Every model. One API.
Run leading open and frontier models through a single OpenAI-compatible API. Switch models without rebuilding your stack.
Tune inference around what matters to you.
Set your target for latency, throughput, quality, and cost. Open Strata configures each deployment around the performance profile your workload actually needs.
Pay for inference, not idle GPUs.
Use one compute commitment across models and hardware while capacity scales with demand. Your infrastructure expands when traffic spikes and contracts when it doesn't.
Capacity that moves with your traffic
Open Strata endpoints scale around real traffic, giving you dedicated-class performance without permanently reserving dedicated-class capacity. Get the performance of dedicated infrastructure while paying for the inference you actually use.
Your traffic isn't fixed. Your infrastructure shouldn't be either.
Overprovision and you pay for idle accelerators. Underprovision and performance collapses when demand matters most. Open Strata removes that tradeoff.
One API. Any model.
Run frontier models, open models, and your own fine-tunes behind one compatible endpoint. Change the model without changing your application.
Research & Engineering
Research on model serving, inference performance, infrastructure economics, and the systems required to run AI reliably at scale. Written by the engineers building Open Strata.
Kimi K3 vs. GPT-5.6 Sol: Performance, cost, and tradeoffs
Compare Kimi K3 and GPT-5.6 Sol on performance, pricing, context, weights, architecture, and deployment tradeoffs.
How Open Strata built one AI agent for inference operations
The hard part wasn’t putting an AI agent in Slack. It was embedding trusted sources, reviewed calculations, and safe inference-operations workflows.
Building sub-second LLM inference for global AI traffic
A fast model isn’t a fast API. We worked backward from a 600ms p99 budget with Cloudflare Workers at the edge and WireGuard to the GPU.
Your questions, answered.
Can we move workloads away from closed-model providers?
Yes. Open Strata lets you run production workloads on leading open models without rebuilding your application around another proprietary vendor. Most teams begin by routing a portion of traffic through Open Strata, compare performance and economics, then migrate the workloads where open models make sense.
Can open models replace closed models in production?
For many workloads, yes. The right model depends on the task. We help teams benchmark open models against their existing stack using real production workloads before moving traffic.
Can I deploy custom or fine-tuned models?
Yes. Open Strata supports specialized models and fine-tunes alongside general-purpose LLMs, so different AI workloads can run through the same infrastructure layer. Deploy compatible Hugging Face models, fine-tunes, and specialized architectures through the same platform.
What happens when something breaks?
You work directly with engineers who understand your deployment. Production issues are handled by the people responsible for the infrastructure, not passed through a generic support queue.
How quickly can we deploy?
Standard model endpoints can be deployed quickly, while optimized or custom environments depend on the workload. Open Strata handles infrastructure configuration so your team can focus on integration rather than GPU operations.
Why use Open Strata instead of self-hosting?
Self-hosting gives you control, but it also means managing GPU capacity, model serving, scaling, observability, upgrades, and infrastructure around the clock. Open Strata provides the control of dedicated infrastructure without requiring your team to become an inference operations company.
Put your models into production.
Run leading open models, custom models, and fine-tunes through one production inference layer.