Made-to-measure inference
Inference cut to measure,
priced off the rack
We run open-weight models on hardware tuned for throughput. Latency is predictable, efficiency is built into the stack so you get the same output quality at lower cost, and nothing is retained after the response ships.
Cut for throughput
Consumer hardware running our inference stack matches enterprise cluster throughput, with predictable TTFT. Capacity scales in micro-increments, so you add exactly what fits without stepping up to the next reservation tier.
Slips over your existing client
Change base_url in your OpenAI SDK client, a slip stitch that sits invisible from the outside while everything else in your codebase stays the same: streaming and function calling work identically, and structured output does too.
Nothing leaves the fitting room
Requests leave our infrastructure when the response ships. We don't log them or train on them.
Integrate
One alteration, and nothing else needs touching.
Any library that accepts a custom base URL routes through pinstripes. The integration guides walk through the common clients.
Models & Pricing
Same models, sharper price.
We optimise the stack for token efficiency, so the same request costs less here than it does at the name-brand providers — without any difference in output quality.
DeepSeek V4 Flash
DeepSeek V4 Pro
Qwen3.5 35B A3B
Qwen3.6 35B A3B
GLM 4.5 Air
GPT OSS 120B
Llama 4 Maverick
Llama 4 Scout
Kimi K2
Step 3.7 Flash
Qwen3 Coder 30B
DeepSeek V4 Flash
DeepSeek V4 Pro
Qwen3.5 35B A3B
Qwen3.6 35B A3B
GLM 4.5 Air
GPT OSS 120B
Llama 4 Maverick
Llama 4 Scout
Kimi K2
Step 3.7 Flash
Qwen3 Coder 30B
For teams scaling up
Start off the rack,
move to bespoke when the fit demands it.
When data residency requirements, latency constraints, or cost at scale make self-hosting the right call, our deployment stack runs on hardware you already own with the same performance characteristics as the API. No retraining or re-integration is required. You already know how it behaves.