Tokenwise
Tokenwise is a drop-in LLM proxy that provides real-time visibility into every LLM call, including cost, latency, errors, and quality. It operates with less than 50ms overhead and does not store provider keys, ensuring security and minimal performance impact. By default, it runs in observe-only mode, requiring no production changes. The platform automatically identifies common sources of LLM cost waste, such as oversized system prompts, unnecessary overuse of expensive models, and cache misses. It then provides one-click fixes—like model swaps, semantic caching, and prompt trims—each replay-checked against a user-defined quality baseline to ensure no degradation. In addition to cost optimization, Tokenwise offers proactive monitoring with budget caps, latency and cost spike alerts, and LLM-as-judge scoring to prevent regressions. It supports all major LLM providers and works with existing SDKs by simply changing the base URL, making integration seamless for any AI-powered application.
- Category
- LLM
- Pricing
- Free
Problem solved
Tokenwise addresses the hidden waste in LLM spending—prompts loaded but unnecessary, calls that could be cached, and expensive models doing cheap work—by revealing where money is leaking and providing safe, quality-checked fixes to reduce costs by 20–30% without compromising output.
Use cases
- Monitor LLM costs, latency, and errors across models and applications
- Identify and quantify wasted spend from prompt inefficiencies and cache misses
- Apply one-click optimizations like model downgrades and semantic caching
- Set budget caps and receive alerts for cost spikes or latency regressions
- Ensure quality is not degraded after optimizations via LLM-as-judge scoring
- Route all LLM calls through a single proxy for simplified multi-provider management
Audience
- Developers
- ML Engineers
- Data Scientists
- Startups
- Enterprise
Pros
- Drop-in integration with a single line change, no SDK rewrite
- Observe-only default mode requires zero production changes initially
- Low overhead (<50ms) and keys never stored for security
- One-click fixes backed by quality evaluations before application
- Real-time dashboard, alerts, and budget caps for cost control
Cons
- Requires trust in a third-party proxy to handle all LLM traffic (data in transit)
- Pricing beyond early adopter plan may be unclear or increase
- Dependent on continuous service uptime; a proxy outage could block LLM calls
- Limited to model providers accessible via OpenAI-compatible API; custom LLM setups may not work
- Optimization effectiveness relies on sufficient traffic for replay analysis
Official website: https://tokenwisehq.com/
Canonical LambdaBase page: https://www.lambdabase.com/tools/tokenwise