AI Tools · Comparison
Replicate vs Together AI
Updated Jun 15, 2026 · 1 min read
ReplicateVSTogether AI
Quick Answer
Best overallDepends on workload
Best for experimentationReplicate
Best for production LLMsTogether AI
Best for custom modelsReplicate
Side-by-Side Comparison
How Replicate, Together AI compare across the features that matter most.
| Feature | Replicate | Together AI |
|---|---|---|
| Focus | Run any containerized model (Cog) | Serverless inference for LLMs |
| Model breadth | Thousands of community models (image/video/audio/text) | ~200 curated models on warm endpoints |
| Custom models | Push your own via Cog | Curated library + fine-tuning |
| Billing | Per GPU-second, scales to zero | Per-token + dedicated GPU endpoints |
| Cold starts | 10-60s on idle models | Warm endpoints, no cold starts |
| Latency profile | Variable (cold starts unless kept warm) | Consistent low latency |
| SLAs | Limited public SLAs | Enterprise SLA guarantees |
| Best for | Bursty, one-off, multimodal experiments | Sustained LLM traffic, production RAG/chat |
KLYROO Test
Our comparison philosophy is to evaluate tools with consistent tasks and realistic workflows across categories such as writing, coding, research, reasoning, image generation, summarization and data analysis. Where we have not run a controlled head-to-head, we label the assessment clearly: this comparison is an editorial evaluation based on documented features and available evidence. We never invent benchmark numbers.
Which One Should You Choose?
- Choose Replicate for breadth, custom Cog models and cost-effective bursty or multimodal jobs.
- Choose Together AI for steady, low-latency LLM traffic with predictable per-token costs and SLAs.
- A hybrid setup — Together for chat, Replicate for multimodal — is common.
Frequently Asked Questions
It depends on your use case. Overall we lean toward Depends on workload, but the best pick varies by need — see the recommendations above.