DeepSeek’s official platform describes its current products and access options. Availability and terms can change, so architecture decisions should use current documentation rather than a fixed model list.
What makes DeepSeek relevant?
DeepSeek combines hosted inference with downloadable open weight models. This gives teams more deployment choices than an API-only offering, but it also moves more operational responsibility to the team. Self-hosting requires capacity planning, security updates, monitoring, and model-serving expertise.
Open weights do not automatically mean open source. Always review the model license and usage terms before selecting a deployment path.
When should a team evaluate DeepSeek?
DeepSeek is worth testing when reasoning, coding, cost, or deployment control matters. The decision should come from task-specific evaluation, not public leaderboard position alone. Compare quality, latency, throughput, privacy requirements, regional availability, and failure behavior with another LLM provider. An LLM gateway can make controlled comparisons and later switching easier.
Why does the hosting route matter?
The same model name can be offered through the developer’s own service, a cloud marketplace, or another inference provider. Those routes can differ in regions, retention rules, throughput, model updates, and contractual support. A team should record both the model identifier and the serving provider in every evaluation. Model quality alone cannot establish whether a DeepSeek deployment fits company policy. Put routing and credentials behind an LLM gateway and compare the approved route with alternatives using the same AI evaluation harness.