5 Enterprise AI Gateways to Control AI Costs

Enterprise spending on LLMs has exceeded $8.4 billion in API costs alone, with 72% of organizations projecting further budget increases in 2026. As systems evolve from single-model prototypes to multi-provider production environments, unmanaged LLM traffic can drive rapid cost escalation. A single uncontrolled workflow may generate thousands of dollars in API charges within hours in the absence of effective safeguards.

Enterprise AI gateways operate between applications and LLM providers, introducing a control layer that manages caching, routing, budget enforcement, and observability without requiring modifications to application logic. This article evaluates five leading enterprise AI gateways for cost control in 2026, outlining their strengths and limitations.

What Cost Control Features Matter in an Enterprise AI Gateway

AI gateways vary significantly in how they approach cost management. At the enterprise level, effective cost control extends beyond basic request logging. The following capabilities are essential for meaningful cost reduction:

• Semantic caching: Stores LLM responses based on semantic similarity rather than strict input matching, reducing redundant API calls across similar queries.
• Hierarchical budget controls: Applies enforceable spending limits across multiple levels such as virtual keys, teams, projects, and organizations, with configurable reset intervals.
• Provider routing and fallback: Dynamically routes requests to lower-cost models and enables automatic failover when a provider becomes unavailable, without requiring application-side intervention.
• Per-request cost attribution: Tracks tokens consumed, cost incurred, and latency for each request, with filtering by provider, model, team, and time range.
• Rate limiting: Prevents individual users or workflows from depleting shared budgets.

Gateways that lack these capabilities primarily offer visibility rather than enforceable governance.

1. Bifrost

Best suited for: Enterprise teams requiring integrated hierarchical budget enforcement, semantic caching, and multi-provider failover with minimal latency impact.

Bifrost is an open-source AI gateway developed in Go by Maxim AI. It delivers a unified OpenAI-compatible API across to more than 1000 models across 20 LLM providers, while embedding a comprehensive cost governance layer. With an overhead of 11 microseconds per request at 5,000 requests per second, Bifrost offers high performance without compromising cost control.

Cost-focused capabilities

Semantic caching operates through a dual-layer approach: exact-match caching with negligible overhead and semantic similarity matching for functionally equivalent queries. This reduces duplicate API usage while preserving response quality.

Hierarchical budget management is built around virtual keys, each configured with spending limits, rate limits, and provider access policies. Budgets can be enforced at four levels: virtual key, team, business unit, and organization, each with independent tracking and configurable reset cycles. When a limit is reached, enforcement is automatic and does not depend on application logic.

Automatic failover enables cost-aware routing across providers. When a primary provider becomes unavailable or exceeds budget limits, requests are redirected to lower-cost alternatives based on predefined routing chains.

Built-in observability records tokens, cost, latency, model, and provider for every request. Integration with Prometheus and OpenTelemetry enables compatibility with monitoring platforms such as Grafana, Datadog, BigQuery, New Relic, and Honeycomb. Native integration with Maxim AI further connects cost data with agent performance metrics.

For teams operating CLI agents like Claude Code, Bifrost enables granular cost tracking across developers, teams, and projects without requiring code modifications.

Deployment: Binary, Docker, Kubernetes, in-VPC

License: Apache 2.0

Language: Go

2. Cloudflare AI Gateway

Cloudflare AI Gateway is a fully managed service deployed on Cloudflare’s global edge network. It eliminates infrastructure overhead and is accessible via the Cloudflare dashboard. For teams already using Cloudflare, integrating LLM cost visibility is straightforward.

Core capabilities include edge-level response caching, rate limiting, real-time analytics, and dashboards that aggregate token usage, latency, and cost across supported providers. In 2026, Cloudflare introduced unified billing, enabling consolidation of third-party model costs into a single invoice.

However, governance capabilities are limited. The platform does not support hierarchical budget enforcement across teams or projects, and caching is restricted to exact matches. Organizations requiring strict budget enforcement or cost-aware routing to alternative models must implement additional layers.

Deployment: Managed cloud

License: Proprietary (free tier available)

3. Kong AI Gateway

Kong AI Gateway builds on Kong’s API management platform by introducing AI-specific plugins. Organizations can extend existing governance frameworks for REST and gRPC traffic to include LLM cost control.

Relevant features include AI-specific rate limiting, token quotas, semantic caching through plugins, and multi-provider routing with circuit breaking and health monitoring. Kong Konnect enhances governance through RBAC, audit logs, and developer portals.

The trade-off lies in configuration complexity. Cost control is distributed across routes, plugins, and control plane settings rather than managed through a unified hierarchy. Additionally, combining semantic caching with hierarchical budgets requires careful orchestration, as native support for both in a cohesive model is limited.

Deployment: Self-hosted, Kong Konnect

License: Apache 2.0 (OSS), proprietary (enterprise)

Language: Lua, Go

4. LiteLLM

LiteLLM is an open-source proxy that standardizes access to over 100 LLM providers through a unified API. It is widely used for experimentation and can be deployed via Docker or direct Python installation.

Cost-related features include per-key budget limits, cost tracking by provider and model, and configurable fallback mechanisms. It also provides a dashboard for usage monitoring. Its extensive provider support is particularly useful for teams working with open-weight or fine-tuned models.

The primary limitation is operational overhead. As a Python-based solution, performance at high throughput should be evaluated carefully. Budget enforcement is limited to per-key configurations rather than hierarchical structures, requiring additional tooling for broader organizational governance.

Deployment: Self-hosted

License: MIT

Language: Python

5. Azure API Management (AI Gateway Pattern)

Azure API Management’s AI gateway pattern extends APIM to manage LLM traffic across Azure OpenAI and external endpoints. It integrates with existing Azure services, including Entra ID for authentication, RBAC for access control, and Azure Monitor for observability.

Capabilities include token-based rate limiting, request logging, and routing policies for distributing traffic across Azure OpenAI instances. This allows organizations to extend existing governance frameworks without introducing new infrastructure.

Limitations include the absence of semantic caching, hierarchical budget enforcement, and native support for multi-provider failover outside the Azure ecosystem. Organizations operating across multiple providers or clouds will need supplementary solutions. MCP support for agent-based workflows is also not included.

Deployment: Azure managed service

License: Proprietary

Selecting the Right Enterprise AI Gateway

The optimal choice depends on the source of cost pressure and existing infrastructure:

• Production environments with multi-provider usage and multi-team governance requirements should prioritize Bifrost for its comprehensive cost control capabilities, including semantic caching and hierarchical budgets.
• Organizations already using Cloudflare can adopt Cloudflare AI Gateway for basic visibility and caching with minimal operational effort.
• Teams using Kong for API management can extend their current setup with AI plugins to unify governance though this often requires managing the complexity of their existing ecosystems
• Developer and research teams may prefer LiteLLM for its flexibility and extensive provider support, accepting the need for additional infrastructure management.
• Azure-centric enterprises can leverage APIM’s AI gateway pattern to maintain governance within the Microsoft ecosystem, while accounting for its limitations in multi-provider scenarios.

For organizations where AI expenditure is a critical concern, an AI gateway serves as the control plane that ensures spending remains predictable, traceable, and enforceable across all teams and workflows.