DeepSeek integration for cost-efficient reasoning: open-weight models that bring strong performance to custom apps at a fraction of frontier pricing.
Paying frontier prices for non-frontier tasks
Most production LLM traffic is repetitive: classify this, extract that, summarize the other. Routing all of it through the most expensive model on the market burns budget for capability the task never uses. A well-placed efficient model handles the bulk while the expensive one handles the exceptions.
What we build with DeepSeek
- DeepSeek-backed processing pipelines for classification, extraction, and summarization
- Model routing layers that send each task to the cheapest model that clears quality
- Self-hosted deployments of open-weight DeepSeek variants where data control matters
- Side-by-side quality and cost benchmarks against your current model spend
How we work
Profile your existing traffic to find over-served tasks
Benchmark DeepSeek on those tasks against current output quality
Roll out behind a routing layer so fallback is instant
Track quality and cost deltas in production, not just tests
Typical stack
Frequently asked questions
For the API, that depends on your data-residency requirements, which we review case by case. Open-weight variants can run entirely inside your infrastructure, which removes the data-sharing question completely.
Usually as the high-volume workhorse in a multi-model setup. We keep frontier models for the hardest reasoning and route the routine majority to efficient models, cutting spend without a visible quality drop.