Target performance, measured like it's already live
How requests move through the platform
Four layers between your HTTP call and a streamed token. Each one is boring, observable, and fast.
Geo-DNS with latency-based routing. TLS terminated at the edge, rate limits applied before anything touches compute.
Priority queues per model. Load balanced across healthy regions, KV-cache aware placement.
Continuous batching, token streaming with sub-50ms TTFB, automatic prompt deduplication.
Dedicated Instinct MI355X nodes per workload. Region failure detected in <1s, traffic rerouted without dropping connections.
Dedicated silicon. No abstraction leaks.
Workloads land on dedicated AMD Instinct MI355X accelerators, tuned and monitored end to end — never oversubscribed, never sharing a box with someone else's training run.
What the platform does
First token in under 50ms. Stream responses token-by-token with sub-second TTFB.
We handle the scaling. Traffic spikes are absorbed automatically. You never think about capacity.
Geo-DNS with latency-based routing. Requests hit the nearest healthy region.
Token-level logging, request tracing, cost attribution. Export to any stack.
KV-cache reuse across requests. Automatic prompt deduplication.
Region failure detected in <1s. Traffic rerouted without dropping connections.
An inference company, not an AI lab.
We don't train models and we don't build products on your data. Prompts and completions are processed in memory and never written to disk — zero retention by architecture, not by policy.
Prompts, completions, and uploaded files live in volatile memory for the duration of the request. Then they're gone.
No fine-tuning, no evals, no "service improvement" pipelines. Your confidential data is never model input again.
We have no models of our own and nothing to gain from your data. Our only product is fast, private inference.
Drop-in apps your team already knows.
Polished desktop and web assistants that feel instantly familiar — wired to your models, your SSO, your audit logs. Migrate seats in an afternoon, not a quarter.
- Instantly familiar. Drop-in replacements for the AI chat apps your team already uses every day.
- SSO & SCIM provisioning. Seats follow your identity provider, not a spreadsheet.
- Your models, your rules. Per-workspace model selection, admin controls, usage caps.
- Audit everything. Full request logs and cost attribution per seat, per team.
- Zero retention on every message. Privacy guarantees apply to apps and API alike.
Multiple models. One endpoint.
{
"model": "GLM-5.2",
"messages": [{"role": "user", "content": "Hello"}],
"stream": true,
"temperature": 0.7
}
{
"id": "chatcmpl-...",
"model": "Kimi-K2.7",
"choices": [{
"delta": {"content": "Hello"}
}],
"usage": {"prompt_tokens": 12, "completion_tokens": 284}
}
A tenth of the cost. All of the capability.
One flat seat price for the apps, usage-based pricing for the API. Either way, expect roughly a 10x reduction against your current AI spend.
- All desktop & web apps included
- API access with usage-based billing
- Multiple models, one endpoint
- Zero data retention, always
- Personal use, no seat math
- All desktop & web apps included
- API access with usage-based billing
- Multiple models, one endpoint
- Zero data retention, always
- SSO & audit logs
- Volume pricing across seats & tokens
- Dedicated capacity on MI355X clusters
- VPC / private deployment options
- Custom SLAs & compliance reviews
- SCIM provisioning & dedicated support
Start building.
Early access for cost aware teams building with LLMs. No commitment required.
Join Waitlist →