How do I deploy SambaNova on-prem for data residency—what’s the process to schedule a demo and start an evaluation?

Organizations that care about strict data residency need on-prem infrastructure that can run modern LLMs and agentic workloads without sending tokens off-site. SambaNova is built for exactly this: rack-level systems you drop into your data center, with a full inference stack and OpenAI-compatible APIs so your teams can start building in minutes—while keeping data, prompts, and logs within your own borders.

Quick Answer: You deploy SambaNova on-prem for data residency by working with our team to size and design a SambaRack deployment for your data center, then use SambaOrchestrator and OpenAI-compatible APIs to run models locally. To schedule a demo and start an evaluation, submit the contact form on SambaNova’s site and our team will walk you through a structured assessment, demo, and pilot deployment plan.

The Quick Overview

What It Is: An on-prem, rack-level AI inference stack—RDUs, SambaRack systems, SambaStack software, and SambaOrchestrator—designed to run frontier-scale LLMs and agentic workflows inside your own data center for strict data residency.
Who It Is For: Infrastructure, platform, and security teams that need sovereign or in-country AI, have power and cooling constraints, and want OpenAI-compatible inference without relying on a public hyperscaler.
Core Problem Solved: Eliminates “one-model-per-node” and cloud-only patterns that make agentic inference expensive, slow, and non-compliant with data residency requirements—while giving you local, high-throughput, cost-efficient LLM serving.

How It Works

SambaNova’s on-prem deployment is a full-stack, data-center-ready solution: SambaRack systems with SN40L-16 or SN50 RDUs, integrated with SambaStack and SambaOrchestrator. You deploy racks into your facility, connect to network and power, and expose OpenAI-compatible APIs to your applications. Models, prompts, and intermediate agent state stay on your infrastructure, satisfying data residency and sovereignty requirements.

Plan & Design (Discovery and Sizing):
You work with SambaNova to map workloads (LLMs, agents, RAG, multi-model flows), data residency constraints, and facility limits (power, cooling, rack space). We size SambaRack SN40L-16 or SambaRack SN50 configurations based on throughput targets, model mix, and redundancy requirements.
Deploy & Integrate (Rack Install + Control Plane):
SambaRack arrives as a data-center-ready system. Your team racks and connects it, and SambaOrchestrator provides the control plane—Auto Scaling | Load Balancing | Monitoring | Model Management—so you can run and scale inference on-prem. APIs are OpenAI compatible, so apps can be pointed at your internal endpoints with minimal code changes.
Evaluate & Scale (Pilot, Validation, Production Rollout):
You start with a focused evaluation: target apps, representative prompts, and performance SLOs (latency, tokens/sec, cost per token). Once validated, you expand to additional agent workflows, more models (including open-source like DeepSeek and Llama), and larger user populations, all staying within your data center for data residency.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
On-Prem SambaRack Systems (SN40L-16, SN50)	Delivers rack-level, chips-to-model computing built around SambaNova RDUs and three-tier memory	Run frontier-scale models and agentic workflows in your own data center, satisfying local data residency and sovereignty constraints
SambaStack with Model Bundling	Executes multiple frontier-scale models on a single node and switches between them in real time	Avoid “one-model-per-node”; run complex agentic flows (planner, tools, RAG, critics) on one system with lower latency and higher efficiency
SambaOrchestrator Control Plane	Provides Auto Scaling	Load Balancing
OpenAI-Compatible APIs	Exposes familiar /chat/completions, /embeddings, and related endpoints	Port existing OpenAI-based apps to on-prem SambaNova in minutes, without a rewritten client stack
Three-Tier Memory Architecture	Keeps models and prompts “hot” in tiered memory on the SN50 RDU	Minimizes memory movement, enabling high tokens-per-watt and fast agentic loops even as context windows and prompt chains grow
Sovereign AI Partner Ecosystem	Offers in-country, SambaNova-powered data centers (e.g., partners in Australia, Europe, UK)	Combine on-prem with sovereign-hosted options when you need both local control and region-specific hosting flexibility

Ideal Use Cases

Best for strict data residency and sovereignty:
Because you can deploy SambaRack inside your own facilities (or with a sovereign partner) so that PII, regulated data, and model logs never leave your jurisdiction.
Best for high-throughput agentic inference at lower cost:
Because SambaStack runs multiple models per node via model bundling and RDU dataflow, delivering high tokens-per-watt and up to 3X savings vs competitive chips for agentic inference, especially where workflows chain many model calls.
Best for regulated industries modernizing LLM stacks:
Because the system ships with formal safety and regulatory documentation (FCC/ICES, EU directives including RoHS, UK regulations, WEEE programs) and can be integrated into existing security, audit, and compliance frameworks.

Limitations & Considerations

Facility readiness and lead time:
You need sufficient rack space, power, cooling, and network integration capacity. Deployment is rack-level infrastructure, not a single appliance; planning with facilities and security teams is essential. SambaNova will help you size and phase the rollout, but procurement and data center changes can add lead time.
Operational ownership:
On-prem means your team owns physical operations—racking, power, network, and day-2 maintenance. SambaOrchestrator simplifies AI workload management, but you still integrate with your existing monitoring, ticketing, and incident processes. If you want less operational load, a sovereign or managed SambaCloud option may be a better starting point.

