Ertas vs Together AI
Compare Ertas and Together AI for LLM fine-tuning in 2026. See how Ertas's visual no-code platform with GGUF export compares to Together AI's cloud fine-tuning and inference service.
Overview
Together AI has built a strong reputation as a cloud platform for open-source model inference and fine-tuning. Their fine-tuning API supports popular architectures like Llama, Mistral, and Mixtral, and their serverless inference endpoints make it easy to deploy fine-tuned models without managing GPU infrastructure. Together AI is developer-focused: you interact through their API or Python SDK, upload training data in JSONL format, and receive a fine-tuned model that you can serve through their inference endpoints.
Ertas approaches the same problem from a different angle. Rather than providing an API-first developer platform, Ertas provides a visual interface where you can upload data, configure training, run experiments, and export models — all without writing code. The critical difference in output is that Ertas produces GGUF files you download and own, while Together AI's fine-tuned models live on their platform and are accessed through their API at per-token pricing.
Both platforms work with open-weight models, which is a significant shared advantage over proprietary fine-tuning services. However, they serve different audiences: Together AI is built for developers who want a managed cloud platform with API access, while Ertas is built for practitioners who want a visual workflow with full model ownership at the end.
Feature Comparison
| Feature | Ertas | Together AI |
|---|---|---|
| GUI interface | ||
| Code required | API/SDK | |
| Model ownership | Full (GGUF file) | Weights downloadable (some models) |
| GGUF export | One click | Not directly |
| Local deployment | ||
| Serverless inference | ||
| Experiment tracking | Basic job tracking | |
| Open-weight models | ||
| Per-token inference cost | None (local) | Yes |
| Dedicated GPU endpoints |
Strengths
Ertas
- Visual interface with guided workflows — no API calls, no SDK setup, no JSONL formatting required
- One-click GGUF export gives you a deployment-ready file for Ollama, LM Studio, or any GGUF-compatible runtime
- No per-token inference cost — once trained, run your model locally at the cost of your own compute
- Built-in experiment tracking with side-by-side comparison of multiple training runs on the same evaluation set
- Non-technical users can operate the full pipeline without developer assistance
- Iterative training from checkpoints lets you refine models incrementally without starting over
Together AI
- Serverless inference endpoints provide instant scaling without capacity planning or GPU management
- Developer-friendly API and Python SDK integrate naturally into existing codebases and CI/CD pipelines
- Competitive per-token pricing makes it cost-effective for low to moderate inference volumes
- Supports a wide range of open-source models including Llama, Mistral, Mixtral, and more
- Dedicated GPU endpoints available for high-throughput production workloads with guaranteed capacity
- Fast fine-tuning turnaround times with optimized infrastructure and efficient training pipelines
Which Should You Choose?
Together AI's serverless endpoints handle scaling automatically. If your application needs to serve thousands of concurrent requests and you want zero infrastructure management, Together AI's managed inference is purpose-built for this.
Ertas produces a GGUF file you own. Once training is complete, you can run the model on your own hardware with no per-token charges and no dependency on any cloud service.
Ertas provides a complete visual workflow with experiment tracking. Together AI requires API calls or SDK usage, which assumes developer skills and comfort with command-line tools.
Together AI's API and SDK make it straightforward to call your fine-tuned model from application code with standard HTTP requests or Python function calls.
At high inference volumes, per-token API pricing adds up significantly. Ertas lets you run your fine-tuned model locally at a fixed hardware cost, which becomes dramatically cheaper as usage grows.
Verdict
Together AI is an excellent platform for developers who want managed fine-tuning and inference for open-source models. Their API is clean, their pricing is competitive, and their serverless endpoints remove the burden of GPU infrastructure management. If you are building an application that needs scalable model serving and you want to stay in the open-source model ecosystem without managing your own GPUs, Together AI is a strong choice.
Ertas serves a different need. If you want full model ownership, no ongoing inference costs, and a visual interface that non-technical team members can use, Ertas is the better fit. The GGUF export means your fine-tuned model is a file you control — not a service you rent. For teams where the goal is to build a model asset rather than consume a model service, Ertas provides a more ownership-oriented approach. The choice ultimately depends on whether you value managed scalability (Together AI) or model ownership and visual workflows (Ertas).
How Ertas Fits In
This is a direct comparison. Ertas and Together AI both work with open-weight models, but they deliver results differently. Together AI gives you a cloud-hosted fine-tuned model accessed through their API with per-token pricing. Ertas gives you a GGUF file you own and deploy anywhere. Ertas also provides a visual interface that removes the need for API calls or SDK knowledge, making fine-tuning accessible to non-technical users.
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