“We were essentially able to reduce the cost of that cluster by about 75%. On AWS, DevZero demonstrated they could achieve significantly higher savings than we initially thought possible.”
Mihir Nair
Head of Architecture, Databahn
Inference Platform
Inference is everywhere.
Manage it from one place.
Cost, latency, and reliability — across applications, agents, and the Claude Code, Cursor, and Codex sessions your developers run every day.
Cost
40–70% lower spend.
Latency
p95 you can ship.
Reliability
Survive provider outages.
Attribution
Every token traceable.
The Problem
Your AI stack is fragile, slow, AND expensive.
Five inference APIs. Three providers. p95 spikes when one rate-limits, silent failure when one goes dark, and a CFO who wants an answer by Friday. You don't have a visibility problem — you have a coordination problem across cost, latency, and reliability. 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 LLM gateway. Every provider. Automatic failover.
DevZero's self-hosted AI gateway sits in front of every inference call — your applications, your batch jobs, and your developers' Claude Code, Cursor, and Codex sessions — across OpenAI, Anthropic, Gemini, Bedrock, Azure, Mistral, and anything OpenAI-SDK compatible. When a provider rate-limits or goes dark, the gateway fails over without your app or your devs noticing. Sub-millisecond added latency per call. Your API keys never leave your infrastructure.
- › 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.
- › Routes Claude Code, Cursor, Codex — same observability surface as your app traffic.
- › Tags every call with team, product, and workflow automatically.
Shadow Cache
Prove the savings AND the latency win before you flip the switch.
Shadow cache mirrors your live traffic in dry-run mode — every prompt is hashed and checked against the cache, but responses still come from the upstream provider. You see the cost reduction, the p95 improvement, and the drift, all measured against your own traffic before any caching is enabled. Flip it on with real numbers behind the decision.
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%+, and how each band moves p95 latency.
Cost AND latency proof
Every band shows would-be savings in dollars alongside the latency reduction from cache hits — the CFO and the SRE both get their answer.
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.
Session-Level Cost
Trace every agent run.
See cost and latency for every agent run — and where the bill came from. Every span: model, tool call, retry. Every dollar mapped to the customer, the workflow, the prompt cluster.
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.
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
Trace every token to the team that spent it.
Every call carries automatic tags for team, product, workflow, and prompt cluster — including the Claude Code, Cursor, and Codex sessions your engineers run every day, so dev-tooling AI spend stops being invisible to finance. It all rolls up into department-level views that match your org chart — so the CFO gets a bill that makes sense, and platform gets a usage policy lever they can actually pull. Chargeback, showback, and per-team rate limits, on the same surface.
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.
$0
Gross 30-day spend
$0
Net after cache
0.0%
Cache hit rate
0.0×
Cache ROI
0ms
p50 added latency
Why DevZero
We've been rightsizing infra at runtime for years.
Kubernetes clusters run idle. GPUs run cold. Provider APIs spike. DevZero's runtime-rightsizing engine has been keeping infra honest — cost, latency, and availability — without restarts, without surprises, since day one. This is that engine, pointed at every token you ship.
Frequently asked questions
What our customers say