Why Together.ai Can’t Handle Custom Models

Your Fine-Tuned Model Doesn’t Fit in Together.ai’s Catalogue

You spent three months fine-tuning a Llama 3.1 model on 500,000 domain-specific examples. The evaluation metrics are stellar — 23% better accuracy than the base model on your benchmark suite. Now you need to serve it in production. Together.ai seems like the obvious choice: they already host the base Llama models, their API is fast, and the pricing is competitive. Except your custom model doesn’t use the standard Llama chat template. It has a custom tokeniser vocabulary extension. It requires a specific quantisation scheme to fit your latency budget. And Together.ai’s platform wasn’t built for any of this.

Together.ai is excellent at what it does: serving a curated catalogue of popular open-source models at competitive prices. But the moment your AI work moves beyond off-the-shelf models into custom territory — fine-tuned weights, modified architectures, multi-model pipelines — the platform’s limitations become constraints. Dedicated GPU infrastructure is where custom models belong.

Where Together.ai Falls Short for Custom Models

The Custom Model Reality

Production AI companies don’t serve base models. They serve fine-tuned models, distilled models, merged models, and ensembles of specialised models working in concert. Together.ai’s fine-tuning offering lets you create LoRA adapters for a limited set of base models, but the resulting models must conform to the platform’s serving constraints. You cannot:

• Deploy models with custom attention mechanisms or architectural modifications
• Serve models that require specific preprocessing or postprocessing pipelines
• Run multi-model inference chains where output from one model feeds directly into another
• A/B test between model variants with traffic splitting at the serving layer
• Hot-swap model versions without downtime for seamless deployments

These aren’t edge cases — they’re standard requirements for any team serious about production model serving. On dedicated GPUs, you have complete control over the serving stack, from model loading to request routing to output processing.

Dedicated GPUs for Custom Model Serving

Self-hosted inference on dedicated hardware removes every constraint Together.ai imposes. Load your custom model with vLLM, Triton, or raw PyTorch — whatever your architecture demands. Serve multiple model versions simultaneously for A/B testing. Chain models in zero-latency pipelines where a classifier routes requests to specialised fine-tuned variants.

The infrastructure cost comparison favours dedicated hardware the moment you move past simple API-style serving. Together.ai charges per-token even for your own fine-tuned models. On a dedicated RTX 6000 Pro 96 GB, your custom 70B model processes tokens at a fixed monthly cost regardless of volume. Compare the economics with our GPU vs API cost comparison tool or estimate with the LLM cost calculator.

Custom Models Deserve Custom Infrastructure

If your competitive advantage comes from proprietary models, those models need infrastructure that doesn’t limit how you serve them. Together.ai is a fine starting point for standard models, but graduating to dedicated GPUs is inevitable once your model development outgrows a managed platform’s constraints.

See our Together.ai alternative page for a direct comparison, browse open-source model hosting for deployment guides, or explore private AI hosting for sensitive model deployments. More in the alternatives section and tutorials.

Filed under: Alternatives