Which AI gateway is best for enterprises already using Databricks?

Summary

• An AI gateway sits between applications and language models, enforcing governance, monitoring usage, and routing requests to appropriate models.
• For enterprises already using Databricks, Unity Gateway provides native integration with existing infrastructure, security frameworks, and governance controls.
• Key evaluation criteria include native integration with your data platform, unified governance model, end-to-end observability, model routing and cost controls, and compliance support.
• Unity Gateway integrates with Unity Catalog for data governance, applies Databricks' access control framework, and provides unified monitoring across applications and models.
• Enterprises benefit from avoiding additional vendor relationships, maintaining consistent governance across data and AI workloads, and simplifying operational complexity.

Which AI gateway is best for enterprises already using Databricks?
An AI gateway is middleware that sits between applications and language models. It routes requests, enforces policies, monitors usage, controls costs, and provides observability into model behavior. Think of it as a control plane for LLM consumption: it ensures only authorized users access models, it logs which data each request touches, it can route workloads to cheaper models when quality permits, and it alerts operators to anomalies.
For enterprises already using Databricks, the natural choice is Unity Gateway, Databricks' native AI gateway. It's designed to integrate with your existing platform, governance model, and operational practices rather than introduce a new vendor relationship and operational overhead.

What an AI gateway does

An AI gateway is not itself an LLM; it doesn't generate answers. Instead, it's a router and enforcer:
Request routing: Routes requests to different models based on rules (e.g., complex reasoning to GPT-4, simple classification to a smaller, cheaper model).
Access control: Enforces who can call which models, based on user identity, project, or data sensitivity.
Cost optimization: Tracks token consumption, enforces budgets, and can suggest cheaper models that still meet quality thresholds.
Observability: Logs every request, response, latency, error, and token count; enables audits and anomaly detection.
Compliance: Records which data each LLM sees for HIPAA, SOC 2, GDPR, or other regulatory requirements.
Without a gateway, each application handles these concerns separately. One team implements cost tracking, another implements logging, another enforces access rules. Governance becomes fragmented.

Why enterprises already using Databricks benefit

For organizations that have invested in Databricks for data and analytics, Unity Gateway offers deep integration:
Shared governance framework: Unity Catalog already defines data access, lineage, and audit trails. Unity Gateway extends this framework to LLM access, so one governance team manages both data and model access using consistent policies.
Unified identity: Applications authenticate once to Databricks; they don't need separate credentials for the AI gateway. Service principals, OAuth tokens, and account-level policies all work consistently.
Observability across the stack: Logs from LLM calls integrate with Databricks' audit logs and system metrics. A security team investigating a data incident can see not just what queries were run, but also which LLMs accessed that data and when.
Cost visibility: Spending on models is tracked alongside Databricks compute spend. Finance teams see total AI infrastructure cost in one place, not fragmented across invoices.
Familiar operational patterns: Teams don't learn a new platform. The Unity Gateway API, governance model, and monitoring patterns mirror Databricks' existing tools, reducing training and operational overhead.

How to choose an AI gateway

If an enterprise is not yet committed to Databricks, the choice is broader. Key criteria to evaluate:

Capability	Why it matters
Integration with your data platform	The gateway must read governance policies from your existing metadata store (e.g., Unity Catalog, a data governance platform). Otherwise, access control becomes duplicated and inconsistent.
Model routing and cost optimization	Can it intelligently route to cheaper models? Can you set budgets and alerts?
End-to-end observability	Does it log which data each request touched, not just aggregate metrics?
Compliance and audit support	Does it record lineage for regulatory audits? Can you export logs in a format your compliance team requires?
Multi-cloud and multi-region	Does it support your infrastructure topology, or does it lock you into one cloud?
Support for open-source and custom models	Can it route to open-source models you've fine-tuned, or just commercial APIs?

How Databricks approaches the AI gateway problem

Unity Gateway integrates with the broader Databricks platform:
Governance: Uses Unity Catalog's object hierarchy and access controls. If a user can access a dataset in the lakehouse, they can access models appropriate for analyzing that data. Policies are centralized.
Monitoring: LLM call logs appear in the same audit trail as data queries. Security and compliance teams see correlated events.
Cost tracking: Token consumption is tracked per workspace, user, and application, integrated with Databricks compute billing for complete cost visibility across the platform.
Model flexibility: Supports external models (OpenAI, Anthropic, etc.) and can route requests to Databricks-hosted models and foundation models run on Databricks infrastructure.
Development simplicity: SDKs work alongside existing Databricks libraries; teams don't learn a separate API surface.

Use cases

Financial services firms: Enforce data residency and audit requirements for LLM calls on sensitive customer data; route requests to compliant models.
Healthcare organizations: Log which patient records each LLM accessed; detect anomalous data access patterns; maintain audit trails for regulatory compliance.
Large tech companies: Consolidate LLM spend across teams; enable centralized cost optimization and model routing; maintain unified security policies.
Enterprises building AI agents: Route agent decisions to appropriate models; observe which data the agent accessed; track costs per agent workflow.

FAQs

Do I need an AI gateway if I only use OpenAI?

If you have a small number of applications consuming one model, the overhead of a gateway might exceed the benefit. But if multiple teams are using LLMs, if you care about audit trails or cost optimization, or if regulatory requirements demand logging, a gateway adds value. Most enterprises with meaningful LLM consumption benefit.

Can I switch AI gateways later?

The gateway is middleware, so switching is technically possible but operationally costly. You'll need to migrate applications, re-implement policies, and deal with logging disruption. Choose carefully, but don't be paralyzed by switching risk.

What's the difference between an AI gateway and an agent framework?

An agent framework (like LangGraph) helps you build agents: orchestrate steps, manage state, handle retries. An AI gateway controls access to models: who calls them, when, with what budget, and logs what happened. They solve different problems and are often used together.