Drop-in proxy integration
Point an existing app at Tokenwise’s proxy by changing the base URL and sending a live key. The docs describe this as a one-line integration with no client library to install and no SDK to maintain.
Tokenwise
Tokenwise is an LLM observability and cost optimization proxy for production apps. It helps teams monitor usage, detect waste, and apply guarded cost-saving changes without rewriting their SDK integration.
What Tokenwise does
Tokenwise is an LLM observability and cost optimization proxy that sits between your application and model providers. It is designed to give teams production visibility into cost, latency, errors, tokens, and quality while keeping the existing SDK and routing model in place.
The product’s core workflow is to observe traffic, identify waste, and suggest or apply changes such as model swaps, caching, prompt trims, fallback chains, and A/B splits. Tokenwise emphasizes a low-friction setup, observe-only behavior by default, and replay-checked recommendations so cheaper output does not silently degrade quality.
Core capabilities
Real-time request observability
Track cost, tokens, latency, errors, and quality for each call, with views sliced by model, app, or tag. The dashboard also shows a 14-day forecast and live updates as traffic changes.
Waste detection and savings estimates
Detect common sources of LLM waste such as oversized prompts, cache misses, and expensive model choices. Tokenwise quantifies the likely savings and shows recommendations on the traffic you actually send.
Optimization rules engine
Apply model switches, caching, prompt trims, fallback chains, A/B splits, and tag overrides from the rules engine. The product says these changes can be replay-checked against your quality baseline before rollout.
Guardrails and alerting
Protect production with alerts for spend spikes, latency regressions, and quality dips. The security and docs pages also describe rollback, webhook delivery, and notification routes such as email, Slack, and Discord.
Broad provider coverage
Support multiple providers through OpenAI-style or provider-specific paths, including OpenAI, Anthropic, Google Gemini, xAI Grok, OpenRouter, Groq, DeepSeek, and Mistral. The docs also mention Cohere, Together, Fireworks, Perplexity, and Bedrock.
Common ways teams use Tokenwise
Track where the bill comes from
Use Tokenwise to see which models, prompts, or endpoints are driving spend, then break costs down by app or tag instead of relying on provider dashboards alone.
Find and reduce waste in live traffic
Review recommendations for model swaps, caching, and prompt trimming when traffic volume makes manual cost analysis too slow.
Protect production workflows
Set alerts for cost spikes, latency regressions, and quality dips so the team can respond before users notice a production issue.
Add observability without a rewrite
Adopt the proxy without changing application code structure beyond the endpoint and headers, which suits teams that already use OpenAI-style SDKs.
Roll out cheaper configurations with guardrails
Use the rules engine and replay checks to test cheaper configurations against a quality baseline before rolling them into production.
Pros and Cons
Pros
- One-line setup through an HTTP proxy with no client library to install.
- Observability covers cost, latency, errors, tokens, and quality in one place.
- Optimization recommendations are tied to actual traffic and can be replay-checked against a baseline.
- Supports a wide range of providers and common SDKs without forcing a LangChain-only workflow.
- Security controls include encrypted storage, hashed access keys, and BYOK handling for provider keys.
Cons
- Some advanced features depend on payload storage, which can be disabled at the workspace or tag level.
- The site is clear that Tokenwise is too early for a SOC 2 attestation.
FAQ
How does setup work?
Tokenwise is an HTTP proxy that sits between your app and LLM providers. The docs say you can point an existing SDK at its base URL with a live key, so setup is designed to work without installing a separate client library or rewriting your app.
Who is Tokenwise for?
The product is built for teams that want visibility into LLM spend, latency, errors, and quality while keeping their existing SDKs. The site highlights use by production apps and teams shipping agents, not just one-off experiments.
What does the workflow look like after onboarding?
The dashboard and rules engine are built to show every call, surface waste, and let you apply changes such as model swaps, caching, prompt trims, fallbacks, and A/B splits. Those changes are replay-checked against a quality baseline before being applied.
Which providers and SDKs are supported?
The site supports OpenAI, Anthropic, Google Gemini, xAI Grok, OpenRouter, Groq, DeepSeek, Mistral, Cohere, Together, Fireworks, Perplexity, and Bedrock, plus Vercel AI SDK and common OpenAI-compatible paths.
Are there any limitations?
Tokenwise positions itself as observability and optimization infrastructure, not a full replacement for your model provider. It is observe-only by default, and some deeper controls such as payload storage, eval scoring, and prompt-version drift detection depend on enabling payload storage.
Quick Facts
- Category
- LLM observability and cost optimization
- Product type
- HTTP proxy and dashboard
- Primary users
- Teams shipping production LLM applications and agents
- Integration style
- Base URL swap with existing SDKs
- Source domain
- tokenwisehq.com
- Pricing signal
- Free trial and paid plans are offered; pricing page references a $9.50/month starting point with a promo code
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