We build apps, agents and coding tools on DeepSeek's open models, and run them safely: in your region, in your cloud or on your own servers, with the testing and guardrails production needs.
Each word wakes only a few of the model's experts.
Simplified illustration.
DeepSeek has shipped quickly, and each release changed what open models could do. This is the short version.
DeepSeek's models are split into many specialist “experts”. For each token only a few are switched on, so you get the knowledge of a giant model at the running cost of a much smaller one.
Bars are scaled to each model's own total. Figures from DeepSeek's V4 announcement.
DeepSeek models can answer instantly or think first. Thinking costs time and tokens, so we turn it on only for the requests that need precision.
“What is the EMI on a ₹25 lakh loan at 9% for 15 years?”
Illustrative example.
The model is open, but where it runs decides who can see your data. Answer a few questions for our recommendation.
The full weights are large. Most private deployments use V4 Flash or a distilled model, often quantised to cut memory further.
Approximate sizes of the published weights. GPU memory needed depends on precision and traffic.
Our view: DeepSeek models are excellent value, and most concerns are about the hosted service rather than the weights. Here is how we handle them.
Where DeepSeek's reasoning and low cost give a clear advantage.
We are model-neutral and often combine several behind one API — DeepSeek for volume reasoning, others where they fit better.
| Model family | Strengths | When we choose it |
|---|---|---|
| DeepSeek | Strong reasoning and coding at very low cost; MIT open weights | Cost-sensitive reasoning, self-hosted deployments |
| Qwen | Open-weight family, strong multilingual | Multilingual and Asian-language products |
| Meta Llama | Open weights with the widest tool support | Private deployments and fine-tuning |
| OpenAI | Hosted voice, agents and images | Multimodal products, fastest launch |
| Anthropic Claude | Long documents, coding, dependable agents | Contracts, research, coding agents |
| Google Gemini | Multimodal with Google grounding | Video, voice and Google Cloud apps |
Straight answers to what clients ask us most.
DeepSeek V4, released as a preview in April 2026, is DeepSeek's current model generation. V4 Pro has about 1.6 trillion total parameters with 49 billion active per token; V4 Flash has 284 billion total with 13 billion active. Both are mixture-of-experts models with a 1-million-token context and MIT-licensed weights.
The models themselves can be run safely. The main question is where your data goes: DeepSeek's own API processes requests in China, so for personal, client or regulated data we self-host the open weights or use a provider in your region, and add the usual guardrails and testing.
Yes. The weights are MIT-licensed, so commercial use and self-hosting are allowed. V4 Pro is very large (roughly 865 GB of weights), so most private deployments use V4 Flash or smaller distilled models, quantised to fit available GPUs.
In thinking mode the model works through the problem step by step before answering. It is slower and uses more tokens but is much more reliable for maths, logic, code and planning. We switch it on only for the requests that need it.
Yes. DeepSeek's API follows the OpenAI Chat Completions format (and supports the Anthropic format), so switching is usually a change of base URL, key and model name. Self-hosted servers such as vLLM expose the same format.
DeepSeek is among the lowest-cost strong models, with discounted off-peak rates on its own API, and self-hosting removes per-token fees entirely at high volume. We compare it with other models on your own tasks before recommending it.
Yes, usually on the smaller distilled models, where LoRA fine-tuning is practical. For the large V4 models we normally rely on prompting and retrieval instead.
Tell us what you want to build and how sensitive your data is. We reply within 4 business hours with honest advice.