Tokenwise

Product Introduction

1. Definition: Tokenwise is a LLM proxy and cost optimization platform that functions as a one-line, OpenAI-compatible baseURL integration. It acts as an intelligent intermediary layer between AI applications (like coding agents or custom apps) and Large Language Model (LLM) providers.
2. Core Value Proposition: Tokenwise provides real-time LLM observability, cost analysis, and one-click optimization for developers and small teams. Its primary purpose is to identify and eliminate waste in LLM API spending by analyzing production traffic, not just public benchmarks, and applying verified fixes that maintain output quality.

Main Features

1. Drop-in Proxy with Zero Key Storage: Technically, it's a high-performance edge proxy running on Cloudflare Workers across 300+ cities, adding less than 50ms of median latency. It works by having you change a single baseURL in your existing OpenAI/Anthropic SDK. It forwards API keys to the provider and immediately drops them from memory, never persisting provider keys to disk or logs, ensuring a secure BYOK (Bring Your Own Key) model.
2. Production Traffic Analysis & Cost Leak Detection: The system logs every request's metadata—model, token count, latency, and status—to a dashboard that provides granular visibility. It uses this data to automatically identify common cost inefficiencies, such as oversized prompts sent with every call (bloated system prompts), cacheable requests that aren't being cached, and expensive models (like Claude Opus) being used for tasks where a cheaper model (like Claude Haiku) achieves equivalent quality.
3. One-Click Optimization with Quality Verification: For each identified inefficiency, Tokenwise generates a specific, actionable fix (e.g., "Trim 2,140-token system prompt," "Enable semantic cache for /classify-intent," "Switch model from Opus to Haiku on /summarize"). Crucially, before presenting the fix, it performs an LLM-as-judge evaluation by replaying your recent, real traffic against the proposed change to verify that output quality meets your baseline. You can apply the fix directly, which updates your proxy routing rules instantly without code redeployment.
4. Intelligent Monitoring and Protection: It includes an automated "Watchdog" feature for quality regression detection. If a cost-saving change causes a significant quality dip or latency spike (configurable thresholds), it can auto-rollback to the last known-good configuration. It also supports daily and monthly budget caps and sends real-time alerts via email, Slack, or Discord for cost anomalies.

Problems Solved

1. Pain Point: The core problem is LLM bill opacity and runaway costs. Development teams often lack visibility into where in their application specific costs are incurred, leading to inefficient resource allocation, paying premium prices for routine tasks, and an inability to track the ROI of their AI features.
2. Target Audience: The primary users are Solo developers, indie hackers, and small engineering teams (2-10 people) shipping AI-powered applications. Specific personas include builders using frameworks like the Vercel AI SDK, developers using AI coding agents (Claude Code, Cursor, Codex), and founders of startups where the monthly LLM bill falls in the $50 to $2,000 range and directly impacts burn rate.
3. Use Cases: Essential scenarios include: Debugging an unexpected spike in an Anthropic or OpenAI bill, validating the cost impact before rolling out a new AI feature to all users, optimizing the cost structure of an app that handles millions of requests, and managing budgets when using expensive frontier models (like Opus) alongside cheaper ones.

Unique Advantages

1. Differentiation: Unlike simple logging tools (Helicone, LangSmith) or development-focused observability platforms (Langfuse), Tokenwise is an active optimization layer. Its key differentiator is the closed-loop process: it doesn't just show you data; it analyzes it to propose specific financial and technical fixes, verifies those fixes against your own quality standards, and enables one-click deployment—all without requiring SDK rewrites or production redeployments. Its proxy overhead (~37ms median) is also significantly lower than SDK-based alternatives.
2. Key Innovation: The core innovation is the "Traffic Replay for Quality Evaluation" system. It uses your own production traffic as the test dataset for its LLM-as-judge scoring. This means optimization recommendations (like switching from Opus to Haiku) are validated against your specific prompts and quality requirements, not generic public benchmarks, ensuring cost savings don't silently degrade your user experience.

Frequently Asked Questions (FAQ)

1. How long does it take to set up Tokenwise and see my cost data? Setup takes approximately 5 minutes. For most developers, it involves changing one line of code—the baseURL in their OpenAI or Anthropic SDK—to point to https://proxy.tokenwisehq.com/openai/v1. Once traffic starts flowing through the proxy, the dashboard populates with real-time cost, latency, and token analytics, allowing you to see exactly where your money is going.

2. Is my data and API key secure with a third-party proxy? Security is architected with a zero-persistence model for sensitive data. Your provider API keys (OpenAI, Anthropic) are never stored on disk; they are forwarded to the upstream provider and dropped from memory immediately. Prompts and completions are encrypted at rest. Your proxy access key is hashed before storage. You also retain full control and can enable opt-out of payload storage per workspace.

3. What specific cost savings can I realistically expect with Tokenwise? The savings are highly variable based on your traffic patterns. Based on the product's metrics, early teams have documented average savings of 20-30% on their LLM bills in the first week. Common savings come from enabling semantic caching for repetitive queries, trimming oversized system prompts, and intelligently routing requests to cheaper models like Haiku or GPT-4o-mini where quality is equivalent.

4. Does Tokenwise work with models other than OpenAI? Yes, it has native path support for Anthropic Claude, Google Gemini (via an OpenAI-compatible shim), xAI Grok, Groq, DeepSeek, Mistral, and OpenRouter (which adds access to 200+ models like Meta Llama, Cohere, and Perplexity). The proxy is compatible with standard SDKs from these providers, all accessible through the same simple base URL change.

5. What happens if I stop paying or cancel my subscription? The system is designed to be non-disruptive. If you cancel, your subscription will not renew at the end of the billing period. The proxy will continue to forward requests normally during your paid term. After that, you would need to reconfigure your application to point back directly to your LLM provider's API endpoint. All your historical analytics and optimization rules would be archived.