Tokenwise

What is Tokenwise?

Tokenwise is an LLM observability and cost optimization product that sits in front of existing model APIs as a drop-in proxy. It gives teams production visibility into each LLM call, including cost, latency, errors, tokens, and quality signals, so they can find waste and reduce spend without rewriting their application stack.

The product is designed to work with existing SDKs and providers. According to the site, it supports one-line setup, keeps provider keys stored on the customer side, defaults to observe-only mode, and adds under 50ms of overhead. It also supports optimization workflows such as model switching, caching, and prompt trimming, with replay checks against a quality baseline before changes are applied.

Key Features

• Drop-in proxy for LLM traffic — Point your app at Tokenwise instead of changing application logic, which keeps adoption lightweight and avoids an SDK rewrite.
• Per-call observability — Track cost, latency, errors, tokens, and quality for each call so teams can see where spend and performance issues originate.
• Cost leak detection — The product flags patterns such as oversized prompts, cache misses, prefix invalidations, and expensive models used for simpler work.
• Optimization recommendations with replay checks — Tokenwise suggests fixes such as model swaps, prompt trims, and caching changes, then checks them against your quality baseline before you apply them.
• Monitoring and alerting — It can surface cost spikes, latency regressions, and quality dips and route alerts to email, Slack, or Discord.
• Existing SDK compatibility — The site shows usage with a standard OpenAI-style client and a base URL swap, indicating it is designed to work with current provider workflows.

How to Use Tokenwise

A typical setup starts by pointing your app’s LLM client at the Tokenwise proxy and adding the required key or header. From there, the dashboard begins showing live usage, cost, and latency data without requiring a production rewrite.

Teams then review the dashboard to identify where money is being spent, inspect recommendations, and choose whether to apply suggested fixes such as model changes, prompt reductions, or caching. If they enable protections, Tokenwise can also watch for regressions and alert the team when spend, latency, or quality moves outside expected bounds.

Use Cases

• Cutting unnecessary model spend — An engineering team can review which prompts, models, or routes are driving the largest share of monthly LLM cost and apply targeted reductions.
• Finding cache opportunities — Teams with repeated or near-identical requests can detect cache misses and prefix invalidations, then enable caching where the traffic pattern supports it.
• Choosing cheaper models for routine tasks — A team can compare quality matches between models and switch simpler workloads from a more expensive model to a lower-cost one when replay checks show acceptable results.
• Monitoring production LLM behavior — Operators can watch live traffic to understand cost, latency, errors, and token usage across apps or tags.
• Protecting quality during optimization — Teams that are actively tuning prompts or models can use rollback-style safeguards and regression alerts to avoid silent output degradation.

FAQ

Does Tokenwise require a rewrite of my app or agent stack? No. The site says it is a drop-in proxy and that you can keep your existing SDK, changing the base URL rather than rewriting the integration.

Does it work in observe-only mode? Yes. The page says observe-only is the default, so teams can start by monitoring before turning on optimization actions.

How quickly can it be set up? The site says you can start free and see spend in about 5 minutes, with one-line setup described in the product messaging.

Are provider keys stored by Tokenwise? The page states that provider keys are never stored, which suggests it is designed to avoid holding your upstream credentials.

What kinds of optimization actions does it suggest? The site mentions model swaps, caching, and prompt trims, along with replay checks against a quality baseline before applying a recommendation.

Alternatives

• Native provider dashboards — Cloud model providers often offer their own usage and billing views, but these are typically limited to a single provider rather than a cross-provider proxy workflow.
• General observability platforms — Broader monitoring tools can track application or infrastructure metrics, but they may not inspect prompt-level LLM traffic or suggest model-specific fixes.
• Custom internal logging and analysis — Some teams build their own middleware and reporting pipelines to measure cost and quality, but that approach usually requires more engineering effort and maintenance.
• LLM experimentation or eval tools — These tools are useful for testing prompts and models, but they are usually centered on evaluation workflows rather than continuous production cost monitoring and proxying.