Run open-source models like Llama 3, DeepSeek, Whisper, and Kokoro with OpenAI SDK compatibility, ultra-fast TTFT, and predictable pay-as-you-go pricing.
< 100ms
median first-token latency
100%
OpenAI SDK drop-in compatibility
99.99%
API availability uptime target
10x
lower cost than legacy APIs
Low pay-as-you-go pricing with no long contracts, hidden fees, or minimum commitment surprises.
Optimized for latency, throughput, and serverless reliability with instant multi-region failover.
Your prompts, outputs, and telemetry data remain strictly private with SOC-2 standard security controls.
High-performance serverless APIs for LLMs, Text-to-Image, Audio Synthesis, and Vector Embeddings.
Llama 3.3, DeepSeek R1, Qwen & Mistral with sub-100ms TTFT.
Flux.1, Stable Diffusion XL & Turbo for 8K photorealistic generation.
"Cyberpunk Neural Grid, 8K, HDR"
Kokoro TTS & OpenAI Whisper for speech synthesis & transcription.
High-dimensional vector embeddings for RAG & semantic search.
See how our serverless inference cloud compares to legacy API providers on speed, cost, and developer experience.
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ACE-Step/acestep-v15-xl-sft
ACE-Step v1.5 is a powerful open-source music foundation model that turns a text prompt into a complete song — vocals, lyrics, and instrumentation — at quality that rivals commercial tools. We run the high-quality XL checkpoint with its planning step ("thinking") on by default, so generations favor musical structure and coherence over raw speed.
sentence-transformers/all-MiniLM-L12-v2
We present a sentence transformation model that generates semantically similar sentences. Our model is based on the Sentence-Transformers architecture and was trained on a large dataset of sentence pairs. We evaluate the effectiveness of our model by measuring its ability to generate similar sentences that are close to the original sentence in meaning.
sentence-transformers/all-MiniLM-L6-v2
We present a sentence transformation model that achieves state-of-the-art results on various NLP tasks without requiring task-specific architectures or fine-tuning. Our approach leverages contrastive learning and utilizes a variety of datasets to learn robust sentence representations. We evaluate our model on several benchmarks and demonstrate its effectiveness in various applications such as text classification, sentiment analysis, named entity recognition, and question answering.
sentence-transformers/all-mpnet-base-v2
A sentence transformation model that has been trained on a wide range of datasets, including but not limited to S2ORC, WikiAnwers, PAQ, Stack Exchange, and Yahoo! Answers. Our model can be used for various NLP tasks such as clustering, sentiment analysis, and question answering.
BAAI/bge-base-en-v1.5
BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned
BAAI/bge-en-icl
A LLM-based embedding model with in-context learning capabilities that achieves SOTA performance on BEIR and AIR-Bench. It leverages few-shot examples to enhance task performance.
BAAI/bge-large-en-v1.5
BGE embedding is a general Embedding Model. It is pre-trained using retromae and trained on large-scale pair data using contrastive learning. Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned
BAAI/bge-m3
BGE-M3 is a versatile text embedding model that supports multi-functionality, multi-linguality, and multi-granularity, allowing it to perform dense retrieval, multi-vector retrieval, and sparse retrieval in over 100 languages and with input sizes up to 8192 tokens. The model can be used in a retrieval pipeline with hybrid retrieval and re-ranking to achieve higher accuracy and stronger generalization capabilities. BGE-M3 has shown state-of-the-art performance on several benchmarks, including MKQA, MLDR, and NarritiveQA, and can be used as a drop-in replacement for other embedding models like DPR and BGE-v1.5.
BAAI/bge-m3-multi
BGE-M3 is a multilingual text embedding model developed by BAAI, distinguished by its Multi-Linguality (supporting 100+ languages), Multi-Functionality (unified dense, multi-vector, and sparse retrieval), and Multi-Granularity (handling inputs from short queries to 8192-token documents). It achieves state-of-the-art retrieval performance across diverse benchmarks while maintaining a single model for multiple retrieval modes.
Every endpoint is continuously benchmarked for first-token latency, token throughput, and payload security.
2.42M+
tokens processed per second
98ms
median time to first token
99.99%
system operational status
10x
average infrastructure savings
Get your API key in seconds and run high-speed inference for Llama 3, DeepSeek, and Whisper with zero upfront contracts.
sentence-transformers/all-MiniLM-L6-v2
$0.005 / 1M tokens
