Together AI
Together AI provides API access to many open-source models (Llama, Mistral, Qwen, StableLM) with options for fine-tuning and private deployments. It is positioned as the open-source alternative to closed-model vendors, giving teams control over model selection and customization without managing infrastructure.
Official site ↗Why it ranks here
Together AI is a strong option when you need an open-source model with fine-tuning or when you want to avoid closed-model vendors. It is situational because most GTM tasks do not require fine-tuning, and open models trail closed ones on reasoning quality.
Where it sits
Together AI's model quality depends on the underlying open-source model. Fine-tuned models can match mid-tier closed models on narrow tasks but will not beat top-tier closed models on complex reasoning. Use Together AI when data residency, cost, or customization justify the quality trade-off.
How GTM Engineering teams use it
GTM engineers use Together AI to fine-tune models on proprietary data (train a model to classify industry-specific intent signals or generate outreach in a specific voice), deploy models in regions where OpenAI or Anthropic are unavailable, or cut costs on high-volume tasks where open models are good enough. Teams export training data from Clay or Salesforce, fine-tune via Together's API, and call the custom model in production.
In-depth notes
Pricing is per-token with lower rates than closed models. Fine-tuning costs vary by model size and dataset. Latency is comparable to OpenAI for popular models. Together AI's dashboard includes experiment tracking and evaluation tools. The service is smaller than AWS or OpenAI, so uptime and support are less mature. Together AI supports private deployments for teams with strict data residency requirements.
Best for
Fine-tuning on proprietary data, open-source model access, data residency requirements, cost optimization.
Avoid if
You need the best reasoning quality or do not have the resources to experiment with open models.
The rest of LLM APIs & intelligence
Claude (Anthropic) Leader Strong-reasoning LLM for research, qualification, drafting, and classification. #2 
OpenAI Leader Broad ecosystem of general-purpose LLMs available via API for GTM pipelines. #3 
Claygent Strong Clay's built-in AI research agent for web scraping, enrichment, and data extraction. #4 
Perplexity API Strong LLM with built-in search grounding for real-time research and fact-checking. #5 
OpenRouter Strong Model gateway providing unified API access to dozens of LLMs with automatic routing. #7 
Amazon Bedrock Strong AWS-managed service providing API access to Claude, Llama, Mistral, and other models. #8 
Groq Situational Ultra-fast inference API for Llama, Mistral, and Gemma with sub-second latency. #10 
Cohere Niche Enterprise LLM with strong embeddings, reranking, and classification APIs. #13 
Replicate Niche Model marketplace and hosting platform for open-source LLMs and AI models. Learning guide
Setup
Create a Together AI account, get an API key, prepare a training dataset (JSON lines with prompt-completion pairs), upload it via the API, and start a fine-tuning job. Your first build fine-tunes a Llama model to classify industry-specific intent signals that general models miss.
First thing to build
Build a custom intent classifier by exporting 500-1000 labeled examples from your CRM (industry tag, intent signal present or not), fine-tuning a Llama model on Together AI, and calling the custom model in production. Use fine-tuning when off-the-shelf models do not recognize domain-specific patterns.
What actually matters
- Prepare training data in JSON lines format with input (prompt) and output (completion) fields; quality matters more than quantity
- Monitor fine-tuning cost (varies by model size and dataset) and training time (hours to days for large models)
- Evaluate the fine-tuned model on a held-out test set before deploying; fine-tuning can degrade performance on unrelated tasks
- Use Together AI for data residency requirements or when you need private model deployments
Watch out
Fine-tuning requires significant experimentation to get right; start with a small model and dataset before scaling to production.