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SambaNova vs StreamLake
SambaNova serves open models fast on its own silicon. StreamLake is Kuaishou's cloud for the proprietary KAT-Coder models, with a Claude Code proxy. Speed on open weights versus a closed coding model.
By The Subconscious Team · Updated
SambaNova vs StreamLake: key differences
Both target coding agents, from different starting points. StreamLake is the AI cloud of Kuaishou and leads with KAT-Coder-Pro V2.5, a proprietary model it says was trained with large-scale agentic RL for repository-level work. It sells per token or through a KwaiKAT Coding Plan, with OpenAI-protocol endpoints and a Claude-protocol proxy for Claude Code. SambaNova serves open models, including MiniMax M2.7, DeepSeek and GPT-OSS 120B, and sells decode speed from its own RDU chip. One sells a model, the other sells the speed at which open models run.
Procurement and model choice decide most cases. StreamLake's data residency is in China and its pricing leads with yuan, which rules it out for many US and EU enterprises but suits Chinese businesses that want domestic MaaS and bare metal. SambaNova gives open-weight options and a hardware path for neoclouds. For a developer wanting a cheap subscription coding model, StreamLake's plan is the more direct offer. For an interactive agent on large open weights, SambaNova is the fit.
What SambaNova and StreamLake do
SambaNova
SambaNova designs its own inference chip, the Reconfigurable Dataflow Unit, and sells fast tokens on large open models through SambaCloud. The RDU maps the model graph onto the chip to cut trips to off-chip memory. A three-tier memory design of SRAM, HBM and bulk DRAM lets one system host very large models and hot swap between several of them in milliseconds. SambaCloud serves models like MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B, with speeds reported by Artificial Analysis.
Example models: MiniMax M2.7, GPT-OSS 120B
Full SambaNova profileStreamLake
StreamLake is the AI cloud brand of Kuaishou, the Chinese short-video company behind the Kling video models. It sells model-as-a-service inference and bare-metal compute to internet businesses, drawing on the infrastructure Kuaishou built to serve video at massive scale. Its developer site offers APIs, SDKs and integration guides aimed at taking teams from testing to production.
Example models: KAT-Coder-Pro V2.5, KAT-Coder-Air
Full StreamLake profileShould you choose SambaNova or StreamLake?
SambaNova
Choose SambaNova for
- Fast decode on large open-weight coding models.
- Agents that switch between models in milliseconds.
- Neoclouds buying racks for a speed tier.
StreamLake
Choose StreamLake for
- A subscription plan for agentic coding.
- Running KAT-Coder inside Claude Code.
- Chinese businesses needing domestic MaaS and bare metal.
SambaNova vs StreamLake at a glance
| Attribute | SambaNova | StreamLake |
|---|---|---|
| Model access | Open weights | Proprietary coding models |
| Flagship models | MiniMax M2.7, GPT-OSS 120B, DeepSeek | KAT-Coder-Pro V2.5, KAT-Coder-Air |
| Speed | ~820 tok/s on MiniMax M2.7 (SN50) | Unknown |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | Per token or KwaiKAT Coding Plan |
| Customization | Unknown | Unknown |
| Deployment | SambaCloud, racks for neoclouds | MaaS API, bare metal |
| Long context | Up to 192K (MiniMax M2.7) | Unknown |
Frequently asked questions
What is the difference between SambaNova and StreamLake?
SambaNova serves open models fast on its own silicon. StreamLake is Kuaishou's cloud for the proprietary KAT-Coder models, with a Claude Code proxy. Speed on open weights versus a closed coding model.
When should I choose SambaNova over StreamLake?
Fast decode on large open-weight coding models; Agents that switch between models in milliseconds; Neoclouds buying racks for a speed tier.
When should I choose StreamLake over SambaNova?
A subscription plan for agentic coding; Running KAT-Coder inside Claude Code; Chinese businesses needing domestic MaaS and bare metal.
Is SambaNova or StreamLake cheaper?
SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). StreamLake: Per token or KwaiKAT Coding Plan. The cheaper choice depends on the model and workload.
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