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DeepInfra vs StreamLake
StreamLake is Kuaishou's AI cloud built around the proprietary KAT-Coder models. DeepInfra is an open-model price floor with a far wider catalog.
By The Subconscious Team · Updated
DeepInfra vs StreamLake: key differences
StreamLake sells access to models you cannot get as open weights. Its headline, KAT-Coder-Pro V2.5, is a proprietary agentic coding model from Kuaishou's KwaiKAT team, which StreamLake says was trained with large-scale agentic reinforcement learning for repository-level work. Developers pay per token or buy a KwaiKAT Coding Plan, and a Claude-protocol proxy drops it into Claude Code or OpenClaw. DeepInfra is the opposite shape: 150+ open models from many labs, nothing proprietary, behind an OpenAI-compatible API with no minimums or contracts, and small models from $0.02 per million.
Procurement will decide it for many buyers. StreamLake's pricing and much of its documentation lead with China and yuan, and data residency in China rules it out for many US and EU enterprises. It does bring Kuaishou's hyperscale video infrastructure and bare-metal compute for Chinese internet businesses. DeepInfra is easier to adopt, with its own caveat around default quantization. Low-cost agentic coding on a subscription, or domestic Chinese MaaS, points to StreamLake. General-purpose bulk inference across many model families points to DeepInfra.
What DeepInfra and StreamLake do
DeepInfra
DeepInfra is the price floor for open-model inference. Developers treat it as the reference point for what a token should cost, with small models like Llama 3.1 8B at $0.02 per million and DeepSeek V4 Flash at $0.14 in and $0.28 out. The catalog covers 150+ open models across text, image and speech behind a fully OpenAI-compatible API. There are no minimums, setup fees or contracts on the shared API.
Example models: DeepSeek V4 Flash, Llama 3.1 8B
Full DeepInfra 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 DeepInfra or StreamLake?
DeepInfra
Choose DeepInfra for
- General bulk inference across many open models
- Western teams that want simple per-token billing
- Workloads beyond coding, such as tagging and chat
StreamLake
Choose StreamLake for
- Agentic coding on KAT-Coder through a subscription plan
- Running a Kuaishou coding model inside Claude Code
- Chinese businesses that want domestic MaaS and bare metal
DeepInfra vs StreamLake at a glance
| Attribute | DeepInfra | StreamLake |
|---|---|---|
| Model access | Open weights | Proprietary coding models |
| Flagship models | DeepSeek V4 Flash, Llama 3.1 8B | KAT-Coder-Pro V2.5, KAT-Coder-Air |
| Speed | ~33 tok/s on DeepSeek V4 Pro (FP4) | Unknown |
| Price | From $0.02 per 1M | Per token or KwaiKAT Coding Plan |
| Customization | No managed fine-tuning | Unknown |
| Deployment | Shared API, no contracts | MaaS API, bare metal |
| Long context | 66K on FP4 DeepSeek V4 Pro | Unknown |
Frequently asked questions
What is the difference between DeepInfra and StreamLake?
StreamLake is Kuaishou's AI cloud built around the proprietary KAT-Coder models. DeepInfra is an open-model price floor with a far wider catalog.
When should I choose DeepInfra over StreamLake?
General bulk inference across many open models; Western teams that want simple per-token billing; Workloads beyond coding, such as tagging and chat.
When should I choose StreamLake over DeepInfra?
Agentic coding on KAT-Coder through a subscription plan; Running a Kuaishou coding model inside Claude Code; Chinese businesses that want domestic MaaS and bare metal.
Is DeepInfra or StreamLake cheaper?
DeepInfra: From $0.02 per 1M. StreamLake: Per token or KwaiKAT Coding Plan. The cheaper choice depends on the model and workload.
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If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.