older or consumer-grade card? Only the exchange lists that range.
- Will you move lots of data out? The owner-run cloud says it doesn't charge for egress.
- Could you need a dedicated cluster within a year or so? The single provider's cluster products are built for that step.
Our verdict for builders and teams
Choose the exchange if you like shopping across hardware types, you're comfortable with host quality varying, and you want API-driven provisioning or interruptible options. Choose the owner-run cloud if you want one provider, predictable instances with its software stack ready to go, and a clear path to dedicated clusters. There's no outright winner: it's control and choice on one side, consistency on the other.
Other GPU clouds on the shortlist
If neither fits, these four are the closest rivals. If cluster-scale training is where you're heading, our guide to providers that compete with CoreWeave covers more of that end.
Runpod
Best for: Developers who want on-demand pods, serverless endpoints and multi-node clusters together; the vendor says pods run across 31 global regions.
CoreWeave
Best for: Large teams training and serving models at scale on a purpose-built AI cloud with its Forge tooling, the vendor says.
DigitalOcean GPU Droplets
Best for: Anyone already on DigitalOcean who'd like GPU virtual machines next to their databases, networking and containers.
Together AI
Best for: Running open-source models, with inference, post-training, on-demand B200 GPU clusters and a model library.
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Frequently asked questions
Is Vast.ai less reliable than Lambda?
Not across the board, but it varies more. Many hosts list hardware on the exchange, so two machines with the same GPU can behave differently. The owner-run cloud controls its own machines, so they're more uniform.
Has Lambda changed its web address?
Yes. Its site announces lambda.com as its new address, so older links and reviews may still use the former domain.
Which one has more GPU types?
The exchange lists 68 or more types, from consumer cards to Hopper and Blackwell. The single provider focuses on NVIDIA HGX B200, H100, A100 and GH200 instances.
Can I rent out my own GPUs?
On the exchange, yes, through its Hosting section with an earnings calculator. The owner-run cloud sells only its own capacity.
Which is better for a big training cluster?
The owner-run cloud has 1-Click Clusters, Superclusters and AI factories for enterprise scale. The exchange has Clusters and Serverless, which suit smaller step-ups.
