5 Enterprise AI Gateways to Control AI Costs

Compare the top enterprise AI gateways for controlling LLM costs in 2026: semantic caching, hierarchical budgets, provider routing, and governance for production AI teams.

Enterprise LLM spending has surpassed $8.4 billion in API costs alone, with 72% of organizations expecting their AI budgets to increase further in 2026. As teams scale from single-model prototypes to multi-provider production deployments, the cost of unmanaged LLM traffic compounds fast. A single runaway workflow can consume thousands of dollars in API fees within hours if there are no spending controls in place.

Enterprise AI gateways sit between your application and LLM providers, adding a control layer that handles caching, provider routing, budget enforcement, and observability without requiring changes to application logic. This post compares five of the best enterprise AI gateways for controlling AI costs in 2026, covering what each does well and where each falls short.

What Cost Control Features Actually Matter in an Enterprise AI Gateway

Not all AI gateways address cost the same way. Effective LLM cost management at the enterprise level requires more than simple request logging. The capabilities that deliver measurable cost reduction are:

• Semantic caching: Cache LLM responses based on semantic similarity, not just exact-match hashing. This reduces redundant API calls for queries that are functionally the same across users and teams.
• Hierarchical budget controls: Enforce spending limits at the virtual key, team, project, and organization level with hard caps and configurable reset durations.
• Provider routing and fallback: Route requests to lower-cost models or providers based on rules, and fall back automatically when a primary provider fails, without requiring application-side changes.
• Per-request cost attribution: Log tokens used, cost incurred, and latency for every request, queryable by provider, model, team, and time period.
• Rate limiting: Prevent individual consumers or workflows from exhausting shared budgets.

Any gateway that claims cost control but lacks most of these capabilities is providing accounting, not governance.

1. Bifrost

Best for: Enterprise teams that need hierarchical budget controls, semantic caching, and multi-provider failover in a single low-latency deployment.

Bifrost is an open-source AI gateway built in Go by Maxim AI. It provides a unified OpenAI-compatible API across 20+ LLM providers and 1000+ models while adding a full cost governance layer on top of every request. At 11 microseconds of overhead per request at 5,000 requests per second, it is the highest-performance option on this list, with cost controls that do not compromise throughput.

Cost-specific capabilities

Semantic caching operates in two layers: an exact hash match that costs nothing beyond a cache lookup, and a semantic similarity search for queries that are functionally equivalent but phrased differently. For teams running similar queries across large user bases, this reduces API spend without degrading response quality.

Hierarchical budget management uses virtual keys as the primary governance unit. Each virtual key carries its own spending limit, rate limit, and provider access policy. Limits can be set at four levels: the virtual key, team, customer, and organization, each with independent tracking and configurable reset durations (daily, weekly, monthly). When a budget is hit, the gateway enforces a hard stop automatically, without requiring application-side error handling.

Automatic failover routes traffic to lower-cost backup providers when a primary provider is unavailable or over budget. Fallback chains are configurable per virtual key, enabling teams to define cost-ordered routing sequences (for example, route to a smaller, cheaper model when the primary model's budget is exhausted for the period).

Built-in observability logs every request with tokens used, cost, latency, model, and provider. Native Prometheus metrics and OpenTelemetry integration make this data available in Grafana, Datadog, New Relic, and Honeycomb without additional instrumentation. Bifrost also integrates natively with Maxim AI's evaluation and observability platform, giving teams a combined view of gateway cost data alongside agent quality metrics.

For teams operating CLI agents like Claude Code, Bifrost enables per-developer, per-team, and per-project cost tracking with no code changes required.

Deployment: Binary, Docker, Kubernetes, in-VPC

License: Apache 2.0

Language: Go

2. Cloudflare AI Gateway

Best for: Teams already on the Cloudflare stack that need a managed, zero-infrastructure cost tracking layer.

Cloudflare AI Gateway is a managed service that sits on Cloudflare's global edge network. It requires no infrastructure deployment and is accessible through the Cloudflare dashboard. For teams that are already routing web traffic through Cloudflare, adding LLM cost visibility is low friction.

Core cost-relevant features include edge-level response caching, rate limiting, real-time usage analytics, and an analytics dashboard that aggregates token usage, latency, and cost across supported providers. In 2026, Cloudflare added unified billing, allowing teams to consolidate third-party model charges (OpenAI, Anthropic, Google AI Studio) onto a single Cloudflare invoice.

The primary limitation for enterprise cost control is governance depth. Cloudflare AI Gateway does not provide hierarchical budget management at the team or project level, and its caching is exact-match rather than semantic. Teams that need hard spending limits enforced per team or consumer, or that need to route intelligently based on cost to a cheaper model when a budget is exceeded, will find these controls absent without building additional tooling on top.

Deployment: Managed cloud (no self-hosting)

License: Proprietary (free tier available)

3. Kong AI Gateway

Best for: Organizations already running Kong for API management who want to unify AI and traditional API governance under a single control plane.

