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

Most platform teams reach for Google Cloud Vertex AI by default, then hit a wall when they try to serve multiple open models with fast routing and “hot” switching for agentic workloads. The core question isn’t “which cloud?”—it’s whether your inference stack is built around one-model-per-node thinking, or around model bundling and fast multi-model switching.

Quick Answer: SambaNova is purpose-built for serving multiple open models with routing and fast switching on a single node, using its RDU-based “chips-to-model” stack and SambaStack model bundling. Vertex AI is a broad managed AI service that can host many models, but its GPU-centric architecture is not optimized for high-throughput, multi-model agentic inference where switching cost and tokens-per-watt dominate.

The Quick Overview

What It Is:
A comparison between SambaNova’s RDU-powered inference stack and Google Cloud Vertex AI for multi-model, open-source LLM serving with routing and rapid model switches.
Who It Is For:
Platform, ML, and infra teams responsible for production LLM serving—especially those running agentic workflows, retrieval chains, or multi-model routing across DeepSeek, Llama, gpt-oss, and other open models.
Core Problem Solved:
How to serve multiple open models at scale, route between them intelligently, and switch quickly—without exploding latency, power, and operational complexity.

How It Works

At a high level, both options let you deploy open models and call them via APIs, but they’re built on very different assumptions:

Vertex AI: GPU-centric, many-models-per-cloud, but typically one-primary-model-per-accelerator at high throughput. Multi-model routing is assembled via separate endpoints, load balancers, and custom logic in your application or Vertex AI Pipelines.
SambaNova: RDU-centric, “chips-to-model computing” with a three-tier memory architecture and SambaStack. Multiple frontier-scale models can be “bundled” and kept hot so an agentic workflow can switch between them on one node, coordinated via SambaOrchestrator and exposed through OpenAI-compatible APIs on SambaCloud.

From an operator’s point of view, the difference shows up in three places:

Hot multi-model serving:
Can you run DeepSeek, Llama, and gpt-oss on the same node and switch between them mid-workflow without a cold start or endpoint hop?
Routing & switching overhead:
Is model routing a first-class infrastructure capability, or something you stitch together using separate endpoints and orchestration glue?
Tokens per watt & scaling behavior:
Does your throughput scale within power and cooling limits, or do you keep adding GPU nodes and complexity to maintain latency under heavy multi-model load?

1. Multi-model agentic inference on SambaNova

SambaNova starts from the workload: complex agentic inference and multi-step LLM workflows that call multiple models in sequence or in parallel. The stack is built for that pattern:

Model bundling on RDUs:
SambaStack uses custom dataflow technology and a three-tier memory architecture on the SN50 RDU to keep several frontier-scale models and prompts “hot” on the same node. Instead of pinning one model per accelerator, the RDU is optimized for switching between multiple models without thrashing memory.
End-to-end workflows on one node:
With model bundling, complex agentic workflows (router → reasoning model → tool-calling model → summarizer) can execute on a single node. This minimizes cross-node communication and reduces the overhead of routing between models.
OpenAI-compatible APIs and orchestration:
SambaCloud exposes DeepSeek, Llama, and gpt-oss via OpenAI-compatible APIs. SambaOrchestrator handles Auto Scaling | Load Balancing | Monitoring | Model Management across SambaRack systems. You port your OpenAI-based app in minutes while gaining multi-model throughput on RDUs.

2. Multi-model serving on Google Cloud Vertex AI

Vertex AI is a general-purpose managed AI platform:

GPU/TPU-backed endpoints:
You deploy each model (e.g., Llama variants, custom checkpoints) to its own endpoint backed by GPUs or TPUs. High-throughput serving typically still assumes one primary model per accelerator configuration.
Routing at the application layer:
Model routing is assembled via:
- Separate endpoints per model
- Your own routing code (e.g., a small classifier model or rules engine)
- Vertex AI Pipelines / Workbench notebooks / custom services
Fast switching means hopping between endpoints and sometimes across zones or node pools, not switching models on a single accelerator.
Generic APIs, not one “chips-to-model” stack:
APIs are cloud-specific and span many services (Vertex AI models, Endpoints, Workbench, etc.). This is flexible, but not purpose-built for agentic inference or bundling multiple frontier models on one physical node.

Features & Benefits Breakdown

Core Feature	What It Does	Primary Benefit
Model Bundling on RDUs (SambaNova)	Keeps multiple frontier-scale models and prompts hot in a three-tier memory architecture so workflows can switch models on one node.	Ultra-fast multi-model switching with lower latency and fewer cross-node hops for agentic workflows.
OpenAI-Compatible Inference APIs (SambaNova)	Exposes DeepSeek, Llama, gpt-oss and BYO checkpoints via OpenAI-compatible APIs on SambaCloud.	Port existing OpenAI-based apps in minutes and immediately gain token throughput and efficiency.
SambaOrchestrator Control Plane (SambaNova)	Provides Auto Scaling	Load Balancing
General-Purpose Model Endpoints (Vertex AI)	Lets you deploy many models across GPUs/TPUs on Google Cloud with autoscaling and managed infrastructure.	Easy on-ramp if you are already standardized on GCP and need generic managed endpoints.
Cloud-Native Pipelines & Tools (Vertex AI)	Integrates with GCP networking, IAM, BigQuery, Dataflow, and Vertex AI Pipelines.	Strong integration story for data-centric workflows, especially if your stack is already GCP-heavy.
GPU-Centric Scaling (Vertex AI)	Scales model deployments across GPU/TPU pools using standard autoscaling patterns.	Straightforward scaling for single-model workloads; less optimized for tightly coupled multi-model agent loops.

