DeepSeek
Open-weight DeepSeek reasoning models, wired into growth systems that ship
DeepSeek is a family of open-weight large language models from the Chinese AI lab of the same name, spanning the general-purpose DeepSeek-V3 chat model and the DeepSeek-R1 reasoning line trained to think step by step before answering. The models ship under permissive licenses with an OpenAI-compatible API and downloadable weights, and they deliver frontier-adjacent reasoning and coding quality at a fraction of the token cost of closed alternatives. That price-to-capability ratio makes DeepSeek a strong fit for high-volume generation, classification, and analysis work.
We run DeepSeek through its OpenAI-compatible endpoint, so it drops into our existing gateway alongside other providers behind a single routing layer with prompt caching, retries, and per-task model selection. High-volume, cost-sensitive jobs (bulk content drafting, taxonomy classification, review and comment mining) route to DeepSeek-V3, while multi-step analytical work — attribution reasoning, spec generation, structured data extraction — routes to DeepSeek-R1 where visible chain-of-thought improves reliability. We pin model versions, log token spend per workflow, and A/B outputs against our other models before anything reaches a client surface.\n\nFor teams with data-residency or throughput requirements, we self-host the open weights (or fine-tuned variants) on GPU infrastructure and serve them through vLLM or SGLang with the same API shape, so switching from the hosted endpoint to a private deployment is a config change. We wrap every DeepSeek call in schema validation, JSON-mode enforcement, and eval harnesses so reasoning traces stay auditable and outputs conform to the contracts our pipelines depend on.
We use DeepSeek-V3 to draft programmatic landing copy, product descriptions, and structured metadata across thousands of pages, keeping per-token cost low enough to make large catalogs economically viable while our eval gates hold quality.
DeepSeek-R1's step-by-step reasoning powers marketing-mix and funnel analysis, where the model works through attribution logic and returns explainable conclusions our analysts can audit rather than black-box numbers.
For clients with data-residency needs, we self-host DeepSeek weights and run sentiment, intent, and support-ticket classification entirely inside their infrastructure with no third-party data egress.
DeepSeek's hosted API prices tokens well below most closed frontier models, and the open weights let us self-host for effectively fixed compute cost. That economics is why we route high-volume generation and classification jobs to it.
We use R1 for multi-step reasoning — analysis, planning, structured extraction where a visible thought process improves accuracy — and V3 for fast, cost-sensitive chat and bulk generation where latency and price matter more.
Yes. The models are open weight, so we deploy them on your GPU infrastructure via vLLM or SGLang behind the same OpenAI-compatible API, keeping all inference and data in your network.
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