Cerebras API

Executes LLM inference on custom Cerebras Wafer-Scale Engine (WSE) proprietary silicon architecture, delivering 2000+ tokens/second throughput by eliminating memory bottlenecks through on-die integration of compute and memory. Supports multiple model families (Llama, Qwen, GLM, GPT-OSS) with OpenAI-compatible REST API endpoints, enabling drop-in replacement for standard LLM APIs while maintaining 20-30x faster token generation compared to cloud-based alternatives.

Provides REST API endpoints following OpenAI's chat completion specification, enabling existing OpenAI SDK code to route requests to Cerebras infrastructure with minimal changes (header/endpoint URL swap). Abstracts underlying model selection across Cerebras-optimized variants (Llama 2/3, Qwen, GLM-4.7, GPT-OSS 120B, Codex-Spark) with request routing and response normalization to maintain API contract compatibility.

Routes inference requests across multiple Cerebras-optimized model families (Llama 2/3, Qwen, GLM-4.7, GPT-OSS 120B, Codex-Spark) based on model parameter in request, with backend load balancing and queue prioritization. Supports model-specific optimizations (e.g., Codex-Spark for code generation) while maintaining consistent API response format across all models.

Implements three-tier rate limiting (free, developer, enterprise) with relative quota multipliers and queue priority. Free tier provides unspecified community-supported quotas; developer tier offers 10x higher rate limits with self-serve payment ($10+/month); enterprise tier provides highest priority queue access with custom SLAs. Backend queue system prioritizes requests by tier, ensuring enterprise customers experience minimal latency variance.

Provides Codex-Spark, a Cerebras-optimized code generation model trained on programming tasks, accessible via standard API with model='codex-spark' parameter. Optimized for code completion, generation, and explanation tasks with specialized token prediction patterns for syntax-aware code output. Offered as separate subscription tier (Cerebras Code: $50-200/month) with daily token allowances (24M-120M tokens/day).

Enterprise tier enables deployment of custom model weights on Cerebras infrastructure, including fine-tuning services and on-premises/dedicated cloud deployment options. Supports model customization for domain-specific tasks (e.g., legal, medical, financial) with Cerebras-managed training pipelines. Includes dedicated support with SLA, custom queue priority, and infrastructure isolation.

Cerebras infrastructure is accessible through third-party platforms including OpenRouter (LLM aggregator), HuggingFace Hub (model marketplace), Vercel (deployment platform), and AWS Marketplace (cloud distribution). These integrations abstract Cerebras API details, enabling developers to access Cerebras models through existing workflows without direct API integration.

Cerebras inference powers voice response generation through partnerships (e.g., Tavus case study), enabling text-to-speech synthesis downstream of LLM inference. Cerebras generates text output at 2000+ tokens/second, which is then converted to speech by partner TTS systems. Enables real-time voice assistant applications with minimal latency.

Cerebras infrastructure supports agentic workflows where LLM inference never blocks agent execution, enabling agents to make decisions and take actions without waiting for slow inference. High throughput (2000+ tokens/second) and low latency enable agents to loop rapidly through reasoning, tool calling, and action execution without stalling.

Cerebras indicates model deprecation through advance notice on the pricing page, with at least one model marked 'Will be deprecated on May 27, 2026'. However, no general deprecation policy is documented — it is unclear whether this is a one-time event, the standard timeline for model deprecation, or whether customers receive migration assistance or alternative model recommendations.