AI Gateway & Control Plane

The control plane for
production AI traffic.

One baseURL change. Instant visibility into cost, latency, and failures — by feature, by model, by provider. Route traffic at runtime without touching your code.

app.lectr.ai
All systems healthy
·12.4 req/min·3 providers healthy·0 anomalies

Overview

Control Surface

Routing RulesPartial16.7% of traffic routed
Spend LimitsOffNo spend limits configured
Fallback StrategyOffNo failover path configured

Operational Metrics

Last 24h ▾
REQ / MIN

12.4

1,789 total

ERROR RATE

0.8%

14 failures

P95 LATENCY

1.24s

Within target

COST / HR

$0.004

$0.10 total

Provider Execution

ProviderRequestsAvg LatencySuccessStatus
OpenAI1,234890ms99.2%Healthy
Anthropic4121.1s98.8%Healthy
Groq143234ms100%Healthy
OpenAI-compatibleOne-line integrationNo SDK requiredFeature-level taggingNo prompts stored
Works withOpenAIAnthropicGeminiGroqAzure OpenAI

The problem

AI traffic is invisible by default

Most teams ship AI features and lose track of them immediately. There is no central view of what is running, what it costs, or where it is failing.

Costs that appear without warning

AI costs compound across models and features with no central view. By the time you notice a spike, it has already happened.

Traffic you cannot see or control

Which feature is sending the most tokens? Which model is failing silently? Without observability, these questions go unanswered.

Providers with no fallback path

A provider outage or latency spike becomes a feature outage. Teams patch it manually, every time.

Routing decisions buried in application code

Every model swap or feature-level override is a code change and a deployment. There is no runtime control.

Capabilities

One proxy. Complete control.

Lectr sits in front of your AI providers and gives you visibility, cost attribution, and routing control — without changing how your application works.

Observability

See every request in real time

Full request log with model, latency, token usage, cost estimate, and status across every AI call. Understand traffic distribution by provider and feature at a glance.

Req/min · P95 latency · error rate · cost/hr · token usage

Cost Management

Break down spend by feature and model

Cost attributed to each feature, model, and provider. Set spend caps — when a threshold is hit, Lectr falls back to a cheaper model automatically.

Spend caps · alerts · model-level breakdown · CSV export

Routing

Route traffic without application rewrites

Define routing rules in the dashboard. Send cost-sensitive features to smaller models, route latency-critical paths to faster providers. Changes take effect immediately — no redeploy required.

Conditions: feature tag · task type · provider · model · priority

Anomaly Detection

Surface problems before they become incidents

Automatic detection of unusual request volume, error rates, latency spikes, and cost anomalies. Review and investigate with a full timeline of what changed and when.

Automated detection · dismiss workflow · baseline learning

Policy Engine

Set limits that protect against runaway spend

Define per-org and per-feature budget policies with soft and hard caps. Set P95 latency targets. Rate limits are enforced via Redis, shared across all proxy instances — no single point of failure.

Hard caps · soft caps · latency targets · rate limiting · per-org enforcement

How it works

Ship in minutes. Not months.

Lectr is designed for teams that move fast. Getting started is a one-line change. Everything else builds from there.

Your App

openai.chat()
one header
Lectr

proxy.lectr.ai/v1

observe · route · control

forwarded as-is
OpenAI
Anthropic
Groq

+ Gemini, Azure

OpenAI SDK compatibleEvery request logged automaticallyRouting rules apply in real time
01

Point your app at Lectr

Swap the base URL in your OpenAI client. Add your org key header. That is the entire integration — no SDK changes, no rewrites.

OpenAI-compatible — works with any SDK that targets the OpenAI API format
02

Tag requests by feature

Add an optional X-Lectr-Feature header to attribute traffic. Cost and latency are broken down per feature from day one.

Feature tagging is optional but unlocks cost breakdown and per-feature routing
03

See everything. Control everything.

Every request is logged. Set routing rules and budget limits from the dashboard — changes apply immediately, no redeploy.

Changes take effect immediately — no restarts, no deploys

Integration

One line to get started

Change the base URL. Add your org key. That is the entire integration. Your existing code keeps working exactly as before.

Before
Direct to provider
from openai import OpenAI

client = OpenAI(
    api_key="sk-your-key",
)

resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[...],
)
After
Through Lectr
from openai import OpenAI

client = OpenAI(
    base_url="https://proxy.lectr.ai/v1",
    api_key="sk-your-key",
    default_headers={
        "X-Lectr-Key": "lc_your-org-key",
        "X-Lectr-Feature": "chat"  # optional
    }
)

Privacy guarantees

Your OpenAI key is forwarded in memory only — never stored or logged
No prompts, responses, or raw request bodies are ever stored
Only metadata is captured — no content, no context, no raw bodies
See itUnderstand itControl it

Start controlling your AI traffic

Full visibility over your AI traffic. Routing and budget controls from the dashboard — no redeploy required.