Key facts
| LiteLLM model | Open-source SDK plus proxy that you self-host |
| Provider keys | You connect your own accounts; tokens bill at each provider |
| Gateway features | Routing, fallbacks, retries, budgets and spend tracking |
| Plugsky model | Managed multi-model API; no gateway or database to operate |
| Models | 30+ models behind one OpenAI-compatible endpoint |
| Pricing | Flat monthly self-serve plans plus a free plan with two models |
| Deployment | Plugsky cloud, your VPC, on-prem or air-gapped |
| Product status | Chat, streaming, JSON mode, function calling and embeddings are live |
TL;DR
- Keep LiteLLM when provider flexibility, your own keys and self-hosting are hard requirements.
- Choose a managed API when operating the gateway is overhead rather than a feature.
- Plugsky serves 30+ models behind one OpenAI-compatible endpoint.
- Flat monthly self-serve plans replace reconciling many per-token provider bills.
- Start on the free plan with plugsky-micro and plugsky-lite, no card required.
How it works, step by step
- List which providers, models and keys your gateway currently fronts, and mark what your app truly depends on.
- Decide whether provider-level routing across your own accounts is a requirement or a convenience.
- Create a Plugsky API key and point a test environment at the OpenAI-compatible base URL.
- Map each model alias your app uses to a Plugsky model, keeping aliases in configuration.
- Re-run your evals and integration tests for streaming, JSON mode and function calling.
- Move traffic in stages, comparing latency, refusals and output quality per workload.
Try it yourself
Open the LiteLLM API cost calculator →
What LiteLLM does well
LiteLLM solves a real problem: one OpenAI-shaped interface over many providers. The SDK drops into an application, and the proxy runs as a shared service that accepts OpenAI-style calls and translates them to each provider's native API. Teams adopt it for a single client library across GPT, Claude, Gemini and open models, for routing and fallback rules in one place, and for spend visibility through virtual keys and budgets.
What LiteLLM does not change is who serves the model. Every request still lands on a provider account you manage, and every token still bills at that provider's rate.
The hidden cost of running a gateway
A proxy in front of every model call becomes production infrastructure. Someone must deploy it, keep its database healthy, rotate provider keys, upgrade versions, watch error rates and respond when it fails — and because all traffic passes through it, its uptime ceiling becomes yours.
A managed API such as Plugsky removes that layer. Plugsky serves 30+ models directly over an OpenAI-compatible endpoint, so there is no proxy to run and no ring of provider keys to secure. Self-serve plans are flat monthly with a free plan that includes plugsky-micro and plugsky-lite; enterprise options add VPC, on-prem and air-gapped deployment. Current plan details are on the live pricing page.
When to keep LiteLLM, when to move
Keep LiteLLM when provider flexibility is a hard requirement: existing cloud commitments, region-by-region routing with your own credentials, or a policy that demands a self-hosted control point. Those are legitimate reasons, and a managed platform cannot replace them today.
- Move when the gateway has become maintenance work rather than a product capability.
- Move when you want one endpoint and one bill instead of reconciling provider accounts.
- Combine when your gateway must stay for legacy providers while new workloads run on a managed catalogue.
Honest comparison
| Capability | Plugsky | LiteLLM self-hosted | Direct provider APIs |
|---|---|---|---|
| What it is | Managed multi-model API | Open-source gateway and SDK | One vendor per integration |
| Infrastructure | None to operate | Proxy, database and upgrades are yours | None |
| Keys and billing | One Plugsky plan | Your provider keys, provider bills | Your keys per provider |
| Model access | 30+ models in one catalogue | Whichever providers you connect | One vendor catalogue each |
| Routing | Choose a model per request | Fallbacks, retries and budgets built in | You build it |
| Private deployment | Cloud, VPC, on-prem, air-gapped | Wherever you run containers | Vendor dependent |
Frequently asked questions
What is the best LiteLLM alternative for a small team?
For a small team the main alternative is a managed multi-model API. Plugsky serves 30+ models behind one OpenAI-compatible endpoint with flat monthly plans, so there is no gateway, database or provider key ring to operate.
Can I keep using my OpenAI SDK after moving off a LiteLLM proxy?
Yes. Plugsky exposes OpenAI-compatible chat and embeddings endpoints, so you change the base URL and model names and keep your existing client code.
Does Plugsky match LiteLLM's routing features?
Partly. Plugsky lets you choose a model per request, but it does not offer provider-level fallback across third-party keys the way a self-hosted LiteLLM proxy does. Keep a gateway if cross-provider routing with your own accounts is essential.
Is there a free way to test a managed API?
Yes. The free plan includes plugsky-micro and plugsky-lite with no card required, and new accounts get a 14-day full-access trial for heavier models.
How much migration work is involved?
You change the base URL, map model aliases and re-run your tests. Applications written against an OpenAI-compatible client usually need no other code changes.
Can I deploy Plugsky privately instead of using its cloud?
Yes. Enterprise deployment options include your VPC, on-prem and air-gapped environments.
Does LiteLLM stop being useful if I move to Plugsky?
No. Many teams keep a gateway for legacy providers or cloud commitments and route new workloads to a managed catalogue.