High-throughput agentic work with 1M context
Selected DeepSeek models served through an optimized inference stack built for lower request cost at production volume.
Infrastructure designed for sustained throughput, stable latency, efficient GPU utilization, and cost-efficient inference.
Prompts and outputs are processed for inference, not stored after completion, and never used to train models.
Use $25 free credits to evaluate throughput, TTFT, latency, request success, and output quality before paying anything or moving traffic.
What is DeepSeek good for? DeepSeek is a family of MIT-licensed open-weight models with their strongest published results in software engineering — coding agents, tool-calling loops, and long-context document work — at per-token costs far below dense frontier models, because sparse MoE designs activate only a small share of parameters per request.
The family’s strongest published benchmarks are software-engineering ones — DeepSeek V4 Flash resolves 79% of SWE-bench Verified issues in max-thinking mode.
Sparse MoE activation — 13B of 284B parameters on V4 Flash — keeps per-request cost low across thousands of daily tool calls.
A 1M-token context window with sparse attention reads entire repositories and document sets in a single prompt.
Strict function calling and JSON mode return schema-valid output for ticket, PDF, and form pipelines at production volume.
Long context fits retrieved passages without aggressive chunking, and thinking modes add reasoning depth only where a query needs it.
Low per-token pricing and deep cache-read discounts suit offline enrichment, tagging, and report generation over large datasets.
Code examples
curl -X POST "https://api.entrim.ai/v1/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $ENTRIM_API_KEY" \ -d '{ "model": "deepseek-ai/DeepSeek-V4-Flash", "messages": [ { "role": "user", "content": "Why do they call it a building if it is already built?" } ] }'SDKs & docs
Use familiar SDK patterns with quickstart examples for common production setups.
OpenAI-compatible
Keep the request format your team already knows.
No cold starts
Production requests are served without model spin-up delays.
Prompts and outputs are processed for inference, not stored after completion, and never used to train models.
Inference and request processing stay within Entrim’s EU-operated environment, with DPA support for customers.
Requests are encrypted, access is isolated by API key, and traffic is handled with tenant-level separation.
Controls are being built around recognized security frameworks, with limited operational metadata used for billing, monitoring, latency, and abuse prevention.