Together Ai Guide

---
name: together-ai-guide
description: Run open models on Together AI — serverless inference, fine-tuning, and GPU clusters for builders.
category: ai-research
---

## Overview

Together AI is a cloud platform for open-source models: serverless inference
APIs for popular open models (Llama, Qwen, DeepSeek, Mistral, and many more),
fine-tuning services, and dedicated GPU clusters. The pitch is open-model power
without the infrastructure: call an API for inference, fine-tune without
managing training clusters, or rent capacity for custom deployments — all within
one platform.

For builders, the practical shape is: start with serverless inference (no setup,
pay per token), fine-tune when a base model needs task specialization, and move
to dedicated endpoints or clusters when volume or latency SLOs demand it. The
platform covers the full lifecycle from experiment to production for teams
standardizing on open models.

Together AI matters in an open-model strategy as the "don't build infra" option.
If you want open models without operating GPUs, it's one of the primary choices
to evaluate.

## When to use

- Serving open models via API without managing GPUs (prototypes through
  production).
- Fine-tuning open models on your data (instruction tuning, domain adaptation)
  without training infrastructure.
- High-volume open-model workloads needing dedicated endpoints with latency
  guarantees.
- Research requiring many open models behind one API and billing account.
- Teams standardizing on open weights for data-control or cost reasons.
- Batch inference over large datasets with open models.

## Core concepts

- **Serverless inference**: pay-per-token API for a large catalog of open models.
  No provisioning; scales automatically. The default starting point — validate
  the model and task fit before committing to anything heavier.
- **Model catalog**: hosted open models across sizes and families, plus some
  proprietary options. Check per-model context limits, pricing, and availability
  before building on them.
- **Fine-tuning**: managed training jobs on your datasets (supervised
  fine-tuning, and often preference-tuning variants). You bring data; the
  platform handles infrastructure, then serves the resulting model.
- **Dedicated endpoints**: reserved capacity for your workload — predictable
  latency and throughput, isolated from noisy neighbors. The step up when
  serverless variability violates your SLOs.
- **GPU clusters**: raw compute rental for custom training or serving stacks.
  For teams that want control but not data-center contracts.
- **OpenAI-compatible API**: standard chat/completions interfaces, so client
  code written for other providers usually ports with minimal changes.
- **Batch inference**: offline bulk processing at reduced rates for
  non-latency-sensitive workloads (dataset labeling, backfills, evals).
- **Usage and cost tracking**: per-model, per-key usage dashboards. With
  fine-tuned models plus inference, attribute costs across the lifecycle.

## Practical workflow

1. **Validate on serverless first.** Pick candidate open models, run your eval
   set through the API. Confirm an open model actually meets your quality bar
   before investing in fine-tuning or dedicated capacity.
2. **Decide: prompt, fine-tune, or both.** If prompting a strong open model
   suffices, stop. Fine-tune when you need consistent behavior the base model
   won't reliably produce (format adherence, domain style, specialized
   knowledge).
3. **Prepare fine-tuning data carefully.** Data quality dominates fine-tuning
   outcomes. Curate and clean; start with a few thousand high-quality examples;
   evaluate the fine-tune against the base model on a held-out set.
4. **Evaluate fine-tune vs. base honestly.** Fine-tuning can degrade general
   capabilities while improving the target task. Measure both — don't ship a
   model that's better at your format and worse at everything else without
   deciding that's acceptable.
5. **Scale the serving tier with demand.** Serverless → dedicated endpoints as
   latency SLOs tighten and volume grows. Make the move based on measured p99
   latency and cost-per-token at your volume.
6. **Use batch for offline work.** Dataset processing, evaluations, and
   backfills go through batch inference — significantly cheaper than
   latency-optimized serving.
7. **Monitor quality continuously.** Track task metrics on production traffic
   (sampled). Model versions and serving stacks change; your metrics are the
   contract.

Checklist for a Together AI deployment:
- Open-model choice validated against proprietary alternatives on your evals.
- Fine-tuning data curated; base-vs-fine-tune comparison done.
- Serving tier matched to latency/volume requirements.
- Batch used for offline workloads.
- Cost tracked per model, key, and workload type.

## Common pitfalls

- **Fine-tuning before validating the base.** Training on a weak base model
  choice wastes the whole effort. Validate base models first.
- **Thin fine-tuning data.** A few hundred noisy examples produce a model that's
  different, not better. Invest in data quality and quantity.
- **Ignoring general-capability regression.** The fine-tune aces your task and
  forgets everything else. Measure broadly, decide consciously.
- **Serverless for strict SLOs.** Shared serverless capacity has latency
  variance. If p99 matters, test it under load — then move to dedicated.
- **No cost attribution.** Inference + fine-tuning + storage across many models
  without per-workload tracking. Surprise bills follow.
- **Model version drift.** Hosted models get updated; your evals were on the old
  version. Pin versions where the platform allows; re-validate on updates.
- **Skipping batch for bulk work.** Running million-record backfills through
  real-time endpoints at full price. Batch exists for this.
- **Open-model hype over measurement.** "Open" is not a quality metric. Benchmark
  against your actual alternatives on your actual task.