Key facts
| API compatibility | Together's inference API is OpenAI-compatible; Plugsky is OpenAI-compatible, so most clients port with a base-URL change |
| Models | Together hosts a broad open-model catalogue; Plugsky serves 30+ managed models behind one API |
| Pricing model | Together bills per token for serverless inference and per GPU-hour for dedicated clusters; Plugsky self-serve plans are flat monthly with fair-use usage |
| Free tier | Together offers trial credits; Plugsky free plan includes 2 free AI models, no card |
| Deployment / residency | Together Cloud plus dedicated clusters; Plugsky adds VPC, on-prem and air-gapped options |
| Migration effort | Model ID mapping and parameter checks; base-URL change for chat and embedding calls |
| Fine-tuning | Plugsky fine-tuning is coming soon; Together supports training and tuning today |
| Product status | Plugsky chat, streaming, embeddings, RAG and agents are live; audio, images, batch and fine-tuning are coming soon |
TL;DR
- Plugsky covers standard open-model inference with flat monthly self-serve pricing.
- 30+ models behind one OpenAI-compatible endpoint reduces vendor integration work.
- Fine-tuning and dedicated GPU clusters remain reasons to keep Together for now.
- Deployment options include your VPC, on-prem and air-gapped environments.
- A hybrid split — dedicated training on Together, production inference on Plugsky — works well.
How it works, step by step
- Inventory Together endpoints in use: chat, completions, embeddings and fine-tuned models.
- Separate inference traffic from training and dedicated-cluster workloads.
- Map inference models to Plugsky equivalents and run evals on your prompts.
- Repoint the OpenAI-compatible client to Plugsky in staging.
- Verify streaming, JSON mode and function calling against your tests.
- Compare monthly spend with the Together AI cost calculator and flat plans.
- Move inference traffic to Plugsky and keep tuning workloads on Together.
Try it yourself
Open the Together AI cost calculator →
Who should consider a Together AI alternative
Together AI suits teams that want open-model inference plus GPU capacity for training and fine-tuning. Teams look for alternatives when most of their spend is steady inference, when per-token and per-GPU-hour billing is hard to forecast, or when compliance wants inference running inside their own network. If your product ships on open models but you rarely train, moving inference to a flat-rate managed platform is usually the simpler architecture.
What to check before you switch
Together's API is OpenAI-compatible, so the port is mostly mapping and validation:
- Model parity: confirm the same base models and context windows are available.
- Tuning dependency: list all fine-tuned models and where they are served.
- Structured output: compare JSON and tool-calling behaviour on your schemas.
- Capacity: decide which workloads need dedicated GPUs versus fair-use managed throughput.
- Residency: document where inference and training data must live.
How Plugsky compares
Plugsky serves 30+ models through a single OpenAI-compatible endpoint with streaming, function calling, JSON mode, embeddings, RAG and agents live. Self-serve pricing is flat monthly with fair-use usage instead of per-token metering, which fits high-volume chat, classification and embedding workloads, and deployment can run on our cloud, in your VPC, on-prem or air-gapped. The honest gaps: fine-tuning is coming soon rather than live, and there is no dedicated GPU capacity to rent. If you train custom adapters or need reserved clusters, keep those with Together.
When Together AI is still the right choice
Stay with Together when you run training and fine-tuning as part of the product, when reserved GPU capacity or custom serving is required, or when you depend on an open model that the Plugsky catalogue does not carry. It is also reasonable if your team values provider breadth in open weights. A hybrid approach keeps costs sane: train and serve specialised models on Together, and handle high-volume inference through Plugsky's flat-rate OpenAI-compatible API.
Honest comparison
| Capability | Plugsky | Together AI | Building in-house |
|---|---|---|---|
| API compatibility | OpenAI-compatible /v1/chat/completions | OpenAI-compatible inference API | Full rewrite plus serving stack |
| Model access | 30+ managed models one API | Broad open-model catalogue | You host every model |
| Pricing | Flat monthly self-serve with fair-use usage | Per-token plus per-GPU-hour | GPUs, ops and scaling cost |
| Fine-tuning | Coming soon | Supported today | You run the training stack |
| Dedicated GPU capacity | Not offered (managed platform) | Reserved clusters available | Your infrastructure |
| Deployment / residency | Cloud, VPC, on-prem, air-gapped | Together Cloud plus dedicated clusters | Wherever you run GPUs |
Frequently asked questions
Can I move my Together inference code to Plugsky?
For chat and embedding endpoints, mostly yes. Both are OpenAI-compatible, so change the base URL and map model names, then verify parameters and structured output.
Does Plugsky support fine-tuning?
Not yet — fine-tuning is listed as coming soon. Keep training and tuned-model serving on Together until it ships.
How does pricing compare?
Together charges per token for serverless inference and per GPU-hour for dedicated capacity; Plugsky self-serve plans are flat monthly with unlimited fair-use usage. See the live pricing page.
Is there a free plan?
Yes — two free AI models (plugsky-micro and plugsky-lite) with no credit card, plus a 14-day full-access trial.
Can I deploy Plugsky in my own network?
Enterprise deployment options include VPC, on-prem and air-gapped environments, which suits teams with strict residency requirements.
Which models should I map first?
Start with your highest-volume chat and embedding models, run evals, and leave tuned or rarely used models on Together initially.
Can I use both platforms?
Yes. Keep training, tuning and specialised models on Together while routing steady production inference to Plugsky.