Lambda alternative
Pacific vs Lambda: procurement-clear reserved clusters
Lambda is a GPU cloud and workstation vendor many ML teams already know. The buy is usually a cloud cluster or reserved instances in Lambda regions. Pacific is a reserved-module buy: 32-node HGX B300 building blocks, fabric chosen with engineering, and a deployment plan you can take to procurement.
Lambda fits teams that want a familiar GPU cloud invoice. Pacific fits buyers who need a reserved physical cluster, acceptance evidence, and a site they can point at.
Decision criteria
Pacific and Lambda, side by side
Honest differences. No invented competitor pricing. Commercial terms for Pacific are always private.
| Criterion | Pacific | Lambda |
|---|---|---|
| What you receive | A reserved Pod 32-class module — 32 HGX B300 nodes, single tenant. | Cloud GPU instances and, separately, on-prem workstations / small clusters. |
| Procurement shape | One reserved-capacity conversation with a site-specific plan and evidence pack. | Cloud reservations plus hardware SKUs; clarity varies by product line. |
| Fabric | Rail-optimized 400G Ethernet; 3.2 or 6.4 Tbps per node, chosen during the plan. | Cloud networking inside Lambda regions; on-prem configs are SKU-dependent. |
| Evidence | Witnessed 24-stage factory, site, and integration tests, including NCCL pass bars. | Cloud SLAs and hardware datasheets — different artifact than site acceptance. |
| On-prem / CUI | Managed on-prem path for CUI scope reduction. | Primarily a cloud and workstation vendor; not a CMMC-scope pod offer. |
| Pricing | Private, on a qualified call. No public rate card. | Lambda publishes some cloud rates; we do not reprint them. |
When to buy which
Honest split
When Pacific wins
- Procurement needs a reserved cluster they can diligence as a physical module, not a cloud SKU.
- You want fabric configuration and acceptance evidence in the same conversation as capacity.
- The cluster may need to land on your pad or a CUI-bounded site later.
When Lambda wins
- Researchers and startups already on Lambda who just need more of that cloud.
- You want workstations or small on-prem boxes, not a 32-node reserved module.
- Time-to-first-GPU in a public cloud region beats a reserved deployment plan.
Next step
Talk through the reserved plan
Money pages first. Book a 30-minute call when you want a site-specific answer.
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