Deno Deploy
Together AI
| Feature | Deno Deploy | Together AI |
|---|---|---|
| Pricing | Free / from $20/mo | Free only |
| Free Plan | ✓ Yes | ✓ Yes |
| Rating | 4.3 / 5 | 4.4 / 5 |
| Best For | typescript-developers, edge-computing, api-builders, jamstack-sites | ai-developers, startups, cost-conscious-teams, open-source-advocates |
| Founded | 2021 | 2022 |
| Edge Functions | ✓ | ✗ |
| Kv Database | ✓ | ✗ |
| Message Queues | ✓ | ✗ |
| Github Integration | ✓ | ✗ |
| Custom Domains | ✓ | ✗ |
| Automatic Https | ✓ | ✗ |
| Playground | ✓ | ✗ |
| Inference Api | ✗ | ✓ |
| Fine Tuning | ✗ | ✓ |
| Custom Models | ✗ | ✓ |
| Openai Compatibility | ✗ | ✓ |
| Batch Processing | ✗ | ✓ |
| Embeddings | ✗ | ✓ |
✓ Deno Deploy Pros
- Deploys to 35+ edge locations automatically
- Zero-config with native TypeScript support
- Built-in KV database and message queues
- Generous free tier (100K requests/day)
✗ Deno Deploy Cons
- Limited to Deno runtime (not Node.js compatible for all packages)
- Smaller ecosystem than established platforms
- Less suitable for long-running background jobs
✓ Together AI Pros
- Fast inference speeds
- OpenAI-compatible API
- Cheaper than OpenAI for many models
- Custom model fine-tuning
✗ Together AI Cons
- Only open-source models
- Limited compared to full cloud providers
- Newer platform
The Verdict
Deno Deploy is built for typescript developers and edge computing, with a focus on edge-functions and kv-database. Together AI targets ai developers and startups and leads with inference-api and fine-tuning.
Together AI uses custom enterprise pricing, while Deno Deploy starts at $20/mo — a tangible advantage for teams with a fixed budget.
Both offer free plans, so you can test each with your real workflow before committing to a subscription.
Feature-wise, Deno Deploy offers broader built-in capabilities (7 features vs 6), while Together AI takes a more focused approach — which can mean a simpler, faster onboarding experience.
This is a genuinely close comparison. If you can, sign up for both free trials (where available) and run a one-week test with your actual team tasks before deciding.