Deploy a high-fidelity vector pooling proxy in front of your LLM pipelines. Shrink sequence lengths, expand context capability, and run inference at 10x efficiency.
Optimize your machine learning pipelines, expand effective context, and run workflows at a fraction of standard API rates.
By pooling semantic embeddings and removing redundant tokens, Wonderwall passes 80% fewer tokens to the LLM, reducing input token billing.
Smaller context windows enable faster prompt encoding and dramatically reduce Time-To-First-Token (TTFT) in time-critical agent systems.
Run Wonderwall as a local service on your own clusters (such as K3s nodes) with zero external data egress, maintaining absolute privacy.
Wonderwall operates as a middleware proxy, dynamically compressing long contexts before they hit downstream transformer models.
Unoptimized user documents, chat histories, or code files (e.g., 50,000 tokens).
Compresses the token count by 5x using semantic vector pooling. Keeps key retention near 99%.
Processes the 10,000 token output at 10x faster latency and slashes input token costs by 80%.
Select your target model and move the slider to see how much your engineering team saves with context compression.
Estimate your monthly savings when switching your LLM context input pipeline to Wonderwall.
A simple REST API and lightweight SDKs allow you to drop Wonderwall into your python, typescript, or cURL pipeline seamlessly.
import wonderwall as ww
# Initialize Wonderwall Client
model = ww.Wonderwall(api_key="ww_live_7x89fa9")
# Compress context from 50k tokens to 10k tokens
compressed_context = model.compress(
text="Your massive dataset or PDF text content...",
ratio=0.20 # 5x efficiency gain
)
# Forward compressed text directly to OpenAI/Anthropic
response = client.chat.completions.create(
model="gpt-4o",
messages=[{
"role": "user",
"content": f"Context: {compressed_context} Answer this query: ..."
}]
)Got questions about token compression, latency, or K3s self-hosting? Start a live chat now.
Find answers to common questions about context compression, performance benchmarks, and implementation.
Wonderwall is a specialized semantic embedding model that acts as an intelligent proxy in front of your LLMs. It analyzes your long context inputs, pools redundant or noise tokens together into dense vector representations, and shrinks the prompt token count by 5x to 10x before sending it to the LLM. You only pay the LLM for the compressed tokens.
Minimal impact. On key benchmarks (like Needle In A Haystack and MMLU), Wonderwall preserves 98.5%+ of facts, reasoning structures, and context retrieval accuracy. It achieves this by stripping out natural language filler and semantic redundancy while preserving the core informational load.
Any token-based language model! Because Wonderwall yields standard text tokens (or embeddings for supported models), it integrates seamlessly with OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini 1.5, and open-weight models like Llama 3.
We offer a fully managed Serverless API for instant developer integration. For enterprise workloads, Wonderwall can be self-hosted locally on your private cloud infrastructure (e.g., using lightweight Kubernetes engines like K3s) to ensure zero data egress and absolute privacy.
Sign up today to get early developer access and a free quota of 100M tokens.
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