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Relace vs RunInfra
RunInfra offers cheap coding plans on mid-size open models and auto-built endpoints. Relace offers fast apply, search and compaction models. One supplies the agent's model, the other its tools.
By The Subconscious Team · Updated
Relace vs RunInfra: key differences
RunInfra and Relace both target developers building with coding agents, from different sides. RunInfra supplies the main model: a small library including Nemotron 3.5 Lightning 30B and Qwen 3.8 27B, on coding plans from $10 a month that plug into Claude Code, Codex, OpenCode, Cline and Aider. It also has an agent that benchmarks models across GPUs, quantizes them and ships scale-to-zero endpoints. Relace supplies tools the main model calls: relace-apply-3 for merges at about 10,000 tokens per second, agentic search and a compaction model.
Pairing them makes sense for a lean team. A mid-size open model on RunInfra writes edit snippets cheaply, and Relace applies them without a full-file rewrite, which Relace says is over 3x faster and cheaper. RunInfra's models are far from frontier quality, and the company has little independent benchmarking. Relace errors past 128K tokens and does no general model serving. For self-hosting, Relace offers guided onboarding, while RunInfra accepts custom uploads up to 50 GB on paid plans.
What Relace and RunInfra do
Relace
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 profileRunInfra
RunInfra pitches open models built for agents, with two ways in. Its hosted Model APIs serve a small curated library, including Nemotron 3.5 Lightning 30B, Qwen 3.8 27B and Ornith 1.5 35B, behind one key that works with both the OpenAI and Anthropic SDKs. Cached context bills at a discount. Coding plans start at $10 a month with limits that reset every five hours and every week, and they plug into Claude Code, Codex, OpenCode, Cline, Aider and dozens of other agent CLIs.
Example models: Nemotron 3.5 Lightning 30B, Qwen 3.8 27B
Full RunInfra profileShould you choose Relace or RunInfra?
Relace vs RunInfra at a glance
| Attribute | Relace | RunInfra |
|---|---|---|
| Model access | Specialist models | Open weights |
| Flagship models | relace-apply-3, agentic search | Nemotron 3.5 Lightning 30B, Qwen 3.8 27B |
| Speed | ~10,000 tok/s apply | Cold starts under 2s |
| Price | 3x+ cheaper than full rewrites | Coding plans from $10 a month |
| Customization | Unknown | Uploads up to 50 GB; auto-quantization |
| Deployment | Hosted API or self-hosted | Model APIs, agent-built endpoints |
| Long context | 128K max | Varies by model |
Frequently asked questions
What is the difference between Relace and RunInfra?
RunInfra offers cheap coding plans on mid-size open models and auto-built endpoints. Relace offers fast apply, search and compaction models. One supplies the agent's model, the other its tools.
When should I choose Relace over RunInfra?
Applying edits and searching repos for any agent; Cutting the main model's output tokens; Enterprises self-hosting coding tools.
When should I choose RunInfra over Relace?
A cheap main model inside agent CLIs; Auto-quantized endpoints without ML ops staff; Voice pipelines alongside coding work.
Is Relace or RunInfra cheaper?
Relace: 3x+ cheaper than full rewrites. RunInfra: Coding plans from $10 a month. The cheaper choice depends on the model and workload.
Which has more context, Relace or RunInfra?
Relace: 128K max. RunInfra: Varies by model.
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.