By Together AILearning Objectives
- Understand what Together AI offers and how it differs from proprietary AI providers
- Compare Together AI's pricing and model catalog to alternatives like Groq and AWS Bedrock
- Evaluate Together AI's research contributions to the open-source AI ecosystem
What Is Together AI?
Together AI (formally Together Computer, Inc.) is a full-stack AI cloud platform that lets developers run, fine-tune, and train open-source AI models. With over 200 models available via a serverless API — including Llama 4, DeepSeek R1, Mixtral, and Qwen — Together AI positions itself as "The AI Native Cloud" for teams that want the power of open-source AI without managing their own infrastructure.
Unlike proprietary providers like OpenAI or Anthropic, Together AI hosts open-source models at dramatically lower prices. Running Llama 4 Maverick on Together AI costs roughly $0.27 per million input tokens — compared to $15 per million for Claude Opus or $2.50 per million for GPT-5.5.
✅Tip
Try Together AI: together.ai — new accounts receive free credits to experiment with inference, fine-tuning, and model hosting.
What Can You Do?
Serverless Inference
Run any of 200+ models via API with sub-100 millisecond latency. The API is OpenAI-compatible, so switching requires minimal code changes. Models span text generation, image generation, video, code, and audio.
Fine-Tuning
Customize models for your specific use case with LoRA (lightweight) or full fine-tuning. Upload your training data, choose a base model, and Together AI handles the GPU infrastructure. Supports models up to 100 billion+ parameters.
Dedicated GPU Clusters
Rent NVIDIA H100, H200, or B200 GPU clusters for custom training jobs. Together AI has secured 200 megawatts of power capacity across North American data centers — enough for large-scale model training.
Batch Processing
Process up to 30 billion tokens asynchronously at reduced prices — ideal for data processing, evaluation, and offline workloads.
Pricing
Representative inference pricing:
| Model | Input (per 1 million tokens) | Output (per 1 million tokens) |
|---|---|---|
| Llama 4 Maverick | $0.27 | $0.85 |
| DeepSeek R1 | $0.55 | $2.19 |
| R1 Distill Llama 70 billion | $0.03 | ~$0.03 |
These prices are a fraction of proprietary alternatives — making Together AI popular with startups and teams building on open-source models.
Research Contributions
Together AI is not just an infrastructure provider — the team actively advances open-source AI research:
- FlashAttention-4 — up to 1.3 times faster than cuDNN on NVIDIA Blackwell GPUs; widely adopted across the industry
- RedPajama-V2 — a 30 trillion token open training dataset, the largest publicly available LLM training dataset
- Mamba-3 — a state-space model architecture that is faster than Transformers at decode time, open-sourced
- 50+ peer-reviewed papers with over 10,000 citations
The founding team includes Chris Re and Percy Liang from Stanford, whose research on efficient attention mechanisms and foundation model evaluation shaped the modern AI landscape.
Together AI vs. Competitors
| Platform | Strength | Models | Best For |
|---|---|---|---|
| Together AI | 200+ models; inference + fine-tuning + training | 200+ | Full-stack open-source AI development |
| Groq Cloud | Fastest single-stream latency (custom LPU chips) | Limited (~10) | Real-time chat and latency-sensitive apps |
| Fireworks AI | Fast multimodal inference; HIPAA/SOC2 | Moderate | Regulated industries needing speed |
| Replicate | Easy model deployment; pay-per-second | Large (community) | Quick prototyping and model experimentation |
| AWS Bedrock | Broadest enterprise ecosystem | Moderate (curated) | Enterprise teams already on AWS |
Together AI's niche: The broadest open-source model catalog combined with competitive pricing, fine-tuning, and dedicated GPU infrastructure. Groq wins on raw latency; Together AI wins on breadth and end-to-end platform capabilities.
Company Details
| Detail | Info |
|---|---|
| Founded | June 2022 |
| CEO | Vipul Ved Prakash (previously Director of Engineering at Apple) |
| Co-Founders | Vipul Ved Prakash; Ce Zhang; Chris Re; Percy Liang |
| Headquarters | San Francisco, California |
| Employees | ~313 |
| Latest Funding | $305 million Series B (February 2025) |
| Valuation | $3.3 billion |
| Total Raised | $534 million across 4 rounds |
| Key Investors | General Catalyst; Prosperity7; Salesforce Ventures; NVIDIA; Kleiner Perkins |
| Estimated Revenue | ~$300 million annualized (September 2025) |
| Acquisition | Refuel.ai (May 2025) for data transformation |
| Website | together.ai |
Strengths
- Broadest model catalog — 200+ open-source models across text, image, video, code, and audio in one platform
- Full-stack platform — inference, fine-tuning, and training in a single service (most competitors offer inference only)
- Competitive pricing — open-source models at a fraction of proprietary API costs
- Research leadership — FlashAttention, RedPajama, and Mamba contributions used across the entire AI industry
- GPU access — on-demand H100, H200, and B200 clusters with 200 megawatts of secured power capacity
- OpenAI-compatible API — easy migration from proprietary providers
Limitations and Considerations
- Open-source models only — you cannot access proprietary models like GPT-5.5 or Claude through Together AI
- Not the fastest — Groq and Fireworks AI both achieve lower latency on inference benchmarks
- Enterprise maturity — newer company (founded 2022) compared to established cloud providers like AWS or Azure
- Revenue estimates are unofficial — the $300 million ARR figure comes from third-party analysis, not company disclosures
- Fine-tuning complexity — while supported, fine-tuning large models still requires ML expertise to get good results
Key Takeaways
- Together AI is the leading full-stack cloud platform for open-source AI — offering inference, fine-tuning, and training for 200+ models at competitive prices
- Dramatically cheaper than proprietary AI providers: Llama 4 Maverick costs $0.27 per million input tokens versus $15 for Claude Opus
- Active research lab producing industry-standard tools (FlashAttention, RedPajama) used across the AI ecosystem
- Best suited for teams building on open-source models who need more than just inference — fine-tuning, training, and dedicated GPU infrastructure
