Wafer AI NEW

Wafer provides serverless inference and dedicated endpoints for running open-source LLMs in production.It supports multiple models (glm-5.2, glm-5.1, kimi-k2.6 with a 262k context window, qwen 3.5, and deepseek variants) for coding, reasoning, and long-context tasks.

Serverless APIs follow the OpenAI chat completions schema and are compatible with OpenAI SDKs, LangChain, and common agent frameworks, with support for streaming, tool use, and JSON mode.Features include workload-specific inference optimization—custom GPU kernels, sharding, KV-cache tuning, and continuous-batching—and server-side caching to reduce repeated-prompt costs.

Dedicated endpoints isolate traffic, offer optional zero data retention, and provide DPA and SLA options for compliance-oriented and mission-critical deployments.The platform serves developers building agents and copilots, ML engineers optimizing inference, and enterprises requiring predictable throughput and low latency for production workloads.

Model cards and public benchmark data are available to help teams compare throughput, latency, and model capabilities for deployment planning.

Key Features

• Serverless inference for running open-source LLMs in production
• Dedicated endpoints with traffic isolation, optional zero data retention, DPA and SLA support
• Support for multiple models including long-context models (e.g., kimi-k2.6 with 262k context window)
• OpenAI-compatible APIs (chat completions schema) with streaming, tool use, JSON mode; compatible with OpenAI SDKs, LangChain, and agent frameworks
• Workload-specific inference optimizations (custom GPU kernels, sharding, KV-cache tuning, continuous-batching) and server-side caching

Use Cases

• Deploy a low-latency customer support assistant using Wafer's dedicated model endpoints and serverless inference to handle long-context conversations (entire ticket histories), stream responses to users, leverage caching for repeat queries, and enforce compliance controls for enterprise data privacy
• Build a document QA and summarization pipeline for legal, financial, or research documents by hosting long-context LLMs on Wafer, using streaming and JSON/tool modes for structured extraction, applying inference optimizations to cut costs, and exposing scalable endpoints with audit-ready compliance
• Integrate real-time personalized recommendations and in-app assistants into web and mobile products with Wafer's low-latency dedicated endpoints, OpenAI-compatible schema for easy SDK integration, endpoint caching and performance benchmarks to meet SLOs, and secure enterprise hosting for production workloads

Who is it for?

• Software developers
• Machine learning engineers
• Data scientists
• Product managers
• Devops engineers