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Together AI vs Relace
Relace sells small tool models for coding agents: apply, search and compaction. Together hosts the general models those agents run on. They work as layers.
By The Subconscious Team · Updated
Together AI vs Relace: key differences
Relace does not serve general-purpose models, so it does not compete with Together for the main inference budget. Its relace-apply-3 merges lazy edit snippets at about 10,000 tokens per second with 128K tokens of input and output, and Relace says this is over 3x faster and cheaper than having the big model rewrite the file. It adds parallel agentic search over large codebases, a compaction model at 50,000 tokens per second and source control with retrieval built in. Together provides the large open models, such as DeepSeek V4, Kimi K3 or Qwen 3.8, that do the reasoning and write the edits.
A coding product could run its main model on Together and call Relace for apply, search and compaction, which keeps utility work off the expensive model. Relace returns an error past 128K tokens, so very large files need a fallback, and Together's larger models can serve that role. Relace also offers self-hosted deployment for companies keeping code in-house, while Together offers fine-tuning to specialize the main model. The question is less which one to pick than where each sits in the pipeline.
What Together AI and Relace do
Together AI
Together AI is the broadest open-model platform in the category. One bill covers per-token serverless inference, batch at up to 50% off, provisioned throughput with a 99% SLA, dedicated deployments, raw GPU clusters, managed fine-tuning and code sandboxes for agents. The text catalog runs past thirty open models, including DeepSeek V4, Kimi K3, GLM 5.2, Qwen 3.8 and MiniMax M3, plus image, video, speech and embedding models. Token prices sit at parity with Fireworks and Baseten.
Example models: Kimi K3, DeepSeek V4 Pro
Full Together AI 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 Together AI or Relace?
Together AI
Choose Together AI for
- The reasoning model that plans and writes code changes
- Fine-tuning a main model on internal code
- Fallback model for files past 128K tokens
Relace
Choose Relace for
- Instant apply of AI edits inside app builders
- Parallel search across large repositories for PR review
- Self-hosted coding utilities for code kept in-house
Together AI vs Relace at a glance
| Attribute | Together AI | Relace |
|---|---|---|
| Model access | Open weights | Specialist models |
| Flagship models | Kimi K3, DeepSeek V4, GLM 5.2, Qwen 3.8 | relace-apply-3, agentic search |
| Speed | 0.99s TTFT on DeepSeek V4 Pro | ~10,000 tok/s apply |
| Price | Parity with Fireworks and Baseten | 3x+ cheaper than full rewrites |
| Customization | LoRA and full SFT; RL in beta | Unknown |
| Deployment | Serverless, dedicated, GPU clusters | Hosted API or self-hosted |
| Long context | 512K on DeepSeek V4 Pro | 128K max |
Frequently asked questions
What is the difference between Together AI and Relace?
Relace sells small tool models for coding agents: apply, search and compaction. Together hosts the general models those agents run on. They work as layers.
When should I choose Together AI over Relace?
The reasoning model that plans and writes code changes; Fine-tuning a main model on internal code; Fallback model for files past 128K tokens.
When should I choose Relace over Together AI?
Instant apply of AI edits inside app builders; Parallel search across large repositories for PR review; Self-hosted coding utilities for code kept in-house.
Is Together AI or Relace cheaper?
Together AI: Parity with Fireworks and Baseten. Relace: 3x+ cheaper than full rewrites. The cheaper choice depends on the model and workload.
Which has more context, Together AI or Relace?
Together AI: 512K on DeepSeek V4 Pro. Relace: 128K max.
Related comparisons
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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.