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Together AI vs StreamLake
StreamLake is Kuaishou's AI cloud, selling proprietary KAT-Coder models mainly to Chinese businesses. Together hosts open weights for a global developer base.
By The Subconscious Team · Updated
Together AI vs StreamLake: key differences
StreamLake's headline is KAT-Coder-Pro V2.5, a proprietary agentic coding model that StreamLake says was trained with large-scale reinforcement learning for repository-level work. Developers pay per token or buy a KwaiKAT Coding Plan, and a Claude-protocol proxy drops the model into Claude Code or OpenClaw. It also sells bare-metal compute backed by Kuaishou's video infrastructure. Together's coding options are open weights like Kimi K3, DeepSeek V4 and GLM 5.2, which you can fine-tune and move, along with a much broader catalog.
Procurement is the practical divide. StreamLake leads with China and yuan in pricing and documentation, and its data residency in China rules it out for many US and EU buyers. Together offers dedicated deployments, provisioned throughput with a 99% SLA and managed training. StreamLake fits Chinese internet businesses wanting domestic model services and bare metal, or developers who specifically want KAT-Coder through a subscription. Teams that want to own and fine-tune their coding model fit Together.
What Together AI and StreamLake do
Together AI
Together AI is the broadest open-model platform in the category. One bill covers per-token serverless inference, batch at up to 50% off, provisioned throughput with a 99% SLA, dedicated deployments, raw GPU clusters, managed fine-tuning and code sandboxes for agents. The text catalog runs past thirty open models, including DeepSeek V4, Kimi K3, GLM 5.2, Qwen 3.8 and MiniMax M3, plus image, video, speech and embedding models. Token prices sit at parity with Fireworks and Baseten.
Example models: Kimi K3, DeepSeek V4 Pro
Full Together AI 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 Together AI or StreamLake?
Together AI
Choose Together AI for
- Fine-tuning an open coding model on internal repos
- Western procurement with published USD pricing
- Mixing coding and general models on one key
StreamLake
Choose StreamLake for
- KAT-Coder-Pro V2.5 inside Claude Code via a proxy
- Subscription-priced agentic coding
- Domestic model services and bare metal in China
Together AI vs StreamLake at a glance
| Attribute | Together AI | StreamLake |
|---|---|---|
| Model access | Open weights | Proprietary coding models |
| Flagship models | Kimi K3, DeepSeek V4, GLM 5.2, Qwen 3.8 | KAT-Coder-Pro V2.5, KAT-Coder-Air |
| Speed | 0.99s TTFT on DeepSeek V4 Pro | Unknown |
| Price | Parity with Fireworks and Baseten | Per token or KwaiKAT Coding Plan |
| Customization | LoRA and full SFT; RL in beta | Unknown |
| Deployment | Serverless, dedicated, GPU clusters | MaaS API, bare metal |
| Long context | 512K on DeepSeek V4 Pro | Unknown |
Frequently asked questions
What is the difference between Together AI and StreamLake?
StreamLake is Kuaishou's AI cloud, selling proprietary KAT-Coder models mainly to Chinese businesses. Together hosts open weights for a global developer base.
When should I choose Together AI over StreamLake?
Fine-tuning an open coding model on internal repos; Western procurement with published USD pricing; Mixing coding and general models on one key.
When should I choose StreamLake over Together AI?
KAT-Coder-Pro V2.5 inside Claude Code via a proxy; Subscription-priced agentic coding; Domestic model services and bare metal in China.
Is Together AI or StreamLake cheaper?
Together AI: Parity with Fireworks and Baseten. StreamLake: Per token or KwaiKAT Coding Plan. The cheaper choice depends on the model and workload.
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Running long-horizon agents?
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.