Key facts
| API compatibility | OpenAI-compatible chat and embeddings |
| Model catalogue | 30+ models across families |
| Pricing model | Flat monthly self-serve plans |
| Fine-tuning | Coming soon |
| Dedicated capacity | Not offered; managed shared service |
| Deployment | Cloud, VPC, on-prem, air-gapped |
| Free access | Free plan with plugsky-micro and plugsky-lite |
| Feature status | Chat, streaming, JSON mode, function calling and embeddings live |
TL;DR
- Both APIs are OpenAI-compatible; the difference is catalogue and economics.
- Together AI leads on fine-tuning and dedicated GPU capacity.
- Plugsky leads on flat pricing and private deployment.
- Per-token suits light usage; flat plans suit steady usage.
- Migrate chat workloads first and keep fine-tuned models where they are.
How it works, step by step
- List the models and endpoints your app calls on Together AI.
- Identify workloads that depend on fine-tuning or dedicated capacity.
- Test the same prompts on Plugsky models and compare structured output quality.
- Compare per-token spend with flat plan pricing at real volume.
- Check residency and deployment requirements.
- Move compatible workloads first and document what stays behind.
Try it yourself
Open the LLM API cost calculator →
What the APIs share
The request shapes match. Chat completions with messages, streaming tokens, tool definitions and JSON mode work the same way, and embeddings are available on both sides. A client written for one service usually runs against the other after a base URL and model-name change.
The catalogue overlap is decent rather than complete. Both serve open-weight families, but exact models, versions and context limits differ, so check the specific model rather than the family name.
Where they diverge
Together AI sells capacity as well as inference. Dedicated endpoints and GPU clusters exist for teams that need predictable hardware, and the fine-tuning service covers custom weights. Billing follows that: per token for serverless, per GPU-hour for clusters.
Plugsky sells a managed catalogue: 30+ models behind one endpoint, flat monthly self-serve plans with unlimited fair use on paid tiers, and a free plan with plugsky-micro and plugsky-lite. Deployment extends to VPC, on-prem and air-gapped for stricter environments, and current plans are on the live pricing page. What is missing today is hosted fine-tuning and dedicated capacity, which remain reasons to keep Together in the stack.
Migration plan
Split your usage into three buckets: plain chat and embeddings, workloads that depend on dedicated capacity, and workloads that depend on fine-tuned weights. The first bucket migrates with configuration changes and a re-evaluation pass; the other two stay until equivalent capabilities are live.
During cutover, run both endpoints in shadow mode for a sample of traffic and compare outputs, latency and cost. Keep model identifiers in configuration and record why each remaining workload stays behind. That documentation is what makes the next migration, in either direction, uneventful.
Honest comparison
| Dimension | Plugsky | Together AI | What to verify |
|---|---|---|---|
| API compatibility | OpenAI-compatible | OpenAI-compatible | Parameter and field support |
| Catalogue | 30+ models across families | Broad open-model catalogue | Exact model versions |
| Pricing | Flat monthly self-serve plans | Per token or GPU-hour | Cost at real volume |
| Fine-tuning | Coming soon | Available | Whether training is on your roadmap |
| Dedicated capacity | Not offered | Endpoints and clusters | Capacity requirements |
| Deployment | Cloud, VPC, on-prem, air-gapped | Hosted platform | Residency and data path |
Frequently asked questions
Can I use my Together AI client with Plugsky?
Yes, if it uses OpenAI-compatible chat completions. Change the base URL, replace the key and map the model name, then re-test tools, JSON mode and streaming.
Does Plugsky offer fine-tuning like Together AI?
Not yet. Fine-tuning endpoints are coming soon. Teams that depend on hosted training should keep a provider that offers it while using Plugsky for base-model inference.
How does pricing compare?
Together AI bills per token for serverless and per GPU-hour for clusters. Plugsky self-serve plans are flat monthly with unlimited fair use on paid tiers. Compare at your real volume.
Which has more models?
Together AI's open-model catalogue is broader. Plugsky serves a curated 30+ models across families, so check that each model you need has an equivalent.
Can I get dedicated capacity on Plugsky?
No. Plugsky is a managed shared service. If you need reserved GPUs, keep that workload on Together AI or self-host.
Is private deployment available?
Yes, on Plugsky via VPC, on-prem and air-gapped options. Together AI is hosted, so verify its regional and contractual options for your requirements.
What is the fastest way to evaluate a switch?
Start on the free plan with plugsky-micro and plugsky-lite, or use the 14-day full-access trial, and score the same tasks on both services.