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Modal vs SambaNova
Modal is general serverless GPU compute for any model you bring. SambaNova serves a set of large open models fast on its own dataflow chip. Flexibility versus decode speed.
By The Subconscious Team · Updated
Modal vs SambaNova: key differences
Modal and SambaNova sit at different layers. Modal is a Python platform for serverless GPUs, from T4 to B300, up to 8 per container, billed per second at list rates like $3.95 an hour for an H100. It runs whatever you bring: LLMs on vLLM, Whisper, OCR, embeddings or training code. SambaNova designs its own chip, the RDU, and serves a curated list of large open models, including MiniMax M2.7, DeepSeek and GPT-OSS 120B, pitching fast decode and millisecond hot swapping between models.
Pick SambaNova when an interactive coding agent on a big open model needs tokens fast and the model is on its list. Pick Modal when the model is yours, or when the job is not an LLM at all. Modal's per-second billing favors spiky work, but cold starts from loading weights and a 3.75x markup for non-preemptible US production add up. SambaNova's catalog is smaller than GPU clouds and several headline numbers are its own benchmarks on hardware still ramping.
What Modal and SambaNova do
Modal
Modal is serverless compute with GPUs attached. A developer decorates a Python function with the hardware it needs, such as gpu="H100", and Modal builds the container, schedules it, autoscales it and scales it back to zero. Billing runs per second with no minimum increment, from $0.59 an hour for a T4 to $3.95 for an H100 at list. Containers can hold up to 8 GPUs across T4 through B300.
Example models: none hosted by default; teams deploy their own, such as open LLMs on vLLM or Whisper
Full Modal 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 Modal or SambaNova?
Modal
Choose Modal for
- Custom and fine-tuned models on per-second GPUs.
- Non-LLM jobs like OCR, transcription and embeddings.
- Inference, training and sandboxes on one platform.
SambaNova
Choose SambaNova for
- Fast decode on large open LLMs.
- Agents that hot swap between models.
- Interactive copilots on big models.
Modal vs SambaNova at a glance
| Attribute | Modal | SambaNova |
|---|---|---|
| Model access | Bring your own weights | Open weights |
| Flagship models | None hosted | MiniMax M2.7, GPT-OSS 120B, DeepSeek |
| Speed | ~1s container boot | ~820 tok/s on MiniMax M2.7 (SN50) |
| Price | Per second; H100 $3.95/hr list | $0.22 in, $0.59 out (GPT-OSS 120B) |
| Customization | Run any training code | Unknown |
| Deployment | Serverless GPU containers | SambaCloud, racks for neoclouds |
| Long context | Depends on the model you deploy | Up to 192K (MiniMax M2.7) |
Frequently asked questions
What is the difference between Modal and SambaNova?
Modal is general serverless GPU compute for any model you bring. SambaNova serves a set of large open models fast on its own dataflow chip. Flexibility versus decode speed.
When should I choose Modal over SambaNova?
Custom and fine-tuned models on per-second GPUs; Non-LLM jobs like OCR, transcription and embeddings; Inference, training and sandboxes on one platform.
When should I choose SambaNova over Modal?
Fast decode on large open LLMs; Agents that hot swap between models; Interactive copilots on big models.
Is Modal or SambaNova cheaper?
Modal: Per second; H100 $3.95/hr list. SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). The cheaper choice depends on the model and workload.
Which has more context, Modal or SambaNova?
Modal: Depends on the model you deploy. 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.