Comparison matrix
Every gateway against every requirement. Check the capabilities you can't trade, and the columns that fail them drop off the grid.
Industry Research
State of AI gateways for the enterprise
A public comparison library that scores every gateway against the same requirement set, with the source cited behind each answer.
An AI gateway is the control point between applications and the models they call. It carries identity, policy, spend, and routing through one endpoint, the operational core of any real enterprise AI strategy.
What it is
A control plane, not another SDK.
A gateway sits in the request path. Client code stops talking to providers directly and uses one endpoint instead. The gateway holds provider credentials, records who called what, and routes the same application to a different model without changing a single line of code. Applications and the models they call stay untouched. The gateway moves the controls every team needs, identity, spend, routing, and audit, out of individual applications and into the one layer every request has to cross. That's the shift beneath most enterprise AI governance frameworks today: shared controls, enforced in one place.
# before: each app holds provider details OPENAI_API_KEY=sk-... ANTHROPIC_API_KEY=sk-ant-... # after: one endpoint, scoped identity OPENAI_BASE_URL=https://gateway.company/v1 OPENAI_API_KEY=$TEAM_SCOPED_KEY
Identity + AccessTie every credential and request to a person, team, or application.
Data ProtectionInspect and redact sensitive content at one checkpoint, a core piece of any AI security compliance program.
Cost + UsageAttribute tokens and cut off the next request the moment a budget runs out.
Provider RoutingChange models through configuration, not application code.
ReliabilityTest health, retries, and fallback before a provider fails.
Governance + AuditRecord who called what, under which policy, and what happened. This is where AI compliance and security stop living in a spreadsheet.
The Problem
The same gap looks different from every seat at the table.
Direct model access feels simple with one team and one provider. Add a second team, a different provider, or an autonomous agent, and it becomes difficult. Each leader ends up asking something different, and the answers sit in separate roles with no shared view.
Finance / FinOpsBudget owner
Which team, feature, or customer drove this month's model spend?
Per-request attribution and budgets enforced before the next call.
SecurityRisk owner
Who sent sensitive data to which model, and under which policy?
One inspection point, scoped credentials, and a request audit trail, the backbone of secure AI workflows.
PlatformReliability owner
What happens when a provider is rate-limited, unavailable, or too expensive?
Health-aware routing, retries, and a tested path to another provider: AI security automation in practice, not a manual runbook.
Application teamsDelivery owner
How many integrations change when the preferred model changes?
One stable endpoint while routing changes behind the gateway.
Coding agents make the same problems arrive sooner. Claude Code, Codex, Cursor, and OpenCode each want a base URL and a key. Without a gateway, every one of those tools becomes another direct provider integration and another gap in the governance framework.
This library
What this comparison is for
We score every gateway against the same requirement set, so products get compared on the same questions instead of their own marketing.
Requirements split into three groups: what a pilot cannot ship without, what real traffic demands next, and what can wait. Every answer carries its rationale and its sources. Inferred scores are marked as inferred. Fit depends on your cloud, your operating model, and which requirements you refuse to trade. We don't price out a project here, and a GitHub star count doesn't stand in for market share in this comparison.
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Go deeper
Where to start
Combinations
Pair any model-hosting service with any gateway, either half optional, and see the combined capability. Early build, reading from the split MaaS and gateway datasets.
Gateway summaries
Who each gateway fits, where it falls short, and dated adoption figures with a source for each one.
Setup patterns
Native cloud control planes compared against portable gateways you can run on AWS, Azure, Google Cloud, or more than one at once.
Selection guide
Three questions about your stack, your operating model, and your must-have requirements, then a shortlist scored from the same cells.
SaaS reference
OpenRouter, Vercel, Cloudflare, Helicone, and MLflow, noted for context. Not scored, not labeled.
Citations for every score are on Sources. The site map lists each gateway page.