SambaNova vs Google Cloud Vertex AI: best option for serving multiple open models with routing and fast switching

Running multiple open models in production is where most inference stacks start to show their seams. You’re juggling Llama and DeepSeek, maybe a reasoning model like gpt-oss-120b, plus a few internal checkpoints—then layering routing, A/B tests, and agentic chains on top. The core question isn’t “cloud vs not-cloud”; it’s whether your infrastructure can keep those models hot, switch between them quickly, and stay within power and budget.

This comparison looks at SambaNova and Google Cloud Vertex AI specifically through that lens: serving multiple open models with routing and fast switching for real agentic workloads.

Quick Answer: If your priority is high-throughput, multi-model agentic inference with fast switching on frontier-scale open models, SambaNova’s chips-to-model stack (RDUs + SambaStack + SambaOrchestrator + SambaCloud) is purpose-built for that pattern. Vertex AI is a strong general-purpose managed ML platform, but it still inherits GPU-style “one-model-per-node” constraints that make routing-heavy, multi-model workflows harder and more expensive to run at scale.

The Quick Overview

What It Is:
A comparison between SambaNova’s full-stack inference platform and Google Cloud Vertex AI for serving multiple open models with routing, model switching, and agentic workflows.
Who It Is For:
Platform teams, infra leads, and ML engineers responsible for production LLM serving—especially where power, cost per token, and multi-model routing are first-order constraints.
Core Problem Solved:
Choosing an inference stack that can efficiently serve many open models, route between them dynamically, and keep latency and cost under control as prompts grow and agent loops get complex.

How It Works

At a high level, both SambaNova and Vertex AI let you:

Serve open-weight and proprietary models behind an API
Stand up model endpoints and scale them with traffic
Implement routing/ensembling logic in your application layer

The divergence is how they handle multi-model inference at scale:

SambaNova starts from the workload: agentic, multi-step inference that calls multiple frontier-scale models in a single workflow. The stack is built around:
- Reconfigurable Dataflow Unit (RDU) chips with a three-tier memory architecture that keeps “models and prompts” hot
- SambaStack for model bundling and switching between multiple models on one node
- SambaOrchestrator as the control plane: Auto Scaling | Load Balancing | Monitoring | Model Management
- SambaCloud exposing all of this behind OpenAI-compatible APIs, so you can port existing apps in minutes
Vertex AI is Google Cloud’s general ML platform. It uses GPU/TPU-based infrastructure, with:
- Managed endpoints per model or model family
- Horizontal scaling of endpoints (often one dominant model per pool)
- Routing implemented via model selection in your app, Vertex AI Pipelines, or custom serving layers

In a multi-model setting, you care about how quickly you can switch models, how many can remain hot simultaneously, and how much this costs in tokens, watts, and operational complexity.

SambaNova multi-model flow

Model onboarding & bundling:
You bring your own checkpoints or choose from supported open models (DeepSeek, Llama, gpt-oss) on SambaCloud. The RDU-based SambaStack is designed for model bundling, so multiple frontier-scale models can reside and be switched between efficiently on a single node.
Routing & agent workflows:
Your application uses OpenAI-compatible APIs to route calls—e.g., Llama for general tasks, DeepSeek-R1 for reasoning, gpt-oss-120b for high-quality generation—without needing separate infra per model. Because the RDU has a three-tier memory architecture, prompts and models stay hot, keeping token latency low across switches.
Production operations across data centers:
SambaOrchestrator manages inference across racks and regions: Auto Scaling | Load Balancing | Monitoring | Model Management | Cloud Create | Server Management. You scale throughput or bring more models into the bundle without rewriting your serving layer.

Vertex AI multi-model flow

Model deployment:
You pick from Google’s hosted models or deploy custom ones (often one primary model per endpoint). Each endpoint generally maps to a particular model version, backed by GPU/TPU nodes.
Routing & workflows:
Routing is done either in your app (choosing which endpoint to hit) or via separate orchestration (Pipelines, custom gateways, or routers). Each model swap can mean a change in endpoint—and under load you’re scaling and paying for multiple GPU pools.
Production operations:
Vertex AI provides autoscaling, monitoring, and model versioning, but endpoints are still largely model-centric. For agentic use cases that hit several large models in a loop, you end up juggling multiple endpoints and pools, each tuned separately.

