Dedicated model endpoints,
deployed wherever you need them
A private, isolated deployment of the model you choose, with reserved capacity and latency you can plan around. Run it on our infrastructure, your own GPUs, or the cloud you already use.
Serve open-weight models or your own fine-tuned models, with enterprise security built in.
Your endpoint, your infrastructure
The same dedicated endpoint, deployed the way that fits your security and cost requirements. You choose where the GPUs live.
We run the GPUs
You get a dedicated endpoint and nothing to operate. We provision, scale, and manage the hardware behind it, so your team ships inference without running infrastructure.
Run on your own GPUs
Deploy into hardware you already own, inside your own environment. Your data and your inference stay entirely within your boundary.
Use your cloud
Host on your preferred AI cloud, including AWS, Azure, Google Cloud, Oracle, Nebius, and more, billed through the agreements you already have.
A production endpoint,
not a shared queue
Dedicated and isolated
Your own deployment on reserved capacity. No shared endpoints, no neighbors competing for the same GPUs.
Predictable performance
Reserved GPUs mean consistent latency and throughput you can plan around, not performance that shifts with someone else's traffic.
Open and fine-tuned models
Serve open-weight models, or your own fine-tuned versions, on the same dedicated endpoint.
The API you already use
Your applications call the standard inference API they already target. No rewrites to move onto a dedicated endpoint.
Usage metering and billing
Per-token metering and billing are built in, so you can track and attribute exactly what each team consumes.
Enterprise security
SSO, RBAC, SOC 2, private VPC deployment, and data residency by design, across every deployment option.
Each endpoint is its own deployment, with its own model, capacity, and metered usage.
Open models, ready to deploy
Deploy any of these models on your infrastructure,
or bring your own fine-tuned version.
Latest Llama 3.3, strong for general-purpose tasks and reasoning.
Large Llama 3.1 for complex reasoning and language understanding.
Compact Llama 3.1 tuned for efficiency with strong general performance.
NVIDIA's Nemotron 70B, tuned for reasoning and analysis tasks.
DeepSeek R1 distilled into Llama for advanced reasoning.
DeepSeek R1 distilled into Qwen, an efficient reasoning model.
Advanced Qwen 2.5 with strong multilingual and reasoning skills.
Mid-size Qwen 2.5 balancing performance and resource needs.
Efficient Qwen 2.5 with solid general-purpose performance.
Google's Gemma 3 27B with strong instruction-following.
Mid-range Mistral balancing capability and efficiency.
Microsoft's Phi-4 with strong reasoning and math.
Large StarCoder 2 for code generation and analysis.
Shared endpoints are fine
until they're in production
Serverless endpoints work for prototyping.
In production, the tradeoffs start to matter.
Best-effort
- You share capacity with everyone else on the platform, so latency moves with their traffic
- Limited to the models the provider offers, with no room for your own fine-tuned versions
- Your data runs through infrastructure you don't control or see
- Capacity is not guaranteed when you need it most
Yours alone
- Reserved capacity that doesn't flex with the neighbors
- Your choice of model, open-weight or fine-tuned
- Deployed inside your own boundary, on your GPUs or the cloud you pick
- Predictable latency and throughput for real production traffic
Built for teams running real inference
Enterprises in production
Teams running real inference traffic that need capacity and latency they can plan around, not a shared queue.
Regulated industries
Where data residency, isolation, and compliance are requirements, not preferences. Keep everything in your own environment.
Teams outgrowing shared endpoints
When serverless stops being enough and you need a deployment you control, with the model and capacity that fit your workload.
Tell us what you're serving,
and we'll scope it
Pricing depends on the model, the throughput you need, and where you deploy. Talk to us and we'll put together a dedicated endpoint that fits.