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Novita AI vs Relace

Relace sells small tool models for coding agents, with a self-hosted option. Novita sells cheap general inference and GPUs. One is a utility, the other a platform.

By The Subconscious Team · Updated

Novita AI vs Relace: key differences

Relace trains small models that act as tools. relace-apply-3 merges edits at about 10,000 tokens per second with 128K tokens of input and output, its compaction model runs at 50,000 tokens per second, and its agentic search answers questions about big codebases in seconds. Relace has no general model serving. Novita is general serving across 200+ open models and several modalities, plus a GPU cloud. For a coding product, Relace handles apply and search while a Novita-hosted model does the reasoning.

Enterprise fit favors Relace in one specific way. Relace offers self-hosted deployment with guided onboarding for companies that keep code in-house. Novita has no public SOC 2, HIPAA or VPC peering, which rules it out for many enterprise buyers, so a security-sensitive team might pair Relace with a different main-model host. For app builders and indie tools on a budget, Novita plus Relace is a cheap stack. Relace's 128K cap means very large files need a fallback model.

What Novita AI and Relace do

Novita AI

Novita AI is a San Francisco inference cloud founded in late 2023 by Frank Lewis and Junyu Huang, and it competes on price and breadth. Its serverless API covers 200+ open models across LLMs, image, video, speech, voice cloning and embeddings, with LLM prices starting at $0.02 per million tokens. The API speaks both OpenAI and Anthropic formats. It became an official Hugging Face Inference Partner in April 2026 and was the day-zero launch partner for Google's Gemma 4.

Example models: DeepSeek V4 Pro, Gemma 4

Full Novita AI profile

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 profile

Should you choose Novita AI or Relace?

Novita AI

Choose Novita AI for

  • Budget reasoning model behind a coding tool
  • Fallback for files past 128K tokens
  • GPU rental for custom models

Relace

Choose Relace for

  • Instant apply in app builders
  • Fast agentic search across large repos
  • Self-hosted coding utilities for in-house code

Novita AI vs Relace at a glance

AttributeNovita AIRelace
Model accessOpen weightsSpecialist models
Flagship modelsDeepSeek V4 Pro, Gemma 4relace-apply-3, agentic search
Speed~36 tok/s on DeepSeek V4 Pro~10,000 tok/s apply
PriceFrom $0.02 per 1M; batch 50% off3x+ cheaper than full rewrites
CustomizationHot-swappable LoRA adaptersUnknown
DeploymentServerless, GPU cloud, dedicatedHosted API or self-hosted
Long contextFull 1M on DeepSeek V4 Pro128K max

Frequently asked questions

What is the difference between Novita AI and Relace?

Relace sells small tool models for coding agents, with a self-hosted option. Novita sells cheap general inference and GPUs. One is a utility, the other a platform.

When should I choose Novita AI over Relace?

Budget reasoning model behind a coding tool; Fallback for files past 128K tokens; GPU rental for custom models.

When should I choose Relace over Novita AI?

Instant apply in app builders; Fast agentic search across large repos; Self-hosted coding utilities for in-house code.

Is Novita AI or Relace cheaper?

Novita AI: From $0.02 per 1M; batch 50% off. Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.

Which has more context, Novita AI or Relace?

Novita AI: Full 1M on 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.