When you want to use open-source models (Llama, Mistral, Qwen, etc.) via API — without managing your own infrastructure — Together AI and Replicate are the leading options. Here’s how to choose.
The Problem They Solve
Running Llama 3.1 70B or Qwen 2.5 Coder yourself requires:
- Renting GPU servers (~$2-5/hour for A100)
- Setting up inference infrastructure (TGI, vLLM, etc.)
- Managing scaling, availability, and monitoring
Together AI and Replicate handle all of this, letting you call open-source models via a simple API without infrastructure management.
Together AI
Together AI specializes in efficient LLM inference, particularly for large open-source language models.
Model Selection
Llama 3.1 (8B, 70B, 405B), Mistral, Qwen, Code Llama, DeepSeek, and dozens more. Together is particularly strong on recent, popular open-source LLMs.
Speed
Together’s inference optimization (using their own inference stack and specialized hardware) is consistently among the fastest for LLM inference. Throughput and latency are competitive with hosted services.
Pricing
| Model | Together AI | Notes |
|---|---|---|
| Llama 3.1 8B | $0.18/M tokens | Cheap for medium quality |
| Llama 3.1 70B | $0.88/M tokens | Strong mid-tier |
| Llama 3.1 405B | $3.50/M tokens | Frontier-approaching |
| Qwen 2.5 72B | $0.90/M tokens | Excellent for coding |
Pricing is competitive, especially for smaller models.
OpenAI-Compatible API
Together AI’s API is OpenAI-compatible — you can swap the base URL and model name in your existing OpenAI code and it works. This reduces migration friction significantly.
Fine-tuning
Together AI supports fine-tuning on their infrastructure. Train a custom model and deploy it via their API.
Replicate
Replicate covers a much broader range of AI models — not just LLMs but image generation, audio models, video models, and specialized ML models.
Breadth of Models
Replicate hosts thousands of models submitted by the community and official developers. Beyond Llama, it includes:
- Stable Diffusion and SDXL variants
- Whisper (audio transcription)
- MusicGen (music generation)
- Specialized models for image segmentation, pose estimation, etc.
For developers building multi-modal applications, Replicate is often the single API covering all model types.
Model Versioning
Replicate versions models explicitly — you can pin to a specific model version for reproducibility. Important for production applications.
Community Models
The Replicate community has published custom fine-tuned models, specialized tools, and experimental models. This long tail of community models isn’t available anywhere else.
Pricing
Replicate uses “cost per second” pricing based on hardware used. This varies significantly by model and is harder to predict than token-based pricing. For LLMs, Together AI is typically cheaper. For image models, Replicate is often more competitive.
API Design
Replicate’s API has a slightly different design (prediction IDs, polling) compared to the synchronous OpenAI-compatible format. This requires more integration work.
Comparison Table
| Aspect | Together AI | Replicate |
|---|---|---|
| LLM selection | ★★★★★ | ★★★★☆ |
| Image generation models | ★★★☆☆ | ★★★★★ |
| Audio/video models | ★★★☆☆ | ★★★★★ |
| LLM inference speed | ★★★★★ | ★★★★☆ |
| LLM pricing | ★★★★★ | ★★★☆☆ |
| OpenAI compatibility | ★★★★★ | ★★★☆☆ |
| Model breadth | ★★★★☆ | ★★★★★ |
| Community models | ★★★☆☆ | ★★★★★ |
Recommendations
Use Together AI for:
- High-throughput LLM applications
- Cost-sensitive text generation at scale
- Drop-in replacement for OpenAI API with open-source models
- Fine-tuning and deploying custom language models
Use Replicate for:
- Multi-modal applications (images + audio + LLMs)
- Accessing community-fine-tuned models
- Experimenting with diverse model types
- Image generation workflows
Use both (common in production): Together AI for the LLM inference layer; Replicate for image and audio model calls.
Alternative: Groq
For latency-critical applications, Groq offers ultra-fast inference (hundreds of tokens/second) on Llama and Mistral models using custom LPU hardware. If your application is bottlenecked by LLM response time, Groq is worth evaluating.