Pricing & Plans

Pricing for on-prem SambaNova deployments is tailored to your workload mix, performance targets, and data residency requirements. Typical inputs include:

Target tokens/sec and concurrency for LLM and agent workloads
Model mix (e.g., DeepSeek-R1, Llama, gpt-oss-120b) and context window needs
Number of racks and redundancy/failover requirements
Deployment model (fully on-prem vs. hybrid with sovereign partners)

While specific SKUs and prices are discussed directly with the SambaNova team, you can think in terms of:

Evaluation / Pilot Deployment:
Best for teams needing to validate throughput, latency, and compliance for a limited set of workloads (e.g., a handful of core applications, a single business unit, or one geography). Typically involves a smaller SambaRack footprint and a time-bound pilot under clear success metrics.
Production / Scale-out Deployment:
Best for organizations ready to standardize on SambaNova as their inference backbone across multiple apps, teams, or regions. Involves multi-rack configurations, higher-availability design, and deeper integration with CI/CD, observability, and governance.

Detailed pricing, including TCO comparisons vs GPUs and cloud consumption, is part of the evaluation and solution design process.

Frequently Asked Questions

How do I actually schedule a demo and start an on-prem evaluation?

Short Answer: Fill out the contact form at SambaNova’s site, select your interest in on-prem / data residency, and a solutions team will guide you through discovery, demo, and evaluation scoping.

Details:
The step-by-step sequence looks like this:

Submit the Contact Form:
Go to https://sambanova.ai/contact and provide your details, selecting interests related to on-prem deployment, data residency, or sovereign AI. Include a brief description of your workloads (e.g., “agentic inference for healthcare data in-country”).
Initial Discovery Call:
SambaNova’s team will schedule a session with your platform, infra, and security stakeholders. Expect to cover:
- Data residency and regulatory requirements (e.g., data must remain in-country, no public cloud)
- Current model usage (OpenAI API, self-hosted models, agent frameworks)
- Throughput, latency, and SLO expectations
- Data center constraints (power, cooling, rack space, networking)
Technical Deep Dive + Demo:
You’ll see SambaStack and SambaOrchestrator in action, including:
- Running key models (e.g., DeepSeek, Llama, gpt-oss) via OpenAI-compatible APIs
- Demonstrations of model bundling and multi-model workflows on a single node
- Observability and scaling via SambaOrchestrator
Evaluation Plan & Proposal:
Together, you’ll define:
- Scope of the pilot (apps, workloads, regions)
- Success criteria (tokens/sec, latency, cost per token, compliance checkpoints)
- Required SambaRack configuration and timeline
Pilot Deployment & Validation:
After hardware provisioning and installation, your team ports existing OpenAI-based apps to the on-prem endpoints (often by just changing the base URL and API key) and runs structured tests. Results then feed a production-scale rollout plan.

How does SambaNova ensure data residency and sovereignty for on-prem deployments?

Short Answer: All inference runs on your hardware in your data center (or a sovereign partner’s); models, prompts, and logs stay within that environment, under your controls and policies.

Details:
From a systems perspective, data residency is enforced by architecture and deployment model:

On-Prem or Sovereign Data Center Placement:
SambaRack systems are physically installed in your own data centers or with a sovereign partner within your jurisdiction (e.g., specialized providers across Australia, Europe, and the UK). No inference traffic needs to leave that environment.
Local Inference Stack:
RDUs, SambaStack, and SambaOrchestrator run entirely within that controlled environment. Requests hit your internal endpoints; model weights, prompts, and agent state are processed in-place.
No Required Call-Out to Public Cloud:
Agentic workflows, multi-model routing, and orchestration are handled within SambaStack and SambaOrchestrator without mandatory round-trips to public cloud APIs.
Enterprise Controls & Compliance:
You can integrate SambaNova logs and metrics into your SIEM, apply your own access controls (RBAC, network segmentation), and enforce retention and deletion policies according to local regulation.

This makes SambaNova suitable for workloads where any off-prem or cross-border data movement is unacceptable, including finance, healthcare, public sector, and national-scale sovereign AI programs.

Summary

Deploying SambaNova on-prem for data residency is a structured path: design the right SambaRack footprint for your agentic and LLM workloads, drop the racks into your own data center (or a sovereign partner’s), and expose OpenAI-compatible APIs your teams already know how to use. Under the hood, RDUs with three-tier memory and SambaStack’s model bundling give you high tokens-per-watt and the ability to run multiple frontier-scale models on a single node, so complex agent workflows execute end-to-end without leaving your infrastructure.

If your mandate is clear—keep data local, reduce inference cost, and avoid the one-model-per-node trap—SambaNova’s chips-to-model computing and full-stack inference design are built to meet it at rack scale.

Next Step

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