# SambaNova vs Venice

> SambaNova sells fast decode on large open models from its own RDU chip. Venice sells private, zero-retention access to 370+ models. Speed against privacy frames this one.

Canonical: https://www.subconscious.dev/compare/sambanova-vs-venice · By The Subconscious Team · Updated September 30, 2026

## How they compare

SambaNova competes on hardware. Its Reconfigurable Dataflow Unit serves MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B through SambaCloud, and SambaNova claims its SN50 rack runs MiniMax M2.7 near 820 tokens per second in its fastest configuration. GPT-OSS 120B lists at $0.22 in and $0.59 out. Venice publishes no speed figures. It competes on what happens to your prompts: open models like GLM 5.3, Kimi K3 and DeepSeek V4 run under contract-enforced zero data retention, and some add TEE inference or end-to-end encryption. Venice's catalog is far wider, with 370+ models across text, image, audio and video, where SambaNova's public list is small.

Context and payment also split them. Venice offers 1M context on most current models, while SambaNova tops out around 192K on MiniMax M2.7. Venice takes USD, crypto, per-request USDC over x402, or daily credits from staked DIEM, which ties budget to the VVV token. SambaNova sells straightforward per-token cloud access plus racks to neoclouds that want a fast tier. Neither offers hosted fine-tuning. For an interactive coding agent where each step's latency matters, SambaNova is the stronger fit. For apps handling sensitive prompts, uncensored models or multimodal generation under one key, Venice covers more ground.

## What each one does

### SambaNova

SambaNova designs its own inference chip, the Reconfigurable Dataflow Unit, and sells fast tokens on large open models through SambaCloud. The RDU maps the model graph onto the chip to cut trips to off-chip memory. A three-tier memory design of SRAM, HBM and bulk DRAM lets one system host very large models and hot swap between several of them in milliseconds. SambaCloud serves models like MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B, with speeds reported by Artificial Analysis.

### Venice

Venice is a privacy-focused AI platform founded in 2024 by Erik Voorhees, the crypto entrepreneur behind ShapeShift. It pairs a consumer chat app with a developer API that works as a drop-in replacement for OpenAI's chat endpoint and covers text, image, audio and video across 370+ models. Open models such as GLM 5.3, Kimi K3, DeepSeek V4 and Venice's own uncensored fine-tunes run under a private tier with contract-enforced zero data retention, and some add TEE inference or end-to-end encryption, where only an attested enclave can decrypt the prompt. Closed models from Anthropic, OpenAI and Google are proxied under an anonymized tier that hides user identity but leaves prompt content visible to the upstream provider.

## Which is best, and when

### Choose SambaNova for

- Fast decode on big open models for coding agents
- Agents that hot swap between several large models
- Neoclouds adding a premium speed tier

### Choose Venice for

- Zero-retention inference on sensitive prompts
- 1M context on current open models
- Crypto-native billing or staked daily credits

## At a glance

| Attribute | SambaNova | Venice |
|---|---|---|
| Model access | Open weights | Open weights, plus proxied closed models |
| Flagship models | MiniMax M2.7, GPT-OSS 120B, DeepSeek | GLM 5.3, Kimi K3, DeepSeek V4 Pro |
| Speed | ~820 tok/s on MiniMax M2.7 (SN50) | - |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking |
| Customization | - | - |
| Deployment | SambaCloud, racks for neoclouds | Serverless API, consumer app |
| Long context | Up to 192K (MiniMax M2.7) | 1M on most current models |

## FAQ

### What is the difference between SambaNova and Venice?

SambaNova sells fast decode on large open models from its own RDU chip. Venice sells private, zero-retention access to 370+ models. Speed against privacy frames this one.

### When should I choose SambaNova over Venice?

Fast decode on big open models for coding agents; Agents that hot swap between several large models; Neoclouds adding a premium speed tier.

### When should I choose Venice over SambaNova?

Zero-retention inference on sensitive prompts; 1M context on current open models; Crypto-native billing or staked daily credits.

### Is SambaNova or Venice cheaper?

SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). Venice: $0.06–$12 in, $0.28–$60 out per 1M; DIEM staking. The cheaper choice depends on the model and workload.

### Which has more context, SambaNova or Venice?

SambaNova: Up to 192K (MiniMax M2.7). Venice: 1M on most current models.

## Running long-horizon agents?

If your agents run past 200K tokens, compare both against Subconscious: [Subconscious vs SambaNova](https://www.subconscious.dev/compare/subconscious-vs-sambanova.md), [Subconscious vs Venice](https://www.subconscious.dev/compare/subconscious-vs-venice.md).

Full profiles: [SambaNova](https://www.subconscious.dev/providers/sambanova.md), [Venice](https://www.subconscious.dev/providers/venice.md).