Open-Model Inference

Cheaper than any provider
on OpenRouter.

Open models on dedicated, sovereign serving. One stack instead of a router, with a learning loop that keeps cache hit rates high, lowering effective token costs while keeping performance at peak.

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50%

Cheaper than any OpenRouter provider

See the full comparison ↓

0% Platform fees

2-line Migration

Sovereign Dedicated capacity

DeepSeek V4 Flashvs OpenRouter providers

Provider	Input $/1M	Output $/1M	Cache hit rate
PRIMALABS	$0.016	$0.266	90%
DeepSeek	$0.031	$0.279	79.6%
NovitaAI	$0.053	$0.279	78.0%
Fireworks	$0.100	$0.279	35.2%

GLM 5.2vs OpenRouter providers

Provider	Input $/1M	Output $/1M	Cache hit rate
PRIMALABS	$0.133	$2.20	90%
Z.AI	$0.346	$4.40	92.4%
Fireworks	$0.421	$4.40	77.7%
Together AI	$0.534	$4.40	75.9%

FLAGSHIP EXAMPLES. THE SAME STACK SERVES ANY OPEN MODEL · PROVIDER PRICING: OPENROUTER EFFECTIVE PRICING, JULY 2026 · CACHE HIT RATE IS THE HIDDEN MULTIPLIER ON EVERY BILL

Why PrimaLabs

Static stacks tune once. Ours learns every workload.

Customization

Tuned to the workload, not the benchmark

The learning loop observes live prompts, context lengths, and load, then re-tunes batching, scheduling, and cache strategy for that exact traffic. Every model on the stack, its serving gets faster and cheaper the longer it runs.

Cache

Hit rate is where the bill is won

Cached input tokens bill at a fraction of list price. One warm, dedicated stack keeps hit rates high where routed serverless traffic fragments them. Effective cost per token drops below any sticker price.

Sovereign

Dedicated capacity, fully controlled

Reserved throughput on one sovereign stack, in the PrimaLabs cloud, a customer VPC, or on customer GPUs, NVIDIA or AMD. Known data path, predictable latency, cost that falls with scale.

Head to head

PrimaLabs versus OpenRouter

	PrimaLabs	OpenRouter
Infrastructure model	Sovereign, dedicated capacity	Serverless router over shared third-party capacity
Cache behavior	One warm stack, hit rate managed by the learning loop	Cache state fragments across whichever provider gets the request
Workload optimization	Learns each workload and re-tunes continuously	None. Inherits whatever the routed provider tuned once
Data path	Known, fixed, auditable. In-VPC and on-prem available	Traverses 60+ external providers. No sovereignty
Capacity	Reserved, guaranteed throughput	Serverless, best effort

FAQ

Common questions

What does sovereign mean here?

The entire serving stack runs on dedicated infrastructure under one roof, deployable in the PrimaLabs cloud, inside a customer VPC, or on customer-owned GPUs. No request ever touches a third-party provider, so the data path is fixed and auditable.

Why highlight cache hit rate?

Cached input tokens bill at a steep discount, so two providers with the same list price can produce very different invoices. Hit rate depends on the serving stack keeping the workload warm, which a router spreading traffic across providers structurally cannot do.

Can existing OpenRouter code migrate?

Yes. The API is OpenAI compatible, so migration is a base URL and key change. The difference is what sits behind the endpoint: dedicated, sovereign capacity instead of a router.

What does customization mean in practice?

Every deployment is tuned to its own traffic. Prompt shapes, context length distribution, request rate, and cache behavior all feed the loop, so two customers running the same model get two differently optimized stacks.

See the numbers on real traffic

Tell us about the workload and the models it runs. Get dedicated pricing and a cache-aware cost model within days.