Tokenwise

Key Features

• •Real-Time Cost Dashboard: See every LLM call's cost, latency, tokens, and errors sliced by model, app, or tag. A 14-day forecast at the top prevents spend surprises. This is the foundation for understanding exactly where your money goes.
• •One-Click Optimization Recommendations: Tokenwise analyzes real traffic against cheaper models, finds cache opportunities, and flags bloated prompts. Each recommendation includes a replay-check against your quality baseline before you apply it, so you switch with proof, not hope.
• •Semantic Cache with Zero Configuration: The proxy caches repeated near-identical calls automatically at the edge for under 50ms overhead. Cache hits reduce latency and cost without any manual tuning, ideal for endpoints like /faq or /classify that receive similar requests.
• •LLM-as-Judge Evaluation Engine: Automatically score model outputs against golden answers to validate quality after a model switch. This engine powers the regression detector and ensures a cost cut never silently degrades user experience.
• •Auto-Rollback Watchdog: Set budget caps and quality thresholds. If a cost spike or quality drop exceeds limits, the watchdog automatically reverts to the last known-good configuration. This protects production from unintended consequences of optimization changes.
• •Multi-Workspace Support with Role Tiers: Manage up to 50 workspaces (Pro plan) with four role tiers for team members. Isolate environments for development, staging, and production, and control access per team member.
• •Weekly Insights Digest: Every Monday, receive a personalized email summarizing metrics, detected waste, and actionable recommendations. This keeps teams informed without needing to log into the dashboard daily.

Use Cases

• •Reducing Oversized System Prompt Costs: A team had a 2,140-token instruction block sent on every request, even when the instructions were never used. Tokenwise flagged the waste and recommended trimming the prompt, saving 38% of their bill with no quality impact.
• •Caching Repetitive Classification Calls: An app making thousands of identical /classify-intent calls every day was re-billed in full each time due to cache misses. Enabling Tokenwise's semantic cache achieved a 71% hit rate, reducing latency by 180ms and cutting monthly spending by 21%.
• •Switching to a Cheaper Model with Quality Confidence: A /summarize endpoint running on Opus was identified as a candidate for Haiku. Tokenwise's replay-check showed a 96% quality match, and applying the one-click swap saved $842 per month without degrading summaries.
• •Setting Budget Caps to Prevent Cost Spikes: A production workspace experienced an 84% spend increase in one hour due to abnormal traffic. Tokenwise's budget cap triggered an auto-rollback to the previous configuration, preventing overshoot and alerting the team via Slack.
• •Running A/B Traffic Splits for Model Changes: Before rolling out a model switch to 100% of users, a team used Tokenwise to send 5-50% of traffic to the new model. LLM-as-judge scored outputs during the experiment, providing confidence to scale up without manual monitoring.

Who is this for?

Developers and small teams running LLM features in production—apps and SaaS that call OpenAI, Anthropic, Google, or other model APIs through Vercel AI SDK, LangChain, or plain SDK. Ideal for solo makers and startups with monthly LLM bills between $50 and $2,000 who need real-time visibility into spending, one-click optimizations, and quality safeguards. Also suited for teams using multiple providers or managing multiple workspaces and looking to reduce waste without redeploying code.