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Engineering Leadership

Secure AI for every team, visibility and control for you

Track spend by project, team, and model in one view, and map AI cost to your goals and OKRs.

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Architecture view

AI requests flow through one managed gateway before reaching models and providers.

Teams

Mapped

Keys

Scoped

Review

Audit log

AI app

Support

Owner, key, model route, and spend in one view.

Provider

Anthropic + Azure

Primary path and fallback path are visible.

Control

Spend limit

Limit is attached to the owning key.

New capabilities

What your team gains with Concentrate

01

Reduce vendor sprawl

One API path lowers the number of provider accounts and dashboards your teams juggle.

02

Keep teams moving

Engineers can test models without waiting on repeated provider setup.

03

Review what changed

Use logs and audit trails to see traffic, settings, and owners.

Where it fits

For leaders moving from experiments to org-wide AI usage

Engineering leaders need a short path from prototype to managed usage without asking every team to own provider accounts and dashboards.

Simpler architecture

Swap backend models through configuration and request routing instead of pushing app changes for every provider decision.

Team boundaries

Create teams, projects, and keys so one group cannot silently spend another group's budget.

Shared logs

Standardize request logs across apps so on-call engineers and leaders can see the same traffic.

Security review

Give security teams SSO, RBAC, redaction, ZDR options, and audit logs without a custom internal gateway project.

Engineering Leadership basics

Frequently asked questions

Can teams choose models without creating provider sprawl?
Yes. Teams can route workloads to approved models through Concentrate while leadership keeps one view of keys, owners, spend, providers, and request logs. Model choice stays flexible without every team creating its own provider account.
What should engineering leaders track once AI usage grows?
Track owners, API keys, model routes, provider paths, spend, latency, errors, fallbacks, and audit events. Those fields answer the questions leaders get from finance, security, and on-call engineers.
When should we stop letting each team manage providers directly?
Direct provider setup works for isolated prototypes. Once multiple teams use AI in customer-facing or high-volume workflows, provider access, logs, fallback paths, and security reviews should move to one managed request path.
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One API for every major LLM provider — routing, spend, logs, and controls in one place.

New York

130 E 59th St, 17th floor

New York, NY 10022

Wilmington

1201 N. Market Street, Suite 200

Wilmington, DE 19801

LLM Gateway
  • LLM Gateway
  • Request Routing
  • Usage Monitoring
  • Spend Management
  • Data Security
  • Access Controls
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  • Status
Legal
  • Privacy Policy
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Features
  • Universal API Keys
  • Spend Tracking
  • Token Allocation
  • Usage Analytics
  • Request Logs
  • Alerts
  • Data Redaction
  • Zero Data Retention
  • Audit Logs

LLM Gateway

  • LLM Gateway
  • Request Routing
  • Usage Monitoring
  • Spend Management
  • Data Security
  • Access Controls

Teams

  • AI Engineering
  • Engineering Leadership
  • Finance & Operations
  • Security & Compliance

Integrations

  • All Integrations
  • Migration Guides

Platform

  • Pricing
  • Model Fortress
  • Enterprise
  • Documentation
  • Status

Legal

  • Privacy Policy
  • Terms of Service
  • Data Processing Addendum
  • Acceptable Use Policy

Features

  • Universal API Keys
  • Spend Tracking
  • Token Allocation
  • Usage Analytics
  • Request Logs
  • Alerts
  • Data Redaction
  • Zero Data Retention
  • Audit Logs

Offices

New York

130 E 59th St, 17th floor

New York, NY 10022

Wilmington

1201 N. Market Street, Suite 200

Wilmington, DE 19801

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