Side-by-side

SkyAIApp vs OpenRouter / LiteLLM / LangChain

Honest comparison — we'll point out where the others are stronger, too. Then we give a one-page migration recipe from each.

Feature matrix

Capability	SkyAIApp	OpenRouter	LiteLLM	LangChain
Unified multi-provider API
Goal × Strategy decision model
Server-side semantic cache
Auto fallback chains
Per-request budget caps
Built-in PII detection / redaction
Prompt-injection defense
Agent runtime (sandbox + retry + state)
End-to-end traces in dashboard
OpenTelemetry export
Webhooks for ops events
SSO/SAML + RBAC
SOC 2 / ISO readiness material
Self-hosted deployment
Open-source SDK
Pricing model	Subscription + usage	Usage-only (markup)	OSS (free self-host)	OSS + paid LangSmith

Full support Partial / needs extra setup Not supported

When NOT to use SkyAIApp

You only need one provider (e.g. pure OpenAI)
Use the provider SDK directly. SkyAIApp's value is multi-model + governance + FinOps, not a wrapper.
You need local-only inference (no internet)
We route public APIs. For local inference look at Ollama / vLLM / Text Generation Inference.
Academic research or one-off scripts
Complexity premium isn't worth it. Just curl or use one SDK.
You're happy in LangGraph
LangGraph's state machine is genuinely good. Migration may cost more than it saves. Let LangGraph call SkyAIApp inside a single node (for multi-model routing + guardrails).

Migrate from OpenRouter

OpenRouter's chat completions API is OpenAI-compatible. SkyAIApp provides an OpenAI-compatible adapter endpoint — migration is mostly a base URL + key swap.

// Before — OpenRouter
const openrouter = new OpenAI({
  baseURL: "https://openrouter.ai/api/v1",
  apiKey:  process.env.OPENROUTER_API_KEY,
});
const res = await openrouter.chat.completions.create({
  model: "anthropic/claude-opus-4.7",
  messages: [{ role: "user", content: "..." }],
});

// After — SkyAIApp (OpenAI-compatible adapter)
const sky = new OpenAI({
  baseURL: "https://api.skyaiapp.com/v1/openai",   // SkyAIApp OpenAI-compat endpoint
  apiKey:  process.env.SKYAIAPP_API_KEY,
});
const res = await sky.chat.completions.create({
  model: "claude-opus-4.7",                       // same model name w/o the "anthropic/" prefix
  messages: [{ role: "user", content: "..." }],
});

// 👉 Or take the full upgrade — declarative goal/strategy:
import { SkyAI } from "@skyaiapp/sdk";
const native = new SkyAI({ apiKey: process.env.SKYAIAPP_API_KEY });
const res = await native.route({
  goal: "quality",
  strategy: "quality-first",
  fallback: { models: ["claude-opus-4.7", "gpt-5.5-pro"], maxRetries: 2 },
  budget:   { maxCostUsd: 0.05 },
  cache:    true,
  messages: [{ role: "user", content: "..." }],
});

Migrate from LiteLLM

LiteLLM's `completion()` API is structurally close to ours. The biggest difference is that SkyAIApp treats goal/strategy as first-class parameters (instead of pre-configured router yaml).

# Before — LiteLLM with router yaml
from litellm import Router
router = Router(model_list=load_yaml("litellm.yaml"), fallbacks=[...])
res = router.completion(model="claude-opus-4.7", messages=[...])

# After — SkyAIApp (goal/strategy inline; no yaml needed)
from skyaiapp import SkyAI
sky = SkyAI(api_key=os.environ["SKYAIAPP_API_KEY"])
res = await sky.route(
    goal="quality",
    strategy="quality-first",
    fallback={"models": ["claude-opus-4.7", "gpt-5.5-pro"], "maxRetries": 2},
    budget={"max_cost_usd": 0.05},
    cache={"enabled": True},
    messages=[...],
)

# Want to keep policies in version control like LiteLLM yaml?
# → Define policy in console, then reference by ID:
res = await sky.route(policy_id="policy_prod_v3", messages=[...])

Migrate from LangChain

LangChain is an abstraction layer. We recommend two paths:

Keep LangChain for business orchestration; use SkyAIApp as the underlying LLM provider (recommended for existing LangChain projects).
Replace LangChain with SkyAIApp Agent runtime (recommended for new projects; the LangChain abstraction tax is usually bigger than the actual code).

# Path 1 — keep LangChain, swap the LLM
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
    model="claude-opus-4.7",
    base_url="https://api.skyaiapp.com/v1/openai",
    api_key=os.environ["SKYAIAPP_API_KEY"],
)
chain = prompt | llm | parser

# Path 2 — go native with SkyAIApp Agent runtime
from skyaiapp import SkyAI, define_tool
sky = SkyAI(api_key=os.environ["SKYAIAPP_API_KEY"])

agent = sky.create_agent(
    tools=["web_search", lookup_invoice],
    max_steps=10,
    model_strategy={"goal": "quality", "strategy": "balanced"},
)
result = await agent.run(task="...")

Where the others win (honestly)

OpenRouter

Pure usage billing, no monthly fee, instant signup, very developer-friendly for individuals.

LiteLLM

Fully OSS, fully self-hostable, some enterprises prefer 'I can see all the code'.

LangChain

Massive ecosystem (200+ integrations), tons of resources, active community; LangGraph's state machine abstraction is very strong for complex agents.

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