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DeepInfra vs Relace
Relace sells small, fast tool models for coding agents. DeepInfra sells general open-model inference. A coding stack can use both.
By The Subconscious Team · Updated
DeepInfra vs Relace: key differences
Relace is a point solution for coding workflows with no general-purpose model serving, so it only overlaps with DeepInfra inside a coding agent. Its Instant Apply model, relace-apply-3, merges a lazy edit snippet into the original file at about 10,000 tokens per second with 128K tokens of input and output, and Relace says this runs over 3x faster and cheaper than a big-model rewrite. It adds agentic search over large codebases, a compaction model at 50,000 tokens per second, and source control with retrieval built in. DeepInfra supplies the other part: 150+ open models that can do the planning and writing.
The split in a real pipeline looks like this. An open model on DeepInfra reads the task and drafts edits at floor prices, and Relace handles the fast utility steps of applying, searching and compacting. Deployment options differ. Relace offers self-hosting with guided onboarding for enterprises that keep code in-house, while DeepInfra is a shared API with no contracts. Mind the limits on each side. Relace returns an error past 128K tokens, so very large files need a fallback, and DeepInfra's quantized endpoints can carry short context caps, like 66K on FP4 DeepSeek V4 Pro.
What DeepInfra and Relace 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 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 DeepInfra or Relace?
DeepInfra
Choose DeepInfra for
- The general model that plans and drafts code changes
- Non-coding workloads that Relace does not serve
- Low per-token cost on the reasoning step
Relace
Choose Relace for
- App builders applying AI edits to user codebases
- PR review and CI jobs that search large repos
- Enterprises that want coding tool models self-hosted
DeepInfra vs Relace at a glance
| Attribute | DeepInfra | Relace |
|---|---|---|
| Model access | Open weights | Specialist models |
| Flagship models | DeepSeek V4 Flash, Llama 3.1 8B | relace-apply-3, agentic search |
| Speed | ~33 tok/s on DeepSeek V4 Pro (FP4) | ~10,000 tok/s apply |
| Price | From $0.02 per 1M | 3x+ cheaper than full rewrites |
| Customization | No managed fine-tuning | Unknown |
| Deployment | Shared API, no contracts | Hosted API or self-hosted |
| Long context | 66K on FP4 DeepSeek V4 Pro | 128K max |
Frequently asked questions
What is the difference between DeepInfra and Relace?
Relace sells small, fast tool models for coding agents. DeepInfra sells general open-model inference. A coding stack can use both.
When should I choose DeepInfra over Relace?
The general model that plans and drafts code changes; Non-coding workloads that Relace does not serve; Low per-token cost on the reasoning step.
When should I choose Relace over DeepInfra?
App builders applying AI edits to user codebases; PR review and CI jobs that search large repos; Enterprises that want coding tool models self-hosted.
Is DeepInfra or Relace cheaper?
DeepInfra: From $0.02 per 1M. Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
Which has more context, DeepInfra or Relace?
DeepInfra: 66K on FP4 DeepSeek V4 Pro. 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.