Features & Benefits Breakdown

The table below maps features specifically to serving multiple open models with routing and fast switching.

Core Feature	What It Does	Primary Benefit
Model Bundling on RDUs (SambaNova)	Runs multiple frontier-scale models on a single RDU node with a three-tier memory architecture that keeps models and prompts hot.	Fast switching between models in agent workflows, higher tokens-per-watt, and less idle capacity than one-model-per-node GPU setups.
OpenAI-Compatible APIs (SambaCloud)	Exposes DeepSeek, Llama, gpt-oss and your checkpoints via OpenAI-compatible endpoints.	Port apps “in minutes” without rewriting clients, making it easy to experiment with and route across multiple open models.
SambaOrchestrator Control Plane	Provides Auto Scaling \| Load Balancing \| Monitoring \| Model Management across racks/data centers for inference.	Unified management of multi-model workloads; scale complex agentic inference without stitching together ad-hoc routers and dashboards.
Vertex AI Managed Endpoints	Hosts models behind Google-managed endpoints on GPUs/TPUs with autoscaling.	Simplifies single-model serving, especially if you’re already deeply invested in GCP and its IAM/networking stack.
Vertex AI Model Garden & Pipelines	Catalog of models plus workflow/pipeline tooling for MLOps.	Good for heterogeneous ML (not just LLMs) and pipeline-style workflows, especially for teams invested in Google’s MLOps ecosystem.
Sovereign / Private Inference Options (SambaRack)	Offers SambaRack SN40L-16 and SambaRack SN50 for on-prem or sovereign deployments with the same inference stack.	Run multi-model workloads where data residency, compliance, or low-latency on-prem inference are required, without losing routing or switching capabilities.

Ideal Use Cases

Best for multi-model agentic inference at high throughput: SambaNova
Because SambaStack is designed for model bundling and infrastructure flexibility, letting complex agentic AI workflows execute end-to-end on one node. The RDU’s three-tier memory architecture keeps several large models available for fast switching, and measured throughput on open models (e.g., gpt-oss-120b at over 600 tokens per second, DeepSeek-R1 up to 200 tokens / second as independently measured) supports production workloads where tokens-per-watt and latency matter.
Best for broad GCP-native ML ecosystems: Vertex AI
Because it integrates tightly with the rest of Google Cloud—BigQuery, Dataflow, GKE, and IAM—making it a natural fit if your organization is already standardized on GCP for non-LLM ML workloads and batch training.

Limitations & Considerations

SambaNova – Cloud ecosystem breadth:
SambaNova is a purpose-built inference stack, not a general-purpose cloud. If you need a single vendor for storage, networking, analytics, and generic compute, SambaNova will typically be part of a multi-cloud or hybrid pattern, not your only platform. The tradeoff is a stack optimized specifically for agentic, multi-model inference.
Vertex AI – One-model-per-node pressure:
GPU- and TPU-centric infrastructures tend to fall back to “one model per node” or per pool once you scale. For routing-heavy, multi-model workloads, that means maintaining and paying for multiple endpoint fleets, more data movement between them, and higher operational complexity. Fast switching across several frontier-scale models in a single node’s memory isn’t what the architecture is optimized for.

Pricing & Plans

SambaNova and Vertex AI structure pricing differently, but the key dimension for this use case is cost per token for multi-model workloads, not just list price per unit.

SambaNova focuses on inference efficiency:

RDU-based systems are engineered for high tokens-per-watt and low power usage (e.g., SambaRack SN40L-16 optimized for low-power inference with an average of 10 kWh), delivering 3X the savings compared to competitive chips for agentic inference in SambaNova’s positioning.
SambaCloud offers token-based inference-as-a-service up through dedicated racks for private/sovereign inference, giving you a path from experimentation to high-throughput production without rewriting the stack.

