LLM Token Optimizer

By default we work from usage logs your provider already produces - OpenAI usage exports, Anthropic Console exports, Bedrock CloudWatch, Vertex AI logging - plus traces from LangSmith, Helicone, Langfuse, Phoenix, or Datadog LLM Observability if you use them. Token counts, model IDs, latencies, and request shapes are enough for most findings. If you want prompt-level recommendations, you can share a representative sample of prompts and completions - redacted as needed. We never need production API keys.

Most teams we audit see 30-70% reduction in token spend after implementing the report's quick wins, with no measurable quality drop on their golden eval set. The biggest contributors are usually prompt-cache adoption (Anthropic, OpenAI, Gemini), system-prompt compression, model right-sizing for non-critical traffic, and tightening agent loops. The report quantifies $/month savings per recommendation so you can decide what to ship.

Yes. We routinely audit Anthropic Claude (Opus / Sonnet / Haiku), OpenAI GPT-5 / GPT-5-mini / GPT-5-nano, Google Gemini 2.5 Pro / Flash / Flash-Lite, and the same models served via AWS Bedrock, Google Vertex AI, and Azure OpenAI. Open-weight models on Groq, Together, Fireworks, or self-hosted deployments are supported as routing targets.

Every recommendation is validated against a golden eval set - either yours, or one we build with you from real production samples. We use a combination of LLM-as-judge scoring and task-specific metrics (exact match, JSON validity, retrieval recall, code-execution pass rate, tool-call correctness) to detect regressions before we ship a recommendation. The final report includes before/after eval results so quality is evidence-based, not promised.

Yes. We can ingest exports or grant read-only access to LangSmith, Helicone, Langfuse, Arize Phoenix, and Datadog LLM Observability. We also work directly from OpenTelemetry GenAI semantic-convention traces and native provider exports if you do not use a third-party observability tool.

Yes - agentic workloads are typically where the biggest savings live. We profile loop iterations, parallel tool calls, retry behaviour, context-window growth across turns, and tool-call efficiency. Common wins include capping reasoning tokens, parallelising independent tool calls, switching parts of the loop to a smaller model, and replacing long-context patterns with retrieval where it improves both cost and accuracy.

Prompt caching (Anthropic, OpenAI, Gemini) reuses the model's internal computation for repeated prefixes - system prompts, few-shot examples, RAG context - and is essentially free quality-wise. Semantic caching reuses entire prior responses for similar queries based on embedding similarity, which is more aggressive and needs guardrails. Most teams should adopt prompt caching first; semantic caching is layered on top for high-traffic, low-risk surfaces. The report gives you a per-surface recommendation for both.

Typical turnaround is 1-2 business days from the moment usage data is shared. Larger AI estates with many agents or providers can take a little longer; we confirm the timeline as soon as we see the scope.