Wafer
The fastest open source LLMs for enterprise
Last reviewed Jul 7, 2026
Wafer is a San Francisco-based inference provider serving open-source LLMs through a serverless API and dedicated endpoints. Its platform uses AI agents to profile workloads and optimize the model, serving engine, kernel, and hardware combination for each deployment.
Wafer does not publish public hourly pricing.
Contact the provider directly for a quote.
Pros & Cons
Advantages
- High-throughput serving with results independently benchmarked on Artificial Analysis
- Prompt caching with discounted cached-input rates
- Dedicated endpoints set up in under 24 hours
- Runs across NVIDIA, AMD, and AWS Trainium accelerators rather than a single vendor
Limitations
- Small serverless model catalog centered on the GLM family
- Inference only - no GPU rental, training, or fine-tuning hosting
- Early-stage company with a smaller ecosystem than established inference providers
Key Features
Serverless Inference
Pay-as-you-go API access to hosted open-source models including GLM, Kimi, Qwen, and DeepSeek with no infrastructure management
Dedicated Endpoints
Custom-tuned inference deployments with performance guarantees, provisioned in under 24 hours
Automated Performance Optimization
Agents profile inference bottlenecks and tune across serving engines (vLLM, SGLang, TensorRT-LLM), custom kernels (CUDA, HIP, Triton, NKI), quantization (FP8/FP4), and decode strategies
Multi-Accelerator Hardware
Workloads run on NVIDIA B200/B300, AMD MI350X/MI355X, and AWS Trainium depending on the model and traffic shape
OpenAI- and Anthropic-Compatible API
OpenAI-compatible endpoint at pass.wafer.ai/v1 and Anthropic-compatible endpoint at pass.wafer.ai/v1/messages, both using Bearer token authentication
Prompt Caching
Cached input tokens are billed at reduced rates on supported models
Zero Data Retention
Request-scoped ZDR available on direct API calls via a Wafer-ZDR header for privacy-sensitive workloads
Pricing Options
| Option | Details |
|---|---|
| Pay-per-token | Prepaid credits with separate input and output token rates per model and no subscription |
| Cached input | Reduced rates for cached input tokens on supported models |
| Dedicated endpoints | Custom pricing for dedicated deployments with tuned performance targets, arranged with the sales team |
Availability & Support
Support
Documentation at docs.wafer.ai, email support (hi@wafer.ai), and scheduled onboarding calls for enterprise
Getting Started
- 1
Create an account
Sign up at app.wafer.ai and load credits for pay-as-you-go usage
- 2
Generate an API key
Create a key in the console and pass it as a Bearer token
- 3
Make your first request
Call the OpenAI-compatible endpoint at pass.wafer.ai/v1 with a model from the serverless catalog
Frequently Asked Questions
Which models does Wafer serve?
Check the pricing table above for the models Wafer currently serves and their per-token rates.
How do I get started with Wafer?
Create an account, Generate an API key, Make your first request
What are Wafer's main advantages?
Wafer's main advantages include: High-throughput serving with results independently benchmarked on Artificial Analysis, Prompt caching with discounted cached-input rates, Dedicated endpoints set up in under 24 hours, Runs across NVIDIA, AMD, and AWS Trainium accelerators rather than a single vendor.
What are Wafer's limitations?
Wafer's main limitations include: Small serverless model catalog centered on the GLM family, Inference only - no GPU rental, training, or fine-tuning hosting, Early-stage company with a smaller ecosystem than established inference providers.