Together AI review

Together AI is a hosted AI infrastructure platform, not a model library or a self-hosted inference server. Its main integration surface is model inference through APIs. The platform also offers batch inference, reserved token capacity, dedicated model and container deployments, GPU clusters, managed storage, and managed fine-tuning.

In a typical stack, an application sends inference work to Together AI rather than provisioning and operating its own serving fleet. Serverless Inference is for on-demand model calls; Batch Inference handles asynchronous workloads of up to 30 billion tokens per model. Provisioned Throughput reserves inference capacity and includes a 99% uptime SLA. GPU compute and model-shaping tools extend the platform into training and infrastructure work.

The service is aimed at teams building with open models that want managed infrastructure, from experimentation to production. That can reduce operational work, but it also puts serving availability, capacity terms and part of the application’s model interface under a hosted provider.

The core integration is API-based inference. Together AI describes Serverless Inference as high-performance inference APIs and says Provisioned Throughput has drop-in API compatibility for production workloads. Batch Inference provides an asynchronous path for large jobs. Together Link is presented as a way to run open models inside a harness you already use, but the homepage does not identify specific harnesses or protocols.

The developer resources include technical documentation, cookbooks, demos, a model library, a playground and Together Chat. Those are useful starting points for selecting models and working through implementation patterns. The homepage does not name programming languages, SDK packages or a CLI. Confirm SDK and language support in the vendor’s documentation before choosing a client library; do not assume that drop-in API compatibility means every SDK or feature behaves identically.

For production integration, check request formats, streaming behavior, error handling, model availability and rate limits in the docs. The platform lists serverless, batch and reserved-capacity options, so the right interface depends on whether the workload is interactive, asynchronous or capacity-sensitive.

Together AI has a free plan, and its model library includes free offerings such as Ternary Bonsai 27B at $0 per 1M input and output tokens. The vendor does not publish the free plan’s allowance. Serverless Inference is usage-based: Kimi K3 is listed at $3 per 1M input tokens and $15 per 1M output tokens; Tev1 4B Experimental is $0.04 per 1M input tokens with free output. Batch Inference lists Kimi K3 at the same rates and Ternary Bonsai 27B at $0.

Provisioned Throughput is billed per PTU per minute, with fixed inference capacity and a 99% uptime SLA; the estimator assumes about 43,800 minutes monthly. Dedicated Inference lists HGX H100 at $5.49 per GPU-hour and HGX B200 at $8.99. HGX H200, B300 and GB200 NVL72, plus reserved rates, require a quote. Dedicated Container Inference uses custom pricing. Media generation adds separate charges, including $0.0154 per FLUX.2 dev image and $0.115 per ByteDance Seedance 2.5 video. Settings can change image costs.

Together AI provides access to open-source models, but that does not make the hosted platform itself open source. Model licenses vary, so check the terms for each model before shipping it. The vendor does not publish a platform source license, a self-hosted Together server option or a defined data-export path.

The service is presented as managed infrastructure, including hosted inference and dedicated deployments. Teams can reduce model-level lock-in by keeping application logic behind an internal model adapter and choosing models whose weights and licenses support another serving stack. That does not remove migration work: request formats, model behavior, fine-tuning artifacts and provisioned-capacity arrangements may differ elsewhere.

Before committing, ask how to retrieve prompts, outputs, fine-tuned weights and stored data, and what happens to reserved capacity if you leave. For a hard self-hosting requirement, evaluate the model and serving software separately from Together AI’s hosted services.

The platform offers several ways to scale: serverless inference, batch workloads up to 30 billion tokens per model, Provisioned Throughput with a 99% uptime SLA, and dedicated GPU infrastructure. The stated SLA applies to Provisioned Throughput; confirm the contract, exclusions and remedies before treating it as an application-level availability guarantee. The pricing page does not give general rate limits, so ask about model-specific quotas, burst behavior and how limits change with capacity.

Reddit reports are mixed. One commenter says Together is “very fast” and can handle many tokens, while another says it can be “not necessarily inexpensive.” A separate thread includes a user reporting a collections email over a $0.51 balance after using a credit. These are individual accounts, not service guarantees, but they make billing controls and account support worth checking.

For production, set usage alerts, validate fallback behavior and test the exact models and deployment type your application will use. Ask the vendor about data handling, security controls, compliance coverage and incident escalation before sending sensitive workloads.