Kong AI Gateway extends Kong's enterprise API gateway platform with AI-specific plugins. Teams that already use Kong to govern REST and gRPC traffic can layer LLM cost controls onto their existing API infrastructure without adopting a separate tool.

Cost-relevant capabilities include AI-specific rate limiting and token quota management via plugins attached to existing Kong routes, semantic caching through the AI Semantic Cache plugin, and multi-provider routing with circuit breaking and health checks. Kong Konnect adds enterprise governance: RBAC, audit logs, and developer portals for teams sharing the gateway.

The cost of this approach is integration complexity. Kong's AI capabilities are delivered through a plugin architecture, meaning cost control configuration is spread across route-level, plugin-level, and Konnect-level settings rather than a unified budget hierarchy. Teams that are not already invested in Kong infrastructure face a steeper setup path compared to purpose-built AI gateways. Kong does not natively support semantic caching with a hierarchical budget layer, so teams implementing both must compose plugins carefully.

Deployment: Self-hosted, Kong Konnect (managed)

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

Language: Lua (plugins), Go

4. LiteLLM

Best for: Developer and research teams that need maximum provider coverage and are comfortable owning infrastructure and configuration.

LiteLLM is a widely adopted open-source proxy that standardizes calls to 100+ LLM providers behind a unified API. It is popular in the developer community for provider experimentation and is self-hostable via Docker or direct Python install.

For cost control, LiteLLM supports per-key budget management, cost tracking by model and provider, and configurable fallbacks. It integrates with a LiteLLM-managed dashboard for usage visibility. Teams running fine-tuned or open-weight models through vLLM, Ollama, or similar runtimes benefit from LiteLLM's breadth of provider support.

The operational trade-off is runtime overhead and infrastructure ownership. LiteLLM is written in Python, and teams running high-throughput production workloads (thousands of requests per second) should benchmark its per-request latency at their target load before committing. Budget management is less hierarchical than Bifrost's virtual key model: limit configuration is per-key rather than across nested organizational levels. Teams that outgrow basic per-key limits will need to implement additional tooling to enforce team-level or project-level cost attribution.

Note: LiteLLM is a Python library rather than a purpose-built infrastructure gateway. This distinction matters for teams evaluating production reliability and separation of concerns in their AI infrastructure.

Deployment: Self-hosted (Docker, Python)

License: MIT

Language: Python

5. Azure API Management (AI Gateway Pattern)

Best for: Enterprises deeply embedded in Azure infrastructure that want to govern LLM traffic within their existing Microsoft ecosystem.

Azure API Management's AI gateway pattern extends APIM to govern LLM traffic across Azure OpenAI and third-party model endpoints. For Microsoft-centric organizations, this approach consolidates LLM governance within an already-familiar control plane, using existing Entra ID authentication, RBAC policies, and Azure Monitor for observability.

Cost-relevant capabilities include token-based rate limiting on Azure OpenAI endpoints, request logging to Azure Monitor and Log Analytics, and routing policies that can direct traffic between Azure OpenAI instances based on load or regional availability. Teams with existing APIM deployments can add AI governance without adopting a new tool.

The limitations are ecosystem tightness and feature gaps relative to purpose-built AI gateways. Azure APIM's AI gateway pattern does not provide semantic caching, hierarchical budget management across nested virtual keys, or automatic failover to non-Azure providers. Teams running multi-cloud or multi-provider LLM deployments will need to extend or supplement APIM to achieve the same cost control depth that dedicated AI gateways provide out of the box. The approach also lacks native MCP gateway support for agentic workflows.

Deployment: Azure managed service

License: Proprietary

Language: N/A (configuration-based)

Side-by-Side Comparison

Choosing the Right Enterprise AI Gateway for Cost Control

The right AI gateway depends on where your primary cost pressure originates and what your team is already operating:

• Production AI teams with multi-provider deployments and multi-team governance needs should evaluate Bifrost first. Semantic caching, four-tier budget management, and automatic failover provide the most complete cost control without additional tooling.
• Teams already on the Cloudflare stack who need basic usage visibility and caching at the edge can add Cloudflare AI Gateway with minimal setup, accepting its governance limitations.
• Organizations already running Kong for API management can extend their existing infrastructure with Kong's AI plugins, particularly for teams where AI traffic is one workload among many.
• Developer teams or research environments that need broad provider access and can own infrastructure management should evaluate LiteLLM for its provider coverage and open-source flexibility.
• Azure-native enterprises that want to keep LLM governance inside their existing Microsoft control plane should assess the APIM AI gateway pattern, with awareness of its limitations for multi-provider and multi-cloud environments.

For enterprise teams where AI costs are on the critical path, an AI gateway is not optional middleware. It is the control plane that determines whether LLM spending is predictable, attributable, and enforceable across every team and workflow in your organization.

To see how Bifrost's hierarchical budget management, semantic caching, and automatic fallbacks can reduce your LLM costs at production scale, book a demo with the Bifrost team.