QuickSilver Pro vs Azure OpenAI Service
Azure OpenAI Service runs the closed OpenAI catalog (GPT-4o, o1, o3-mini) on Microsoft infrastructure with Azure-native compliance, Private Link, and Entra ID auth. For workloads where an open-source model is quality-equivalent, QuickSilver Pro serves the DeepSeek V4 wave (V4 Flash + Pro) at 6x–30x lower output cost and exposes them through the same OpenAI SDK — no resource group provisioning, no AAD setup, no Cognitive Services quota requests.
At a glance
| Feature | QuickSilver Pro | azure-openai |
|---|---|---|
| Model catalog | Open-source LLMs (DeepSeek V4 Flash + Pro, Qwen 3.7 Max + 3.6 Plus + 3.6, Kimi K2.6) | GPT-4o, o1, o3-mini, GPT-4o-mini (closed) |
| Model weights | Open (MIT / Apache) | Closed |
| Low-cost chat output (GPT-4o-mini / V4 Flash) | $0.224 / 1M | $0.60 / 1M |
| Premium reasoning output (o3-mini / V4 Pro) | $2.10 / 1M | $4.40 / 1M |
| API setup | Sign up, paste key | Provision resource, request quota, AAD |
| Private Link / Entra ID / Sentinel | No | Yes |
Pricing (per million tokens, USD)
Competitor list prices as published by each provider.
| Model | QSP input | QSP output | azure-openai input | azure-openai output | vs. list |
|---|---|---|---|---|---|
| deepseek-v4-flash vs gpt-4o-mini | $0.112 | $0.224 | $0.14 | $0.60 | ~63% |
| deepseek-v4-pro vs o3-mini | $0.70 | $2.10 | $1.10 | $4.40 | ~52% output |
| qwen3.6-35b vs gpt-4o | $0.112 | $0.80 | $2.50 | $10.00 | ~92% |
Migration - two lines
import os
from openai import OpenAI
# Was: AzureOpenAI(azure_endpoint=..., api_version=..., api_key=...)
client = OpenAI(
base_url="https://api.quicksilverpro.io/v1",
api_key=os.environ["QSP_KEY"],
)
r = client.chat.completions.create(
model="deepseek-v4-pro", # or deepseek-v4-flash, qwen3.6-35b, ...
messages=[{"role": "user", "content": "Hi"}],
)FAQ
Yes — the OpenAI SDK is the same shape Azure exposes. Change `azure_endpoint` to a plain `base_url=https://api.quicksilverpro.io/v1`, drop the deployment-name indirection (use the model ID directly: deepseek-v4-flash, deepseek-v4-pro, etc.), and supply your QSP key. Streaming, tool calling, and usage accounting all work. JSON schema strict mode is model-dependent — the Claude models do not support it, so use a tool with `strict: true` there.
When AAD auth, Private Link, Sentinel logging, or Microsoft Compliance Manager mappings are non-negotiable. Also when you need closed-model capabilities (real-time audio, DALL-E, the Assistants API) or when GPT-4 measurably beats DeepSeek V4 on your evals. QuickSilver Pro is for the chat / coding / RAG slice where open-source matches.
On the direct quality maps: GPT-4o-mini→V4 Flash is ~63% lower on output. o3-mini→V4 Pro is ~6× lower on output. Real-world bills typically land at 10–20% of Azure OpenAI for traffic that re-routes cleanly to open-source.
QuickSilver Pro infrastructure is hosted on dedicated bare-metal in Europe (OVH) with US edge. Region pinning is available on teams plans for data-residency requirements. For full Azure-region-locked inference with sovereign-cloud controls, Azure OpenAI is the right tool.