Together AI connects model selection, fine-tuning and dedicated inference

Together AI is a platform for running and adapting AI models. It offers serverless inference APIs, managed dedicated endpoints, fine-tuning and GPU infrastructure. A developer can begin with a supported model on a shared API and investigate a dedicated deployment or adapted model as the application becomes more specific. Those routes share parts of the developer experience, but they have different economics and controls.

01 / ProductA platform with several ways to run a model

The platform introduction¹ brings together inference, training and infrastructure. Serverless inference supplies access to supported models without the developer choosing a GPU configuration. Dedicated endpoints reserve serving resources for a deployment. Fine-tuning changes a supported model using training examples, while GPU clusters address teams that need more direct training or compute infrastructure.

The catalog includes language, image, audio and other supported tasks. It should not be described as a single collection of interchangeable open models: current documentation also distinguishes passthrough models, whose requests use a different provider route. The exact model and deployment type determine the supported request fields, price, availability and handling of data.

Serverless documentation³ describes common inference interfaces across shared and dedicated routes. That can simplify an application transition, but changing an endpoint is still a model and operating change. Prompts, structured responses, latency, output quality and available capacity need to be checked for the specific destination rather than assumed from API similarity.

02 / AudienceWho should consider Together AI

Together AI is relevant to teams that want to compare models and maintain a route toward customization. An ecommerce company extracting product attributes might start with several serverless models, select one against labeled examples, and later adapt a supported model to its catalog vocabulary. The platform can keep those stages within one broader development environment.

It also fits infrastructure teams evaluating dedicated inference for stable traffic or specific deployment requirements. The decision is less compelling when the organization needs only a single provider’s proprietary application and has no interest in model selection or serving configuration. A wider catalog creates useful options when someone owns the evaluation process.

The Replicate blueprint offers another perspective on moving from an available model to a running API. The Groq blueprint is useful when fast supported-model inference is the main purchasing criterion. Compare the complete task, including preprocessing, retries and response quality, rather than choosing from the model list alone.

03 / WorkflowA proposed workflow for cleaning product catalog data

Consider a retailer receiving supplier descriptions with inconsistent sizes, materials and category names. This is a proposed workflow, not a benchmark performed for this article. Define a target record with fields such as product family, material, dimensions, confidence and evidence text. Include an explicit unknown value so the model does not have to invent a missing measurement.

Build a judged dataset from actual catalog patterns, removing information that is unnecessary for the task. Include descriptions that mix packaging dimensions with product dimensions, contain several variants or use regional units. Hold back a separate evaluation set. A model that produces well-formed JSON can still select the shipping-box size, so format and field accuracy need separate checks.

Evaluate two or three supported serverless models using the same business definitions and appropriate model-specific request configuration. Record the exact model identifier, token usage, invalid outputs and reviewer corrections. Compare cost per accepted product record. Longer explanations may improve a particular model’s reliability, but they also change output usage and can slow a bulk import.

Use deterministic code for unit conversion and allowed category mappings wherever the rule is clear. The model’s useful job is interpreting messy language and identifying evidence. Preserve the original supplier text next to each extracted field. Reviewers should be able to trace a proposed material or dimension to the description without accepting an unsupported summary.

Investigate fine-tuning⁵ only after identifying a repeatable error pattern. The service distinguishes LoRA adaptation from full fine-tuning and supports supervised and preference-oriented approaches. A consistent taxonomy mismatch might justify training examples; missing supplier facts will not be fixed by training. Keep training and held-out examples separate and compare the adapted model with the original baseline.

For steady production traffic, compare a dedicated endpoint⁴ with serverless usage. Dedicated deployments can serve supported custom or fine-tuned weights and expose deployment controls. Capacity remains a function of hardware and configuration. Test the expected import burst, record failed requests and decide how the application queues records when the endpoint is full.

04 / PricingPricing depends on the model and serving route

A dedicated endpoint price and a GPU-cluster price are not interchangeable. They describe different services, even when the hardware name is the same. Promotions also have end dates: the pricing page includes time-limited offers. A forecast should preserve the named service, hardware, rate date and billing unit rather than copy an attractive GPU number into a long-term estimate.

Serverless caching is model-dependent. Where a cached-input rate is offered, matching prefixes may reduce the input charge, but automatic caching is best effort. Do not assume that every request will be a cache hit or that the entire conversation is cached. Use reported usage from a representative workload to estimate the actual proportion.

For the catalog workflow, budget for extraction, correction and reprocessing. A cheaper model that needs several retries may be more expensive per approved record. Fine-tuning adds dataset preparation and training costs; dedicated serving adds capacity that may sit idle between imports. The right comparison includes those stages and the staff time needed to approve difficult records.

05 / DistinctionsWhat stands out in the operating model

Together AI offers a progression from shared inference to a more specific deployment without requiring the team to begin with hardware management. That can be valuable when demand is uncertain. A working task evaluation can precede a capacity commitment, and the same judged dataset can later test a fine-tuned or dedicated alternative.

The platform’s treatment of adapted models is also useful. Training creates an artifact that must be evaluated and served, rather than ending at a training-success message. Depending on the supported route and model terms, a team can deploy the result through the platform or download it for another environment. Portability still requires checking the model license, format and runtime support.

Region control is a meaningful distinction between routes. The serverless documentation does not offer selectable inference regions; dedicated deployment supports region choices. A team with a concrete location requirement should establish that requirement before designing around shared inference. A common API does not make these operating properties identical.

06 / QuestionsQuestions to resolve about data and deployment

Is zero data retention enabled for the organization? Together’s retention documentation⁶ describes an explicit organization privacy setting: select No for storing prompts and responses. A personal setting does not automatically configure the organization. Training use is a separate opt-in control and should not be treated as the same setting.

What changes when that retention setting is enabled? The documentation says passthrough models become unavailable because that route requires prompt retention. Metadata can remain, and uploaded training or batch files are separate stored objects that need deletion. Enabling the setting also does not retroactively remove previously stored material. These distinctions matter when a catalog contains unpublished products or supplier agreements.

Who owns the deployment revision and rollback? Keep the chosen model, adaptation version, prompt, schema and endpoint configuration together. If a new version changes category assignments, the team needs to identify which records used it and rerun only the affected work. A stable API address is useful routing; it is not sufficient version history for an important data transformation.

07 / DecisionChoose the route after measuring the task

Together AI is a strong candidate when model choice, adaptation and deployment flexibility are part of the application’s roadmap. Begin with a defined task and a judged dataset. Use serverless inference to establish the baseline, then consider fine-tuning or dedicated capacity when the measured failure pattern or operating requirement supports that move.

For catalog extraction, the durable result is a reproducible transformation with traceable evidence, a clear exception queue and a measured cost per accepted record.