Inference Efficiency
Rightsize your LLM inference stack.
Every call. Automatically.
A gateway, a shadow cache, and an eval lab that prove every LLM optimization against your own traffic — before you ship it.
The Problem
Your AI stack got expensive. Fast.
Five inference APIs. Three providers. Forty developers shipping prompts their own way. Spend is scattered across five dashboards, nobody owns the bill, and the CFO wants an answer by Friday. You don't have a visibility problem — you have a coordination problem. And observability alone won't fix it.
Observe
See every call, session, and prompt cluster — across every provider, in one metering surface.
Simulate
Dry-run caching and model swaps against your own traffic. Measure hit rates and quality deltas before you commit.
Automate
Roll out the winning change. Keep quality honest as it runs with live divergence tracking.
Unified Gateway
One gateway. Every provider. Zero migration.
DevZero speaks the OpenAI SDK — so every app already talking to OpenAI, Anthropic, Gemini, Bedrock, or Azure OpenAI is instantly metered, traced, and optimizable. No code rewrite. No proxy gymnastics. One base URL change and your efficiency story starts.
- › Self-hosted gateway — your API keys never leave your infrastructure.
- › Captures cost, latency, retries, tool-call %, and finish reason per call.
- › Works with streaming, function calling, and vision endpoints.
- › Tags every call with team, product, and workflow automatically.
Session-Level Cost
Cost per call isn't the unit. Cost per agent run is.
Agents make dozens of calls to finish one task. DevZero rolls every call in a session into one trace — tokens, retries, tool calls, total dollars — so you see what a workflow costs, not just what a prompt costs. The first place you'll notice the runaway retry loop draining your budget.
Prompt Clusters
89 workloads, not 30,000 calls.
DevZero groups your traffic by what it actually does — customer support, SQL generation, classifier, summarizer — so cost conversations happen at the workflow level, not the request level. Finally a vocabulary your product team recognizes.
Each cluster is a semantic neighborhood. Each dot is one of your real prompts. We use this shape everywhere else on this page — recommendations, routing, evals.
Shadow Cache
Prove the savings before you flip the switch.
Turning on semantic caching is scary — you don't know the hit rate, you don't know the quality impact. Shadow Cache runs in dry-run mode next to production: it hashes every prompt, counts would-be hits across similarity bands, and measures what you would've saved. Ship caching the day the numbers hold up.
Zero risk
Shadow traffic never serves a response. Production sees no change until you flip the threshold.
Similarity bands
Tune aggressiveness post-hoc. See exactly how many requests would hit at 90%+ vs 80%+ vs 70%+.
Dollar-anchored
Every band shows would-be savings in dollars, not just hit counts. The CFO conversation writes itself.
See what shadow caching saves on your traffic
Eval Lab
Swap models with evidence, not vibes.
Pick a workload. Pick the models you want to compare. Click run. DevZero replays your real traffic through every candidate and plots quality-vs-cost, so you can say “Haiku is good enough here” with a number behind it — not a gut call.
- › 20+ candidate models across Anthropic, OpenAI, Gemini.
- › Runs against your traffic, not a benchmark.
- › Quality score, cost per 1M tokens, latency — one view.
Ready to swap models with evidence?
Cache Quality
Cache hits without quality drift.
Every cached response is scored for semantic divergence against a live baseline. If a model's cached outputs start to diverge, the dashboard tells you which model, which band, and how bad — before users notice, before the CFO asks.
Attribution
Give the CFO a bill that makes sense.
Every call is tagged by team, product, and business unit automatically — so spend rolls up the way your org chart does. Chargeback, showback, or just a monthly review that doesn't need a spreadsheet.
Recommendations
Savings that come with a price tag.
Every recommendation — TTL tweaks, model swaps, prompt consolidations — arrives with a dollar amount and a quality forecast. Accept the ones that make sense. Skip the ones that don't. No hand-waving.
Numbers the CFO Cares About
One dashboard. One conversation.
Based on a typical mid-stage deployment after 30 days.
Gross 30-day spend
Net after cache
Cache hit rate
Cache ROI
p50 added latency
Why DevZero
We've been rightsizing cloud bills live for years.
Kubernetes clusters run idle. GPUs run cold. Teams ship infrastructure they never use. DevZero's live-rightsizing engine has been cutting those bills automatically — without restarts, without surprises — since day one. This is that engine, pointed at every token you ship.