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SambaNova vs Relace
SambaNova serves the big open model a coding agent thinks with. Relace serves the small fast models it works with: apply, search and compaction. Complementary layers of one agent.
By The Subconscious Team · Updated
SambaNova vs Relace: key differences
Relace is a toolkit for coding agents, not a general host. relace-apply-3 merges lazy edit snippets at about 10,000 tokens per second with 128K tokens of input and output, its agentic search answers questions about large codebases in seconds, and a compaction model runs at 50,000 tokens per second. It also offers source control with retrieval built in. SambaNova serves the large general models that would drive such an agent, like MiniMax M2.7, DeepSeek and GPT-OSS 120B, on its own RDU chip.
Teams building a fast coding agent could use both: SambaCloud for the main model's decode, Relace for the utility calls that would otherwise burn frontier tokens. The limits sit in different places. Relace returns an error past 128K tokens, so very large files need a fallback, and it has no general-purpose serving. SambaNova's catalog is smaller than GPU clouds. Relace offers self-hosted deployment for code that has to stay in-house, while much of SambaNova's hardware goes to neoclouds as racks.
What SambaNova and Relace do
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.
Example models: MiniMax M2.7, GPT-OSS 120B
Full SambaNova profileRelace
Relace trains small, fast models that act as tools for coding agents. Its best-known product is Instant Apply: a frontier model writes a lazy edit snippet, and relace-apply-3 merges it into the original file at about 10,000 tokens per second with 128K tokens of input and output. Relace says this runs over 3x faster and cheaper than having the big model rewrite the file. It exposes both a REST endpoint and an OpenAI-compatible one, and the model is also listed on OpenRouter.
Example models: relace-apply-3, Relace agentic search
Full Relace profileShould you choose SambaNova or Relace?
SambaNova
Choose SambaNova for
- Fast decode for the agent's main open model.
- Hot swapping between several large models.
- Operators buying racks for a premium speed tier.
Relace
Choose Relace for
- Instant apply and repo search inside coding agents.
- Context compaction at 50,000 tokens per second.
- Self-hosted coding utilities for private code.
SambaNova vs Relace at a glance
| Attribute | SambaNova | Relace |
|---|---|---|
| Model access | Open weights | Specialist models |
| Flagship models | MiniMax M2.7, GPT-OSS 120B, DeepSeek | relace-apply-3, agentic search |
| Speed | ~820 tok/s on MiniMax M2.7 (SN50) | ~10,000 tok/s apply |
| Price | $0.22 in, $0.59 out (GPT-OSS 120B) | 3x+ cheaper than full rewrites |
| Customization | Unknown | Unknown |
| Deployment | SambaCloud, racks for neoclouds | Hosted API or self-hosted |
| Long context | Up to 192K (MiniMax M2.7) | 128K max |
Frequently asked questions
What is the difference between SambaNova and Relace?
SambaNova serves the big open model a coding agent thinks with. Relace serves the small fast models it works with: apply, search and compaction. Complementary layers of one agent.
When should I choose SambaNova over Relace?
Fast decode for the agent's main open model; Hot swapping between several large models; Operators buying racks for a premium speed tier.
When should I choose Relace over SambaNova?
Instant apply and repo search inside coding agents; Context compaction at 50,000 tokens per second; Self-hosted coding utilities for private code.
Is SambaNova or Relace cheaper?
SambaNova: $0.22 in, $0.59 out (GPT-OSS 120B). Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
Which has more context, SambaNova or Relace?
SambaNova: Up to 192K (MiniMax M2.7). Relace: 128K max.
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.