Vertex AI typically uses usage-based pricing per model endpoint and per token/generated unit, plus underlying compute/storage. For multi-model workloads, this often becomes:

Multiple high-memory GPU or TPU endpoint fleets
Separate scaling patterns and idle capacity per model
Additional networking and orchestration costs if you implement your own router

Think in terms of:

SambaCloud “pay-per-token” / managed inference: Best for teams that want fast time-to-value with open models (DeepSeek, Llama, gpt-oss) and are willing to treat inference as a specialized utility they can plug into existing infra.
SambaRack SN40L-16 / SN50: Best for infra buyers needing rack-level control, low power per rack, and sovereign/private deployment while retaining SambaCloud’s programming model via SambaOrchestrator.
Vertex AI pay-per-endpoint + tokens: Best for GCP-first organizations that are okay with running multiple model-specific endpoints and optimizing them individually.
SambaCloud / SambaRack: Best for teams needing high-throughput multi-model inference, with tight cost control and options for sovereign or on-prem deployment.
Vertex AI Managed Endpoints: Best for GCP-native teams who prioritize ecosystem integration over maximizing tokens-per-watt for complex agent loops.

Frequently Asked Questions

Can I route between multiple open models with SambaNova as easily as with Vertex AI?

Short Answer: Yes—and if your app already speaks OpenAI APIs, it’s typically easier and more efficient on SambaNova for multi-model agentic workloads.

Details:
SambaNova exposes DeepSeek, Llama, and gpt-oss models via OpenAI-compatible APIs on SambaCloud. That means most applications can:

Swap the base URL and API key
Keep their existing routing logic (e.g., choosing models based on tenant, use case, or experiment)
Immediately benefit from the RDU’s throughput and multi-model switching

Because SambaStack supports model bundling and a three-tier memory architecture, you’re not just routing across endpoints—you’re routing across models that can remain hot on a single node. With Vertex AI, routing typically means choosing among model-specific endpoints, each backed by separate GPU/TPU capacity. That works, but under agentic load it amplifies your idle capacity and per-model tuning overhead.

What if I need to bring my own checkpoints, not just use hosted open models?

Short Answer: Both platforms let you bring your own models; SambaNova is optimized to run those checkpoints efficiently alongside other frontier-scale models on the same stack.

Details:
SambaNova supports Inference | Bring Your Own Checkpoints on the same RDU-based infrastructure that powers SambaCloud’s open models. Combined with SambaOrchestrator, you get:

A consistent control plane (Auto Scaling | Load Balancing | Monitoring | Model Management)
The ability to run your internal models next to DeepSeek, Llama, or gpt-oss
The same OpenAI-compatible programming model for both

On Vertex AI, you can upload and serve custom models, but they’ll live as separate endpoints and often separate GPU/TPU pools. Routing between your internal checkpoints and Google-hosted or other open models then becomes another layer of endpoint management.

If your roadmap includes mixing internal and open models in the same agent workflows, SambaNova’s chips-to-model design and model bundling materially reduce the operational friction.

Summary

For workloads centered on serving multiple open models with routing and fast switching, SambaNova and Vertex AI are not equivalent choices.

Vertex AI is a solid, general-purpose managed ML platform optimized for GCP-native environments and model-centric endpoints.
SambaNova is a full-stack inference system—RDU chips, SambaRack hardware, SambaStack software, SambaOrchestrator control plane, and SambaCloud APIs—engineered specifically to break the “Not One-Model-Per-Node” pattern that constrains modern agentic AI.

If your success metric is how many routed, multi-model agent loops you can run per watt, per rack, and per dollar, SambaNova’s custom dataflow technology, three-tier memory, and model bundling give it a structural advantage. You get frontier-scale open models like DeepSeek, Llama, and gpt-oss running at measured high throughput, accessible over OpenAI-compatible APIs, ready to plug into your existing applications.

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