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Comparison

QuickSilver Pro vs AWS Bedrock

Bedrock is the right choice when AWS-native integration (IAM, Guardrails, Knowledge Bases, VPC endpoints, BYO-VPC inference) is load-bearing for compliance. For everyone else, QuickSilver Pro serves the latest DeepSeek and Qwen weights at competitive per-token rates through the OpenAI-compatible API your stack already speaks. DeepSeek V4 Pro ($0.435 / $0.87) is the closest replacement for Bedrock's DeepSeek reasoning, plus you get the whole V4 wave, Qwen 3.6, and Kimi K2.6 that Bedrock doesn't carry.

At a glance

FeatureQuickSilver Probedrock
Open-source LLM catalogKimi K3, GLM 5.2, MiniMax M3, DeepSeek V4 Flash + Pro, Qwen 3.7 Max, Kimi K2.6Earlier DeepSeek generations, Llama 3.x, Mistral, Nova, Anthropic Claude
DeepSeek V4 Pro$0.435 / $0.87Not offered
API surfaceOpenAI-compatible (drop-in)Bedrock Runtime (AWS SDK) or Converse API
AWS IAM / VPC / GuardrailsNoYes
Closed frontier models (Claude, Nova)Claude yes — 20% below Bedrock's own rateYes
Minimum top-up$5AWS billing
AuthAPI key (Bearer)SigV4 signed requests

Pricing (per million tokens, USD)

Competitor list prices as published by each provider.

ModelQSP inputQSP outputbedrock inputbedrock outputvs. list
DeepSeek V4 Pro$0.435$0.87$0.55$2.19~68% output
DeepSeek V4 Flash$0.112$0.224not on Bedrock

Migration - two lines

After - QuickSilver Pro
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.quicksilverpro.io/v1",
    api_key=os.environ["QSP_KEY"],
)

r = client.chat.completions.create(
    model="deepseek-v4-pro",
    messages=[{"role": "user", "content": "Hi"}],
)

FAQ

Mapping Bedrock's DeepSeek reasoning to DeepSeek V4 Pro, QuickSilver Pro is ~68% lower on output ($0.87 vs $2.19 per 1M). On the rest of the V4 wave (V4 Flash), Qwen 3.6, and Kimi K2.6 the comparison is moot because Bedrock doesn't carry those weights yet — QSP is the only managed provider for the latest DeepSeek and Qwen releases.

If IAM / VPC endpoints / Guardrails / Knowledge Bases / BYO-VPC are load-bearing for your compliance story, or if you need Claude / Nova / closed models in the same provider. The Bedrock premium pays for AWS-native integration; QuickSilver Pro is for teams who don't need that and would rather not pay the markup.

Drop the AWS SDK / SigV4 plumbing and use the openai SDK directly: base_url="https://api.quicksilverpro.io/v1", api_key="$QSP_KEY". Bedrock's Converse API is broadly equivalent in capability to OpenAI chat completions; QSP serves the OpenAI shape directly so any OpenAI SDK works unchanged.

Not natively. QSP is focused on raw chat-completions inference at a narrow surface and predictable price — not a wider LLM platform. For Guardrails-equivalent content moderation, drop in any open-source moderation library against the chat output. For RAG, our 1M-context V4 Pro and 262K-context Qwen 3.6 let you skip the vector store entirely on small-to-mid corpora.

Start with your own key

Change two lines. 20% below list from the first call.

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