wafer

Key Features

• •Serverless inference offers pay-as-you-go access to top open models like GLM-5.2 and Qwen 3.5 without infrastructure management, providing fast APIs with transparent per-million-token pricing for input, output, and cached content.
• •Dedicated endpoints deliver isolated infrastructure for mission-critical workloads with low-latency responses for voice agents, high-throughput scaling for batch processing, and reliability at scale with SLA-backed uptime and predictable performance.
• •Workload-specific optimization tunes inference around custom models, hardware accelerators (AMD/NVIDIA), traffic patterns, and production constraints, profiling each model and implementing custom GPU kernels for maximum performance.
• •Automatic server-side caching provides approximately 10× cost savings on repeated prompt prefixes without configuration flags, particularly beneficial for long system prompts, multi-turn conversations, and document-heavy RAG applications.
• •OpenAI Chat Completions schema compatibility allows existing AI clients—including OpenAI SDK, LangChain, and LiteLLM—to work seamlessly as drop-in replacements, supporting streaming, tool use, and JSON mode across all serverless models.
• •Enterprise security features include dedicated endpoints with zero data retention options for compliance-bound workloads, signed DPAs, SLA-backed service levels, and isolation from shared inference pools for sensitive data processing.
• •Performance optimization achieves benchmark-leading speeds of 288.5 tokens per second on Qwen 3.5 397B-A17B through continuous-batching scheduler tuning, expert sharding for cache hierarchy fitting, and custom GPU kernel implementation.
• •Rapid provisioning enables custom-tuned deployments for dedicated endpoints in under 24 hours, allowing enterprises to quickly scale production AI workloads with hardware-specific optimization and workload-aware configuration.

Use Cases

• •Voice agents and intelligent copilots utilize low-latency dedicated endpoints for real-time interactive AI products that require lightning-fast responses and natural conversation flow in customer-facing applications.
• •Coding agents and batch processing workloads leverage high-throughput capabilities to scale parallel generations without bottlenecks, handling large volumes of code generation, analysis, and automated development tasks efficiently.
• •Document-heavy RAG systems benefit from automatic cache hits on repeated prompt prefixes, significantly reducing costs for applications processing similar documents or maintaining consistent context across multiple queries.
• •Enterprise applications with sensitive data or compliance requirements use dedicated endpoints with zero data retention, signed DPAs, and isolated infrastructure to meet regulatory standards while maintaining AI capabilities.
• •Teams migrating from OpenAI API implement Wafer as a drop-in replacement with existing SDKs and workflows, accessing faster open models at competitive pricing while maintaining compatibility with current development tools.
• •Mission-critical production workloads rely on SLA-backed uptime, predictable performance, and workload-specific optimization for stable AI operations in financial, healthcare, or other high-stakes industry applications.

Who is this for?

Wafer targets enterprise developers and AI product teams building production AI applications, particularly those with mission-critical workloads requiring dedicated endpoints. It serves compliance-bound organizations in regulated industries needing data isolation and zero retention options, companies scaling batch processing or real-time AI interactions, and technical teams seeking serverless simplicity with OpenAI API compatibility. The platform appeals to organizations prioritizing performance benchmarks, cost efficiency through automatic caching, and rapid deployment of custom-tuned inference infrastructure.