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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 hosts only a handful of open models. 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 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 profileRelace
Relace started out training small, fast models that act as tools for coding agents, and by October 2026 its homepage led with open-model inference instead. It says it served 30 trillion tokens in the 30 days to late October and ranks among the top providers on OpenRouter. The hosted catalog covers DeepSeek V4 Pro, V4 Flash and V4.1 Flash, Kimi K3, GLM 5.3, GLM 5.3 Flash and GLM 5.2 over OpenAI and Anthropic compatible APIs, billed per token with no subscription. Prices run from $0.07 in and $0.14 out per million tokens on DeepSeek V4 Flash to $2.00 in and $14.00 out on Kimi K3, and Relace says it reprices often.
Example models: DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3
Full Relace profileShould you choose Novita AI or Relace?
Novita AI vs Relace at a glance
| Attribute | Novita AI | Relace |
|---|---|---|
| Model access | Open weights | Open weights, plus specialist coding models |
| Flagship models | DeepSeek V4 Pro, Gemma 4 | DeepSeek V4.1 Flash, Kimi K3, GLM 5.3, relace-apply-3 |
| Speed | ~36 tok/s on DeepSeek V4 Pro | ~10,000 tok/s apply |
| Price | From $0.02 per 1M; batch 50% off | $0.07 in, $0.50 out (DeepSeek V4.1 Flash) |
| Customization | Hot-swappable LoRA adapters | Unknown |
| Deployment | Serverless, GPU cloud, dedicated | Hosted API, OpenRouter or self-hosted |
| Long context | Full 1M on DeepSeek V4 Pro | 128K on apply; varies by open model |
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: $0.07 in, $0.50 out (DeepSeek V4.1 Flash). 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 on apply; varies by open 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.