Key features
- 100+ model providers — Anthropic, OpenAI, Gemini, AWS Bedrock, Azure, vLLM, Ollama in unified APAC API
- OpenAI-compatible proxy — single endpoint for all APAC LLM providers without application code changes
- Model fallback — automatic APAC failover between providers on errors, rate limits, or outages
- APAC cost tracking — per-request token cost attribution by APAC team, product, and user
- Virtual API keys — APAC team-scoped keys with budget limits and rate limiting
- Load balancing — distribute APAC traffic across multiple deployments of the same model
- LangFuse integration — APAC LLM observability with prompt tracing and performance analytics
Best for
- APAC platform engineering teams managing multiple LLM providers (Anthropic for APAC production, vLLM for sensitive data, Ollama for development) who need a unified API endpoint and cost visibility across all APAC AI providers
- APAC enterprises implementing AI cost governance — LiteLLM's virtual key system with budget limits enables APAC finance and platform teams to track and control AI API spending by team or product without manual invoice reconciliation
- APAC AI platform teams building provider-agnostic applications — LiteLLM's model alias routing enables switching APAC providers without application code changes, providing leverage in APAC AI provider negotiations and flexibility during provider outages
- APAC organisations combining cloud LLMs (Claude for public data) with self-hosted LLMs (vLLM for APAC sensitive data) who need a unified routing layer that directs APAC requests to the appropriate model based on data classification
Limitations to know
- ! Additional network hop — LiteLLM's proxy introduces a network hop between APAC applications and LLM providers; APAC applications with strict latency requirements should measure LiteLLM proxy overhead versus direct provider API calls
- ! Provider SDK lag — LiteLLM must track and update provider API specifications as APAC LLM providers release new features; some new Anthropic or OpenAI APAC features may have a lag before LiteLLM supports them fully
- ! Operational overhead — self-hosted LiteLLM proxy requires deployment, monitoring, and maintenance; APAC teams using only one LLM provider should evaluate whether the proxy overhead is justified relative to direct provider SDK integration
- ! Database dependency for persistence — LiteLLM's cost tracking, virtual keys, and request logging require a PostgreSQL database; APAC platform teams must provision and manage this database or use LiteLLM's managed cloud offering for persistent APAC cost data
About LiteLLM
LiteLLM is an open-source LLM API proxy and SDK that enables APAC platform engineering teams to expose a single OpenAI-compatible API endpoint that transparently routes requests to 100+ LLM providers and self-hosted models — unifying APAC LLM access across Anthropic Claude, OpenAI GPT-4, Google Gemini, AWS Bedrock (Claude, Titan, Jurassic), Azure OpenAI, Cohere, Mistral AI, Groq, self-hosted vLLM clusters, and locally running Ollama models — with a consistent request/response format, APAC usage logging, and model-level cost tracking.
LiteLLM's model routing configuration — where APAC platform engineers define a `litellm_config.yaml` listing models (mapping APAC model aliases like `apac-primary` → Claude 3.5 Sonnet on Anthropic, `apac-secondary` → Llama 3.1 70B on self-hosted vLLM, `apac-fast` → Llama 3.2 3B on Ollama) with provider credentials, rate limits, and fallback order — enables APAC application teams to use consistent model aliases while APAC platform engineers control which provider and model actually serves each APAC alias, enabling provider migration without application code changes.
LiteLLM's fallback model configuration — where APAC platform engineers define fallback chains (`apac-primary` falls back to `apac-secondary` if Anthropic API returns 429 or 500 errors, and falls back to `apac-fast` if secondary is unavailable) — enables APAC applications to maintain service availability during APAC cloud AI provider outages, rate limit exhaustion, or regional API degradation events without APAC application-level error handling for provider failures.
LiteLLM's APAC cost tracking — where LiteLLM logs every APAC LLM API call with the model used, token counts (prompt and completion), calculated cost (using provider pricing tables), APAC user identifier, and response latency to a configurable backend (PostgreSQL, Redis, or Langfuse) — enables APAC platform engineering and finance teams to attribute AI API costs by APAC team, product, or user, implementing APAC cost centre billing for shared LLM infrastructure without requiring individual APAC applications to implement cost tracking.
LiteLLM's virtual key system — where APAC platform engineers issue virtual API keys to APAC development teams that map to specific LiteLLM models (not raw provider credentials), with configurable token budget limits, rate limits, and allowed models per key — enables APAC platform teams to provide self-service LLM API access to APAC development teams with budget guardrails, preventing individual APAC teams from exceeding allocated AI spending without central approval.
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