Fast, secure AI inference for agents, with models and data you own
Agents, copilots, and coding tools are only as good as the inference behind them. SambaNova delivers fast tokens on the largest open models, with the security, model ownership, and deployment flexibility enterprises need.
See how coding agents run on SambaNova ⟶
Whether you are an AI-native company shipping agents to millions of users or an enterprise moving AI into production, you can start building with fast AI inference today on SambaCloud and scale to dedicated racks in your own data center or with our partner Neoclouds.
Agentic AI turns inference speed into a business metric
Agents run in loops, not one-offs
They read context, call tools, generate, verify, revise, and continue until the task is complete.
Latency compounds across every step
In agentic workflows, slow output tokens do not delay one response, they slow the entire task.
Production AI needs more than raw speed
Teams need privacy, deployment choice, governance, and infrastructure that fits the environments they already run.
Maximize productivity with fast inference for agents
Agents do not stop at one answer. They read context, call tools, write code, generate outputs, check work, and loop until the task is complete. Every added step compounds latency.
Slow tokens do not delay one reply; they delay the entire workflow your team or your customers depend on. SambaNova gives teams fast, responsive inference so coding agents, copilots, voice agents, research assistants, and internal workflows keep moving at user speed.
When inference is fast, users wait less, agents complete more steps, and applications feel interactive instead of delayed.
Delivering fast tokens is a data-movement problem. SambaNova’s Dataflow Architecture maps the model graph to an efficient path across the reconfigurable dataflow unit (RDU) chip instead of making repeated trips to off-chip memory. Less memory movement means lower latency and less power for the same work.
- Built for coding agents, copilots, voice AI, and research workflows
- Supports leading open-source models through OpenAI-compatible APIs
- Designed for multi-step, multi-model AI applications
- Enables a path from experimentation to production deployment
Premium inference for coding agents
Coding is one of the most demanding and fastest-growing AI use cases. Developers need infrastructure that can keep up with structured inputs, streaming responses, tool calls, and iterative workflows without slowing the user down.
SambaNova’s full stack solution, from hardware to software, is designed to run the largest open models with the fastest speeds, so agents finish refactors, migrations, and test-fix loops in a fraction of the time.
With SambaStack, organizations can support the multi-step patterns that modern AI apps depend on while keeping the path open to private and dedicated deployments as requirements evolve.
Keep your GPUs. Add the speed agents need.
SambaStack orchestrates models across SambaRack™ systems and can complement your existing GPU infrastructure, serving workloads behind a standard API endpoint. This gives teams a practical path to higher-performance inference without forcing an all-or-nothing infrastructure change.
For AI-native teams, this means faster product experiences and a clearer route to scale. For enterprise IT, it means improving inference architecture while preserving existing investments and deployment flexibility.
You own your models. You control your data.
SOC 2
Type 2
ISO 27001
Private
Deployment
Data Control
Air-gapped Deployment
Your data is one of your most valuable assets, and once you fine-tune a model on it, the model is too. With SambaNova you always own your models and control your data. Deploy in the cloud, in your data center, or in air-gapped environments, with security built to meet data sovereignty, regulatory, and privacy requirements without sacrificing performance.
- Zero data retention: prompts and completions are not stored or used for training
- Air-gapped and fully disconnected operation for the most sensitive workloads
- SOC 2 Type 2 and ISO/IEC 27001:2022 certified
- Model ownership: models you build and tune are yours to run, retire, relocate, or restrict
Companies building with SambaNova
JPMorganChase selected SambaNova as an inference infrastructure partner and is deploying SambaRack SN40 systems to power secure, on-premises AI inference using leading open-source models.
“AI infrastructure has to meet a very high bar for performance, control and reliability.”
— Darrin Alves, CIO, Infrastructure Platforms, JPMorganChase
Hume.ai uses SambaCloud to deliver expressive speech-language models for emotionally intelligent voice agents, with response times on the order of 100–300 ms and private deployment options for enterprises.
Maitai delivers fully managed inference to enterprises, fine-tuning customer applications and hosting them on SambaNova for low latency, high throughput, and better accuracy.
FAQs
What does SambaNova offer AI natives and enterprises?
How fast is SambaNova inference?
SambaNova is built for fast output tokens. On SambaCloud, MiniMax M2.7 runs at around 400 output tokens per second, the fastest of any provider measured by Artificial Analysis. On SambaRack SN50, independent benchmarks by SemiAnalysis and Artificial Analysis measured the same model at around 800 tokens per second. Agents generate tokens at every step of a task, so that speed shortens the whole workflow.
Which models can I run on SambaNova?
SambaCloud serves leading open-weight models from DeepSeek, OpenAI (gpt-oss), Meta (Llama), MiniMax and Google (Gemma). The current list, with context lengths, is on the SambaCloud models page. With SambaStack, teams can also deploy their own fine-tuned versions of supported models, which then run like any other model on the platform.
Can I start in the cloud and move on-premises later?
Yes. Start on SambaCloud and move to dedicated capacity or on-premises SambaStack as your needs evolve, using the same APIs and without rearchitecting your AI stack.
Who owns the models and data?
You do. Prompts and completions are not stored or used for training, and models you fine-tune remain yours across cloud, on-premises, and hybrid deployments.
Do I have to replace my GPUs?
No. SambaStack works alongside existing GPUs. With disaggregated inference, GPUs handle prefill and RDUs handle latency-sensitive decode, behind a single API endpoint.