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.
LLM API Pricing
Pay-per-token pricing. Prices shown per 1M tokens.
Prices last updated: August 13, 2026
Wafer pricing by model
Input, output, and batch rates, plus alternatives, for one model at a time.
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
What GPU types does Wafer offer?
Check the pricing table above for Wafer's current GPU availability and pricing.
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.
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