Key facts
| What Together AI is | Inference, fine-tuning and GPU platform for open models |
| API style | OpenAI-compatible chat completions |
| Pricing model | Per token for serverless, GPU-hour for clusters |
| Capabilities | Serverless inference, dedicated endpoints and fine-tuning |
| Plugsky model | Managed 30+ model catalogue served directly |
| Plugsky pricing | Flat monthly self-serve plans; free plan with two models |
| Plugsky deployment | Cloud, VPC, on-prem or air-gapped |
| Best for | Teams wanting managed breadth without GPU operations |
TL;DR
- Together AI offers inference, dedicated capacity and fine-tuning on open models.
- Alternatives trade that depth for simpler pricing or different deployment models.
- Plugsky fits teams that want 30+ models without GPU operations.
- Hyperscaler marketplaces fit cloud-native procurement and billing.
- Self-hosting fits strict control and steady high utilisation.
How it works, step by step
- Decide which Together capabilities you use: serverless inference, clusters or fine-tuning.
- Check whether those map to a managed API, a marketplace or self-hosting.
- Test your prompts on the candidate platform and compare quality.
- Model the cost of per-token usage against flat monthly plans.
- Review residency, data handling and procurement requirements.
- Migrate the workloads that fit and keep the rest where they are.
Try it yourself
Open the Together AI API cost calculator →
What Together AI covers
Together AI is more than an inference endpoint. It sells serverless per-token access to a broad open-model catalogue, dedicated endpoints for predictable capacity, GPU clusters for training and custom serving, and a fine-tuning service. That combination suits teams building on open models as a platform rather than simply calling an API.
The cost of that depth is complexity: several products, different billing units and infrastructure decisions that a pure inference customer never has to make.
The alternative landscape
Three alternatives cover most cases. A managed multi-model API such as Plugsky replaces serverless inference with a curated catalogue, flat monthly self-serve plans and a free plan covering plugsky-micro and plugsky-lite, adding VPC, on-prem and air-gapped deployment for teams that need them. A hyperscaler marketplace fits organisations that want one cloud bill, identity model and region story. Self-hosting fits teams with GPUs, platform engineers and steady high utilisation.
Honest trade-offs: Plugsky does not sell dedicated GPU capacity or hosted fine-tuning today, so teams that depend on those stay with Together or self-host. Conversely, Together's billing scales per token, which is less predictable for high-volume inference.
Deciding and migrating
Start from the capability list, not the brand. If you only use serverless chat completions, a managed platform is simpler and more predictable. If you need dedicated capacity or training, keep a platform that sells them and consider splitting inference workloads.
Migration for the serverless part is mechanical when both sides are OpenAI-compatible: base URL, model mapping, evaluation, staged cutover. Keep fine-tuned models where they are until equivalent endpoints are live on the destination, and document the exception rather than leaving it implicit.
Honest comparison
| Capability | Plugsky | Together AI | Alternative paths |
|---|---|---|---|
| Serverless inference | 30+ models, flat monthly plans | Broad open-model catalogue, per token | Marketplace or self-host |
| Dedicated capacity | Not offered | Dedicated endpoints and clusters | Stay or self-host |
| Fine-tuning | Coming soon | Available as a service | Stay or train in-house |
| Deployment control | Cloud, VPC, on-prem, air-gapped | Hosted platform | Self-hosting for full control |
| Pricing shape | Flat monthly self-serve plans | Per token or GPU-hour | Match to usage profile |
| Ops burden | None | Low to medium | High when self-hosting |
Frequently asked questions
What is the best Together AI alternative for simple inference?
A managed multi-model API such as Plugsky, which serves 30+ models behind one OpenAI-compatible endpoint with flat monthly plans and no infrastructure to operate.
What if I need dedicated GPU capacity?
Together AI sells dedicated endpoints and clusters, and self-hosting is the other option. Plugsky does not offer dedicated GPU capacity today, so those workloads should stay put.
Can I move fine-tuned models to Plugsky?
Not yet. Fine-tuning endpoints are coming soon. Until then, keep fine-tuned weights on a host that accepts them and use Plugsky for base-model inference.
Which option is cheaper?
Per-token pricing rewards light or spiky usage; flat monthly plans reward steady usage; self-hosting wins only at high sustained utilisation. Model it at your real volume.
Is migration hard?
For serverless chat workloads, no. Both APIs are OpenAI-compatible, so you change the base URL and model names, then re-run your evaluation set.
Does Plugsky offer private deployment?
Yes. Enterprise options include VPC, on-prem and air-gapped deployment, which can be decisive for regulated workloads.
Should I keep Together AI as well?
That is reasonable if you rely on dedicated capacity or fine-tuning. Many teams keep a platform for those and route general inference to a managed catalogue.