Together AI: From Serverless Inference to Dedicated Endpoints and Fine-Tuning

TL;DR Together AI puts serverless APIs for open-weight models, dedicated GPU endpoints, batch inference, and fine-tuning on one platform, letting teams validate per token before moving to reserved deployment when traffic or customization justifies it.

#together-ai #llm #inference #fine-tuning #openai-compatible #developer-platform

Table of Contents

Serverless: Choose a Model Before Managing GPUs
OpenAI Compatibility: A Similar Entry Point, Not an Identical Platform
Dedicated Endpoints: Reserved Hardware for Predictability
Fine-Tuning: Managed Training Does Not Outsource Data Quality
Choosing Together AI Against the Alternatives
The Bottom Line
References

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Together AI is an AI cloud platform centered on open-weight models. It is more than a catalog behind one model API: from the same account, you can test models such as Llama, Qwen, DeepSeek, and FLUX on serverless endpoints, move a chosen model onto reserved hardware through a dedicated endpoint, and train and deploy your own fine-tuned variant.

That product range addresses the gap between experimentation and production model serving. A prototype should not begin by renting an idle GPU, but a mature, latency-sensitive workload should not remain permanently constrained by a shared service. Together's core idea is to give both stages a similar API surface so that scaling does not require rewriting the application each time.

Serverless: Choose a Model Before Managing GPUs

Serverless Models are the easiest entry point. There is no provisioning cost or minimum usage. Text, embedding, and reranking workloads are generally billed by token, while images, video, and audio use modality-specific units. Shared infrastructure means the platform handles model loading and scaling, but it also imposes rate limits. This tier therefore fits evaluation, prototypes, bursty traffic, and workloads whose demand has not stabilized.

Together's distinction is not merely the size of its model catalog. It brings multiple modalities into one platform: chat and reasoning models sit alongside embeddings, rerankers, image, video, speech, and safety models. That is useful for RAG and multimodal products because a team can assemble the complete pipeline under one SDK and billing system before deciding which components deserve their own deployment.

For non-interactive work such as bulk classification, offline summarization, evaluation, or synthetic data generation, use the Batch API. Together documents discounts of up to 50% on selected serverless models, in exchange for turning individual calls into a job: upload JSONL, wait for completion, and download the results. Interactive chat still belongs on the regular inference endpoint; scheduled overnight work is the better batch candidate.

OpenAI Compatibility: A Similar Entry Point, Not an Identical Platform

Together provides an OpenAI-compatible interface. An existing OpenAI Python or TypeScript client can usually call chat, streaming, tool use, structured output, vision, embeddings, images, and speech after changing the API key, baseURL, and model name:

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.TOGETHER_API_KEY,
  baseURL: 'https://api.together.ai/v1',
});

const response = await client.chat.completions.create({
  model: 'openai/gpt-oss-20b',
  messages: [{ role: 'user', content: 'Summarize these meeting notes in three points.' }],
  stream: true,
});

“Compatible” means lower migration cost, not a complete substitute for every OpenAI feature. The current compatibility matrix does not implement the Responses API, Assistants, Threads, or Runs, and fine-tuning and batch jobs use Together-native APIs. Some parameters are ignored, while model IDs follow a <provider>/<model> namespace. Put provider differences behind an adapter and branch on HTTP status codes rather than assuming identical error codes or response fields.

Dedicated Endpoints: Reserved Hardware for Predictability

Once the model and traffic pattern are known, the elasticity of shared serverless inference may stop being the priority. Dedicated Endpoints run one model on hardware reserved for one customer, without shared-fleet rate limits and with latency and throughput that are easier to capacity-plan. They retain the same inference APIs, so moving from a serverless prototype mainly changes model or endpoint configuration rather than the entire calling path.

Dedicated endpoints bill by the minute while hardware is running, whether requests arrive or not. At the time of writing, the official list starts at US$3.99 per hour for a single H100; these figures change, so production estimates should use the live pricing page. There is no universal traffic threshold. Measure real token cost, peak concurrency, latency targets, and minimum replica count under load, then compare the resulting monthly totals.

A dedicated endpoint supports horizontal autoscaling between configured minimum and maximum replica counts. More replicas accept more concurrent requests and increase cost in proportion to active capacity. The official scaling documentation also warns that hardware availability can prevent an endpoint from reaching its configured maximum during a spike. A workload that cannot tolerate capacity shortfalls needs load tests, headroom, and a fallback path—not merely a larger max_replicas value.

Fine-Tuning: Managed Training Does Not Outsource Data Quality

Together's fine-tuning tools cover supervised fine-tuning (SFT), LoRA, and preference optimization through DPO, with parameters for RPO and SimPO variants. The CLI can upload local training data, estimate price, launch a job, track events, and download checkpoints. LoRA jobs can export either merged weights or an adapter, so teams can retain the artifact and plan deployments beyond a hosted API.

Fine-tuning should not be the first response to a weak prompt. It is appropriate when an application needs stable formatting, consistent style, domain-specific behavior, or a smaller model to execute a repeated task at scale. When facts change frequently, RAG is usually easier to update. Keep a held-out evaluation set, establish a baseline with the base model, and compare the fine-tuned version on the same product metrics. Falling training loss alone does not demonstrate a better application.

The resulting model still needs an inference destination. Some configurations can use serverless LoRA, while fuller customization or fixed performance requirements point toward a dedicated endpoint. Deployment eligibility depends on the base model and endpoint catalog. Before preparing a dataset, verify the intended model's training method, context limits, and serving path so that a successful training job does not produce an artifact the production environment cannot run.

Choosing Together AI Against the Alternatives

Together is a strong fit for teams that want multiple open-weight models and expect to progress from API experimentation to dedicated deployment or fine-tuning. If a product needs only one managed proprietary model, the first-party OpenAI, Anthropic, or Google API will generally expose that vendor's newest and most complete feature set. Together's compatibility layer cannot recreate the Responses API or other provider-specific capabilities.

If interactive latency is the sole priority, start with our Groq Console introduction. Groq emphasizes LPU-backed token generation speed; Together offers a broader path across model selection, batch work, tuning, and dedicated serving. If price dominates the decision, do not compare only headline token rates. Use our pricing comparison of more than 40 LLM inference services to check whether credits, limits, and workload shape actually align.

The other path is self-hosting vLLM. It provides more control while returning GPU procurement, capacity planning, upgrades, monitoring, and incident response to your team. A Together dedicated endpoint sits between raw API access and self-hosting: you choose the model, hardware, and scaling range while the platform operates the serving layer. Self-hosting can make sense with mature platform engineering and consistently saturated GPUs; otherwise, a managed dedicated endpoint usually reaches production sooner.

The Bottom Line

Together AI is not merely a model aggregator. It is a production path for open-weight models: serverless inference enables low-commitment evaluation, batch processing handles deferrable workloads, dedicated endpoints serve predictable production traffic, and fine-tuning addresses stable behavioral requirements that prompting and retrieval cannot resolve.

The practical adoption sequence is to benchmark quality and latency on serverless, test batch processing for schedulable work, and compare serverless with dedicated costs using real concurrency. Start fine-tuning only when an evaluation can state precisely where the base model fails. That sequence preserves Together's flexibility without renting GPUs for traffic that does not exist or training a model for a problem that has not been defined.