Capacity-priced AI infrastructure
Your AI bill should not rise
with every token.
Run compatible models on dedicated capacity, billed by the second at a rate locked when your instance starts. Keep your OpenAI-compatible code, choose the model, and plan spend without token-meter anxiety.
Want to explore first? Open the demo
A clear cost boundary for production AI. Dedicated endpoint, isolated inference, and a capacity rate you can forecast before you scale.
Models available through one API
Model providers
Deepseek Ai
Qwen
Zai Org
Unsloth
Yuxinlu1
Empero Ai
Google
HauhauCSDeepseek AiQwenZai OrgUnslothYuxinlu1Empero AiGoogleHauhauCSChoose the cost boundary
Stop treating production usage as an open-ended meter.
Token pricing is useful when usage is occasional or uncertain. For steady workloads, plan around the capacity your application needs instead.
See Compute pricingToken-priced services
Usage sets the cost.
Every input and output token changes the bill. Costs rise as requests, context, and model output grow.
QDivZero Compute
Capacity sets the cost.
An instance is billed by the second at the rate fixed when it launches. Schedule it around the work you need to run and forecast that capacity before you scale.
Compute uses active-capacity pricing. Public Models remain token-priced.
Keep your integration. Control what you spend.
Point existing OpenAI-compatible code at QDivZero. Compute capacity is billed by the second at the rate locked when an instance launches, so your team can plan around the capacity it needs instead of watching a token meter climb.
See capacity pricingfrom openai import OpenAI
client = OpenAI(
base_url="https://api.qdiv0.com/v1",
api_key="your-api-key",
)
response = client.chat.completions.create(
model="your-model",
messages=[
{"role": "user", "content": "Hello world"},
],
)
Compute
Run compatible models through one OpenAI-compatible endpoint.
Run compatible Hugging Face models through an OpenAI-compatible endpoint. Active capacity is billed by the second at a rate locked when the instance launches.
Explore ComputePricing estimates
Pool 03
Example: qwen-cheap
Pool 01
Example: qwen-fast
Illustrative monthly estimate
Illustrative estimate; actual cost depends on active capacity.
Flexible Vector Database
Reuse Compute embeddings for semantic search, discovery, and recommendations.
Reuse a Compute embeddings instance for semantic search, discovery, and recommendations instead of adding a separate embedding service.
Explore Flexible Vector DatabaseSemantic search
traveller_backpack_pro
Illustrative result · waterproof
weekender_duffel
Illustrative result · weekend
hydration_pack
Illustrative result · durable
Firewall
Keep blocked requests away from the target model.
Attach guardrails to supported chat-completion requests and keep blocked requests away from the target model.
Explore FirewallRequest flow
Allowed request
firewall: production-guardrails
Blocked request
prompt injection detected
Pending review
awaiting verification
Smart Balancers
Route requests by configured intent and priority.
Route by configured intent and priority, with ordered failover between healthy destinations. Compute-backed routes retain capacity pricing; Public Model routes remain token-priced.
Explore Smart BalancersExample balancer: support-balancer
Primary
Example: qwen-cheap
Fallback
Example: public-gpt-oss-120b
Priority
1 → 2
Trigger
ordered failover
Next step / 05
Make AI spend a decision, not a surprise.
Deploy compatible models on active capacity billed by the second at the rate set when you launch. Keep your OpenAI-compatible application and scale on a budget you can plan.