TokenLens — Enterprise LLM Cost & Shadow Evaluation Proxy Platform: "An API proxy that cuts enterprise LLM inference bills by 42% via real-time shadow model benchmarking."
Runaway LLM API Spend & Risk of Quality Regression
Engineering teams deploying Generative AI features face a difficult choice: spend millions on top-tier models (GPT-4o, Claude 3.5 Sonnet) or downgrade to cheaper models and risk subtle quality regressions. Standard analytics tools only show past bills without routing traffic dynamically.
Zero-Latency Edge Routing & Asynchronous Shadow Scoring
We built an ultra-fast proxy layer that routes production requests to the primary provider while running identical prompts in the background against cheaper candidate models to score quality parity.
Edge Proxy Architecture
Deployed on Cloudflare Workers ensuring less than 14ms overhead so primary user responses are never delayed.
Asynchronous Shadow Benchmarking
Calculates semantic similarity between frontier and candidate model outputs in background queues.
Automatic Provider Fallback
Instant failover across OpenAI, Anthropic, and Groq whenever provider latency spikes or errors occur.
Dynamic Traffic Routing & Automated Model Cost Arbitration
Gives engineering teams granular telemetry and automated cost controls.
Sub-14ms Edge Traffic Interceptor
Passes API requests securely with tenant rate-limiting and zero data retention.
Background Shadow Model Benchmark
Scores whether cheaper models match frontier model response quality on real production prompts.
Automated Circuit Breaker
Switches model providers instantly if rate limits or errors trigger, preventing downtime.
42% Reduction in Inference Expenditure & 99.99% Uptime
TokenLens empowered engineering teams to scale AI features aggressively while keeping API budgets under tight control.
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