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DeepInfra vs SambaNova
SambaNova sells fast decode on large open models from its own chip. DeepInfra sells the lowest price it can across a much bigger open catalog.
By The Subconscious Team · Updated
DeepInfra vs SambaNova: key differences
SambaNova and DeepInfra both serve open weights, but they optimize for opposite ends. SambaNova designs its own Reconfigurable Dataflow Unit and sells fast decode on big models like MiniMax M2.7, DeepSeek, Gemma 4 31B and GPT-OSS 120B through SambaCloud. Its three-tier memory lets one system hot swap between models in milliseconds, and SambaNova reports a SambaRack SN50 running MiniMax M2.7 near 820 tokens per second in its fastest configuration. DeepInfra makes no speed pitch in its profile. It sells 150+ models at or near the lowest per-token price, from $0.02 per million on Llama 3.1 8B.
Catalog and self-serve maturity favor DeepInfra. SambaNova's public list is smaller, much of its value arrives through rack sales to neoclouds and partnerships, and many headline figures, such as its claim of 5x the peak speed of an NVIDIA B200, are vendor benchmarks on hardware still ramping. DeepInfra's catch is quality control, since default quantization can trim context and output quality. Interactive coding agents on large models, where decode speed shapes the experience, suit SambaNova. Offline extraction and tagging, where nobody watches tokens stream, suit DeepInfra.
What DeepInfra and SambaNova do
DeepInfra
DeepInfra is the price floor for open-model inference. Developers treat it as the reference point for what a token should cost, with small models like Llama 3.1 8B at $0.02 per million and DeepSeek V4 Flash at $0.14 in and $0.28 out. The catalog covers 150+ open models across text, image and speech behind a fully OpenAI-compatible API. There are no minimums, setup fees or contracts on the shared API.
Example models: DeepSeek V4 Flash, Llama 3.1 8B
Full DeepInfra profileSambaNova
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.
Example models: MiniMax M2.7, GPT-OSS 120B
Full SambaNova profileShould you choose DeepInfra or SambaNova?
DeepInfra
Choose DeepInfra for
- Offline jobs where cost matters and speed does not
- Wide model choice on a self-serve API
- Budget backends for high-volume chat
SambaNova
Choose SambaNova for
- Interactive coding agents on large open models
- Agents that switch between several models per task
- Neoclouds adding a premium speed tier
DeepInfra vs SambaNova at a glance
| Attribute | DeepInfra | SambaNova |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | DeepSeek V4 Flash, Llama 3.1 8B | MiniMax M2.7, GPT-OSS 120B, DeepSeek |
| Speed | ~33 tok/s on DeepSeek V4 Pro (FP4) | ~820 tok/s on MiniMax M2.7 (SN50) |
| Price | From $0.02 per 1M | $0.22 in, $0.59 out (GPT-OSS 120B) |
| Customization | No managed fine-tuning | Unknown |
| Deployment | Shared API, no contracts | SambaCloud, racks for neoclouds |
| Long context | 66K on FP4 DeepSeek V4 Pro | Up to 192K (MiniMax M2.7) |
Frequently asked questions
What is the difference between DeepInfra and SambaNova?
SambaNova sells fast decode on large open models from its own chip. DeepInfra sells the lowest price it can across a much bigger open catalog.
When should I choose DeepInfra over SambaNova?
Offline jobs where cost matters and speed does not; Wide model choice on a self-serve API; Budget backends for high-volume chat.
When should I choose SambaNova over DeepInfra?
Interactive coding agents on large open models; Agents that switch between several models per task; Neoclouds adding a premium speed tier.
Is DeepInfra or SambaNova cheaper?
DeepInfra: From $0.02 per 1M. SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). The cheaper choice depends on the model and workload.
Which has more context, DeepInfra or SambaNova?
DeepInfra: 66K on FP4 DeepSeek V4 Pro. SambaNova: Up to 192K (MiniMax M2.7).
Related comparisons
Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.