Ideal Use Cases

Best for multi-model agentic inference with high switching frequency: SambaNova
Because it uses custom dataflow technology and a three-tier memory architecture on RDUs to keep multiple models and prompts hot, then switches between them on a single node. This directly reduces latency and power for complex workflows.
Best for teams already locked into GCP and generic ML ops: Vertex AI
Because it plugs into existing GCP IAM, networking, and data pipelines. If your workloads are mostly single-model or loosely coupled multi-step flows, generic GPU endpoints and Vertex Pipelines are often “good enough.”

Limitations & Considerations

SambaNova – Cloud & ecosystem scope:
SambaNova is focused on inference efficiency and agentic workloads, not on being a full general-purpose cloud. You still run your broader data and application stack elsewhere, integrating SambaCloud or SambaRack via APIs and standard networking.
Vertex AI – Multi-model agentic performance limits:
Vertex AI can serve many models, but its GPU-centric design and one-model-per-node operational patterns mean you typically route between endpoints instead of switching within a node. As workflows become more agentic (longer loops, more model hops), latency, cost per token, and energy use can climb.

Pricing & Plans

Specific pricing will vary by region, consumption model, and contract, but the patterns differ:

SambaNova:
Flexible consumption models—from token-based inference-as-a-service on SambaCloud through to dedicated SambaRack systems for fully private or sovereign inference. SN40L-16 is optimized for low power inference (average of 10 kWh), and SN50 is designed for fast agentic inference at a fraction of the cost for the largest models. Independent testing (e.g., Artificial Analysis) has shown:
- gpt-oss-120b at over 600 tokens per second
- DeepSeek-R1 at up to 200 tokens per second
  This tokens-per-watt posture is central if power/cooling are first-order constraints.
Vertex AI:
Usage-based pricing around:
- Per-model deployment and per-node accelerator cost (GPU/TPU)
- Per-token or per-character charges on managed models and endpoints
- Additional charges for networking, storage, and surrounding GCP services
  Effective cost for multi-model agentic workloads often reflects the need to overprovision GPU capacity to preserve latency across many endpoints.

Plan Fit

SambaCloud / SambaRack + SambaOrchestrator (SambaNova):
Best for teams needing high-throughput, multi-model inference with strict latency and energy budgets, and those planning to run frontier open models like Llama 3.1 (8B, 70B, 405B) and DeepSeek at scale.
Vertex AI Standard Deployments:
Best for teams already committed to Google Cloud who need straightforward managed LLM endpoints and are less sensitive to the efficiency impact of routing across multiple GPU-backed endpoints.

Frequently Asked Questions

Can SambaNova really replace Vertex AI for serving multiple open models?

Short Answer: Yes, if your priority is high-throughput, multi-model inference with fast switching for agentic workloads, SambaNova is designed for that job.

Details:
For teams whose bottlenecks are:

Multiple model calls per user interaction
Growing prompts and context windows
Increasing energy and cooling costs in the data center

SambaNova’s chips-to-model stack—SN50 RDUs, SambaRack systems, SambaStack, and SambaOrchestrator—directly targets those issues. Model bundling and tiered memory allow multiple frontier-scale models and prompts to stay hot and switch within a node. The OpenAI-compatible APIs mean the migration path from an existing OpenAI/Vertex-style app is minimal. Vertex AI remains useful for generic ML workloads and for teams fully standardized on GCP, but it doesn’t offer the same architectural commitment to multi-model agentic inference on one node.

How hard is it to move from a Vertex AI-style deployment to SambaNova?

Short Answer: Migration is typically straightforward because SambaCloud exposes OpenAI-compatible APIs and supports the same open models many teams already use.

Details:
Most Vertex AI deployments that are LLM-heavy follow a pattern: an application server calls OpenAI-style chat/completions endpoints on cloud-hosted models. To move those workloads to SambaNova:

Port the API calls:
Point your existing OpenAI-compatible client at SambaCloud’s endpoints. Model names change (e.g., targeting DeepSeek, Llama, or gpt-oss variants on SambaNova), but your client libraries and request structure generally don’t.
Bundle models for workflows:
Identify your routing and agent steps—router model, reasoning model, tools model, summarizer—and map them to the open models exposed through SambaCloud. SambaStack handles bundling them on RDUs.
Operationalize with SambaOrchestrator:
Where you previously depended on Vertex AI’s autoscaling and monitoring, SambaOrchestrator provides Auto Scaling | Load Balancing | Monitoring | Model Management across SambaRack deployments, in the cloud or on-prem.

For teams with strict data residency or sovereign AI requirements, SambaNova also supports fully private inference via dedicated racks, while maintaining the same inference stack and APIs.

Summary

For serving multiple open models with routing and fast switching, the decision comes down to architecture:

Vertex AI offers broad, cloud-native managed services centered on GPUs and TPUs. It’s a good fit for generic LLM endpoints and teams deeply invested in GCP. Multi-model routing is achievable but assembled from multiple endpoints and orchestration layers, with switching overhead that grows as your agentic workflows get more complex.
SambaNova is purpose-built for agentic, multi-model inference. Its SN50 RDU, three-tier memory architecture, and SambaStack model bundling enable complex workflows to run end-to-end on one node. SambaOrchestrator delivers the control plane for Auto Scaling | Load Balancing | Monitoring | Model Management, while SambaCloud provides OpenAI-compatible access to DeepSeek, Llama, gpt-oss, and more—often at significantly higher tokens-per-second and tokens-per-watt.

If your roadmap leans into agents, multi-model routing, and sovereign inference—with power and cooling as first-order constraints—SambaNova’s chips-to-model computing is the better long-term foundation.

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