Resources // AI Infrastructure
Together AI Startup Accelerator
- High-value
- VC-only
Offer $50,000 credits
Suits for VC-backed
Updated Jun 2026
The offer
- Up to $50,000 in platform credits on Together AI Cloud, plus "forward-deployed engineering" hours
- Go-to-market and community support, plus access to Together's VC and partner network
- Three funding-based tiers:
- Build: up to $15K credits and 3 hrs engineering, < $5M raised
- Scale: up to $30K credits and 6 hrs engineering, $5-10M raised
- Grow: up to $50K credits and 10 hrs engineering, > $10M raised
- Note: credits do not apply to Reserved GPU Clusters
Who qualifies
- Selection-based, building AI-native apps (inference, fine-tuning, or model deployment on Together AI)
- Application asks for funding raised (tier is gated on capital raised)
- Provide company website, contact details, and team info
Community Insights
Together AI is seen as a high-quality, developer-friendly way to burst from local models to big cloud models and SDXL-class image generation. It is a managed token API rather than per-hour GPU rental, which small teams find easier to adopt. It is strong on breadth and fine-tuning and the cheapest of the major inference providers, but mid-pack on raw speed and reliability versus specialists (Groq for latency, Fireworks for production reliability). The main caution: credits burn fast under heavy image generation and experimentation, so the accelerator’s value depends on disciplined usage.
Best Practices (from community tips)
- Use credits as burst capacity. Keep routine inference local and cheap, and send only the big models (70B+, SDXL) to Together.
- Budget credits carefully. Image generation and repeated experiments drain a trial fast. Cache outputs and store prompts and seeds.
- Benchmark models early. Treat the accelerator as a model-evaluation playground before you architect your long-term stack.
- Stay provider-flexible. Abstract your LLM calls so you can move between Together, OpenRouter, and local models when credits run out.