ScitiX unveiled the full scope of its production inference platform, purpose-built for enterprises running AI at scale. As organizations move from experimentation to live workloads, the company is positioning inference not as a supporting function, but as the operational core of modern AI stacks.
The platform—running entirely on ScitiX-owned and operated NVIDIA B200, H200, and H100 infrastructure—delivers a unified execution layer that abstracts away the complexity of model orchestration, while giving customers granular control over performance, cost, and compliance. Current production metrics include over 1 trillion tokens processed daily, average time-to-first-token of approximately one second, a cache hit rate exceeding 90%, and 99.9% uptime.
What the platform does
ScitiX Model Inference is designed for enterprises running multiple models simultaneously—whether open-source, fine-tuned, or third-party. Rather than lock customers into a single model provider, the platform serves as a neutral, high-performance routing layer that standardizes access through familiar APIs.
Key capabilities include:
- Intelligent model routing and fallback — Automatically directs queries to the optimal model based on latency, cost, or quality targets, with failover built in.
- Session-aware context reuse — Maintains long-running conversational state and caches intermediate results, drastically reducing redundant compute.
- Fault-tolerant execution — Handles retries, timeouts, and partial failures gracefully, so a single misbehaving call doesn’t break the entire workflow.
- Private deployment environments — Dedicated tenancy options for workloads with strict data residency or security requirements.
- Zero-retention policies — Ensures no customer prompts or outputs persist beyond the transaction, meeting the most stringent compliance standards.
- Full-stack observability — Provides infrastructure-level telemetry, audit logs, and performance dashboards that surface exactly where latency or cost is coming from.
These capabilities are not theoretical. They are live today, supporting some of the most demanding inference workloads in production—including those from RadixArk, the commercial team behind SGLang, which runs its heaviest scenarios on ScitiX.
Designed for the realities of production AI
The platform addresses a specific pain point that has become increasingly apparent across enterprise deployments: model quality matters, but model operations matter just as much. Production failures rarely trace back to model weights. They stem from runtime variability, configuration drift, sandbox timeouts, and unpredictable infrastructure behavior.
ScitiX’s internal evaluation framework, SiEval, reflects this philosophy. Rather than treat evaluation as a leaderboard exercise, SiEval examines the entire execution chain—how results are produced, whether execution paths are reproducible, and whether outputs can support high-stakes decisions like release approval, rollback, or checkpoint promotion. In internal testing, SiEval demonstrated up to 10.5× acceleration on evaluation-heavy pipelines and 7.22× end-to-end speedups across large-scale leaderboard workflows, with the largest gains in pipelines involving LLM judges, sandboxed code execution, and long-context processing.
Why enterprises are shifting to an inference-first model
The economics of AI have shifted. Token prices are falling, but total operational spend is not—because every user interaction can cascade into dozens of internal inference calls. As agentic workflows multiply, managing that complexity with per-model point solutions becomes unsustainable.
ScitiX’s bet is straightforward: the infrastructure layer that manages execution, governance, and observability will matter as much as the models themselves. The platform is built to give enterprises control over the variables that actually impact their bottom line—latency SLAs, per-request cost, data governance, and model agility.
Availability
ScitiX Model Inference is available now to enterprise customers.
For more information, visit scitix.ai/inference.
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