Key facts
| Together AI API surface | OpenAI-compatible chat completions for a large open-model catalogue |
| Together AI pricing model | Usage-based per-token billing plus dedicated GPU capacity pricing |
| Together AI deployment | Together-managed cloud with dedicated endpoints and clusters |
| Plugsky API compatibility | OpenAI-compatible /v1/chat/completions; change the base URL and model name |
| Plugsky pricing model | Flat monthly self-serve plans with unlimited fair-use usage; no per-token billing |
| Plugsky model access | 30+ managed models behind one API, from free to frontier tiers |
| Plugsky fine-tuning | Coming soon — not available today |
| Plugsky deployment and residency | Plugsky cloud, your VPC, on-prem and air-gapped; region selection for residency |
TL;DR
- Together AI's edge is fine-tuning and dedicated GPU clusters for open models.
- Plugsky's edge is managed simplicity with flat monthly self-serve pricing.
- Both are OpenAI-compatible, so client migration is configuration, not a rewrite.
- Keep fine-tuned and dedicated-capacity workloads on Together until Plugsky fine-tuning ships.
- Free plan with 2 free AI models (plugsky-micro, plugsky-lite) plus a 14-day full-access trial.
How it works, step by step
- List your Together AI usage: serverless models, fine-tuned models and dedicated clusters.
- Create a Plugsky account and API key on the free plan (no card required).
- Map serverless model IDs to the closest Plugsky models and record the mapping.
- Switch a copy of your client to the Plugsky base URL and run your eval suite against both.
- Compare quality, latency and tool-calling behaviour on the same prompts.
- Keep fine-tuned models and dedicated GPU workloads on Together for now.
- Canary production traffic and keep both clients behind a provider switch for rollback.
Try it yourself
Open the OpenAI compatibility checker →
Where Together AI wins
Together AI is built for teams working with open models at depth. The catalogue is broad, fine-tuning and custom model hosting are established, and dedicated clusters give you predictable performance for heavy or latency-sensitive workloads. If you train on your own data, serve a model that does not exist in managed catalogues, or need guaranteed capacity rather than best-effort routing, those capabilities are difficult to substitute. The platform also suits experimentation across open-weight models before committing to one.
Where Plugsky wins
Plugsky is the simpler default when custom weights are not the point. One OpenAI-compatible endpoint serves 30+ managed models, and flat monthly self-serve plans with unlimited fair-use usage replace per-token budgeting for chat, summarisation and agent workloads. There is no cluster to size or idle capacity to pay for, and routing, fusion and failover are handled by the platform. The free plan includes two free AI models and a 14-day full-access trial. Enterprise deployments extend to VPC, on-prem and air-gapped environments with region selection for residency.
What to test before switching
Split the comparison by workload type. For serverless chat, compare quality, JSON adherence and tool correctness on your prompts, then measure throughput under realistic concurrency. For fine-tuned models, test whether prompting a general model reproduces the behaviour before assuming the fine-tune must move — sometimes it does not, and the migration is worth it; sometimes it does. For dedicated capacity, compare the cost of reserved GPUs against flat pricing at your actual volume, including idle time. Check the data path and retention terms for each platform.
Migration path from Together AI to Plugsky
Both APIs are OpenAI-compatible, so the mechanics are small: set base_url='https://api.plugsky.com/v1', use a Plugsky key and map model names. Keep provider selection in configuration so routing and rollback are cheap. Migrate serverless chat and embeddings first, measure for a week, and leave fine-tuned models and dedicated clusters on Together until Plugsky fine-tuning becomes available. Running two providers behind one internal client is a stable end state, not a temporary hack.
Honest comparison
| Capability | Plugsky | Together AI | Building in-house |
|---|---|---|---|
| API compatibility | OpenAI-compatible | OpenAI-compatible | Full rewrite |
| Pricing model | Flat monthly self-serve, unlimited fair use | Per token plus dedicated GPU pricing | GPU + ops cost |
| Model access | 30+ managed models | Large open-model catalogue | You host each model |
| Fine-tuning | Coming soon — not available today | Fine-tuning plus dedicated clusters | You own the pipeline |
| Deployment and residency | Cloud, VPC, on-prem, air-gapped; region choice | Together-managed cloud and dedicated capacity | You control |
| Migration effort | One line plus model mapping | Stay for fine-tunes and dedicated capacity | Months |
Frequently asked questions
Can I reuse my Together AI client with Plugsky?
Yes. Both expose OpenAI-compatible chat completions, so the same client works with a different base URL, key and model name.
Does Plugsky support fine-tuning?
Not yet. Fine-tuning is marked as coming soon, so keep fine-tuned models on Together AI for now.
How does Plugsky pricing differ from Together AI?
Together bills per token and for dedicated capacity. Plugsky self-serve plans are flat monthly with unlimited fair-use usage and no per-token charges. See the live pricing page for current plans.
Can I keep dedicated GPU clusters and use Plugsky?
Yes. Many teams keep dedicated capacity for heavy or pinned workloads and route general chat, embeddings and RAG traffic to Plugsky.
Will quality match my current open-model setup?
Map each model to the closest Plugsky option and score both on your own prompts. Behaviour can differ even between similar model sizes.
Does Plugsky support tools, streaming and embeddings?
Yes — streaming, function calling, JSON mode, embeddings, RAG and agents are all live on Plugsky.
Can Plugsky deploy in our own environment?
Yes. Enterprise deployments support VPC, on-prem and air-gapped environments with region selection for data residency.
How should I start the evaluation?
Create a free account with no card, map your highest-volume model, and run your eval suite against both platforms before moving production traffic.