Key features
- OpenAI-compatible API: APAC drop-in replacement for ChatCompletion and embedding calls
- 50+ open-source models: Llama 3, Mistral, Mixtral, Whisper, SDXL on one platform
- Per-token pricing: APAC pay-per-use with no minimum commitment or reserved capacity
- Multilingual models: APAC language embedding and generation model support
- Serverless: APAC teams use inference without GPU provisioning or scaling management
- Model switching: APAC benchmark and swap models without application code changes
Best for
- APAC AI engineering teams building applications on open-source LLMs who need low-cost serverless inference without GPU infrastructure — particularly APAC startups with cost sensitivity and applications where Llama 3 or Mistral quality is sufficient to replace more expensive closed-model providers.
Limitations to know
- ! No fine-tuning — APAC custom model deployment requires separate infrastructure
- ! Cold start latency on low-traffic APAC endpoints vs dedicated GPU instances
- ! APAC data residency: cloud-only with US-based infrastructure, not APAC-regional
About DeepInfra
DeepInfra is a serverless LLM inference platform giving APAC AI teams API access to over 50 open-source models at competitive per-token pricing — including Llama 3 (8B and 70B), Mistral 7B, Mixtral 8x7B, Whisper (speech-to-text), and text embedding models. APAC teams building applications on open-source LLMs use DeepInfra to avoid the infrastructure overhead of running GPU servers while maintaining access to models they can switch between without vendor lock-in.
DeepInfra's OpenAI-compatible API means APAC applications written for OpenAI's ChatCompletion interface switch to DeepInfra models by changing the base URL and model name — no SDK changes required. APAC teams use this compatibility to benchmark open-source alternatives against GPT-4o-mini on their specific tasks and switch to cheaper open-source models where quality is comparable.
DeepInfra's pricing positions it as one of the lowest-cost options for open-source LLM inference in the APAC market — Llama 3 70B inference costs significantly less per million tokens than comparable closed-model APIs. APAC startups with high inference volume and cost sensitivity use DeepInfra to reduce LLM API costs by 5–10x versus closed-model providers for tasks where Llama 3 quality is sufficient.
DeepInfra also hosts specialized APAC-relevant models including multilingual embedding models (useful for APAC language RAG pipelines) and Whisper variants for APAC audio transcription workloads. APAC teams building multilingual applications use DeepInfra as a unified inference endpoint for both text generation and audio processing without managing separate GPU infrastructure for each model type.
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