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Inference comparison

Baseten vs NVIDIA DGX Cloud Lepton

Managed inference API against gpu marketplace. Editorial fit scores help shortlist; live pricing stays on the provider sites.

Baseten product facts checked October 2, 2026Baseten editorial scores reviewed September 3, 2026NVIDIA DGX Cloud Lepton product facts checked October 2, 2026NVIDIA DGX Cloud Lepton editorial scores reviewed September 3, 2026

Managed inference API

Baseten

Model APIs: model-specific token/tool pricing. Dedicated deployments, image builders and training: instance minutes while the workload is running.

vs

GPU marketplace

NVIDIA DGX Cloud Lepton

Partner GPU marketplace / DGX Cloud usage

Some outbound links use a first-party redirect hop for click counting. Commission is only claimed when a partner programme is active for that specific link — most vendor hops here are not paid placements. Affiliate disclosure.

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Baseten vs NVIDIA DGX Cloud Lepton GPU inference comparison
DimensionBasetenNVIDIA DGX Cloud Lepton
CategoryManaged inference APIGPU marketplace
BillingModel APIs: model-specific token/tool pricing. Dedicated deployments, image builders and training: instance minutes while the workload is running.Partner GPU marketplace / DGX Cloud usage
GPU choiceSelect hardware per deploymentNVIDIA and partner GPU classes exposed through DGX Cloud Lepton
Cold startDedicated endpoints can scale to zero; requests may wait for a replica to load. Keep capacity warm or accept cold-start latency; verify request parking/timeout behavior for the selected endpoint.Zero replicas cannot serve requests or automatically wake on traffic under the current documentation. Scale the deployment up again, or configure a nonzero minimum; provisioning/model-load delay depends on capacity.
Model accessChoose a supported hosted Model API or deploy custom weights/Truss model to a dedicated endpoint; hosted API catalog is not an unlimited arbitrary-model gallery.Deploy models to managed endpoints (including vLLM/SGLang workflows) using selected partner GPU capacity; NVIDIA also links to build.nvidia.com serverless APIs/NIMs as separate surfaces.
Best forML teams shipping bespoke models who want managed serving without building the ops stackTeams wanting to deploy/customize models to managed endpoints over NVIDIA Cloud Partner/GPU marketplace capacity.
Watch outDedicated billing includes image-builder workloads, cold starts/model loading and idle warm replicas. Monthly budgets notify by default; optional enforcement stops Model API requests only, while dedicated and training workloads continue accruing charges.Endpoints default to one replica and scale to zero after one hour inactive. The documentation says automatic scale-up from zero is unsupported: an operator must scale the deployment up before it can serve requests again. Partner inventory, rates and SLAs vary.
GPU choice (1–10)6/10Winner: 7/10
Time-to-serving (editorial) (1–10)Winner: 7/105/10
Price clarity (1–10)5/105/10
Production ops (1–10)Winner: 9/107/10
Open-model breadth (1–10)Winner: 7/106/10

Chart

Fit scores on the decision axes

Editorial 1–10 ratings for this pair only — not live pricing or latency benchmarks.

GPU choice

Pick exact GPUs

Time-to-serving (editorial)

Warm-path fit

Price clarity

Easy to forecast

Production ops

Less DIY ops

Open-model breadth

Catalog depth

Scores are editorial planning ratings (1–10) for product shape — not published $/hour quotes or vendor SLAs. Verify current pricing on each provider site.

How to decide

Prefer Baseten when ml teams shipping bespoke models who want managed serving without building the ops stack. Prefer NVIDIA DGX Cloud Lepton when teams wanting to deploy/customize models to managed endpoints over nvidia cloud partner/gpu marketplace capacity.. If those statements both feel true, rent a GPU for control and keep a managed API for peak traffic — do not force one product to do both jobs.

Common questions

Baseten vs NVIDIA DGX Cloud Lepton

Answered from the verified figures on this page rather than general guidance.

How are Baseten and NVIDIA DGX Cloud Lepton billed?
Baseten uses model apis: model-specific token/tool pricing. dedicated deployments, image builders and training: instance minutes while the workload is running.; NVIDIA DGX Cloud Lepton uses partner gpu marketplace / dgx cloud usage. Marketplace hourly spend tracks GPU uptime; serverless and managed APIs bill for what you invoke — different failure modes if you forget to shut things down.
Which gives more control over the GPU, Baseten or NVIDIA DGX Cloud Lepton?
Baseten: Select hardware per deployment. NVIDIA DGX Cloud Lepton: NVIDIA and partner GPU classes exposed through DGX Cloud Lepton. NVIDIA DGX Cloud Lepton scores higher for GPU choice (7/10 vs 6/10). Pick a marketplace when you need a specific SKU; pick serverless or managed APIs when you want the platform to handle hardware.
Which has faster cold starts, Baseten or NVIDIA DGX Cloud Lepton?
Baseten: Dedicated endpoints can scale to zero; requests may wait for a replica to load. Keep capacity warm or accept cold-start latency; verify request parking/timeout behavior for the selected endpoint.. NVIDIA DGX Cloud Lepton: Zero replicas cannot serve requests or automatically wake on traffic under the current documentation. Scale the deployment up again, or configure a nonzero minimum; provisioning/model-load delay depends on capacity.. Our editorial ratings favour Baseten for warm API speed (7/10 vs 5/10), but real latency depends on model size, region, and whether endpoints are kept warm. Those 1–10 scores are editorial rankings, not measured milliseconds — record your own TTFT/p95 on a warm endpoint in the target region before you buy on latency.
Should I use Baseten or NVIDIA DGX Cloud Lepton?
Choose Baseten when ml teams shipping bespoke models who want managed serving without building the ops stack. Choose NVIDIA DGX Cloud Lepton when teams wanting to deploy/customize models to managed endpoints over nvidia cloud partner/gpu marketplace capacity.. Watch out: Dedicated billing includes image-builder workloads, cold starts/model loading and idle warm replicas. Monthly budgets notify by default; optional enforcement stops Model API requests only, while dedicated and training workloads continue accruing charges. Endpoints default to one replica and scale to zero after one hour inactive. The documentation says automatic scale-up from zero is unsupported: an operator must scale the deployment up before it can serve requests again. Partner inventory, rates and SLAs vary.