Top 5 Enterprise AI Gateways to Control LLM Spend Across Providers

Enterprise AI spending grows faster than engineering teams can track it. With multiple providers (OpenAI, Anthropic, Google Vertex, AWS Bedrock), multiple applications, and dozens of developers and teams each making independent API calls, monthly LLM spend routinely surprises finance teams and exceeds forecasts. The root cause is structural: direct provider API access provides no centralized cost attribution, no per-consumer budget enforcement, and no mechanism to prevent any individual service or user from consuming disproportionate quota. An AI gateway solves this by making every AI request pass through a control point where spending limits, routing rules, and cost visibility are enforced.

This guide compares the five most capable enterprise AI gateways for controlling LLM spend across providers in 2026.

What Effective LLM Cost Control Requires

An AI gateway earns the "LLM cost control" label when it provides:

• Per-consumer budget enforcement: Monthly or daily token or dollar spend limits per user, team, or application, enforced automatically (not just reported after the fact).
• Semantic caching: Response caching for similar queries to eliminate redundant API calls.
• Cross-provider visibility: A unified cost view across all providers, not per-provider dashboards that must be manually aggregated.
• Routing to cost-optimal models: Rules that direct different workload types to the most cost-appropriate model, not just the highest-capability model.
• Rate limiting: Per-consumer request rate controls that prevent throughput spikes from generating unexpected costs.
• Real-time alerting: Spend alerts that fire before monthly budgets are exhausted, not after.

1. Bifrost

Bifrost is the open-source AI gateway built in Go by Maxim AI. It provides the most complete LLM cost control feature set of any enterprise AI gateway in 2026, combining per-consumer budgets, semantic caching, cross-provider routing, and unified observability in a single deployable platform.

Best for: Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

LLM cost control capabilities:

Virtual keys are the primary mechanism for cost control in Bifrost. Each consumer (user, team, service, or application) receives a virtual key with an explicit budget limit: a monthly or daily cap on token spend or dollar spend. When a virtual key reaches its limit, requests are rejected at the gateway before reaching any provider. There are no retroactive overages.

Rate limits complement budget limits by capping throughput: a batch processing job with a high token budget can still be rate-limited to prevent throughput bursts that crowd out interactive workloads sharing the same provider quota.

Semantic caching reduces API calls by serving cached responses for semantically similar queries. This is the highest-leverage cost reduction mechanism for applications with repeated query patterns, such as support bots, FAQ assistants, document analysis pipelines, and code review workflows.

Routing rules and provider routing direct workloads to cost-appropriate models automatically:

• Batch summarization jobs route to lower-cost models (e.g., GPT-4o-mini, Claude 3 Haiku)
• High-complexity reasoning tasks route to frontier models
• Background jobs run during off-peak windows against lower-cost provider tiers

For MCP-heavy agentic workloads, Code Mode reduces token consumption per tool-use interaction by 50%, directly cutting inference costs for agent-based workflows. The MCP Gateway resource page and the MCP token cost analysis blog document cost savings at scale.

Bifrost's built-in observability provides real-time cost breakdowns by virtual key, model, and provider, with export to Prometheus, OpenTelemetry, Grafana, and Datadog via the Datadog connector.

Access profiles in the enterprise tier allow reusable budget policy templates to be applied at scale without per-key configuration overhead. The governance resource page covers the full cost control architecture.

2. AWS Cost Explorer + Amazon Bedrock Usage Governance

AWS provides LLM cost control through the combination of Amazon Bedrock (model access), AWS Budgets (spend alerts), and IAM quotas (request limits). For teams running AI workloads on Bedrock, AWS Cost Explorer provides per-model and per-service cost attribution.

Best for: Organizations with existing AWS cost management infrastructure that want LLM spend visibility integrated into their existing AWS billing dashboards. Teams using Bedrock-native models (Claude, Titan, Llama on Bedrock) who want unified spend reporting alongside other AWS services.

Cost control capabilities: AWS Budgets alert when Bedrock spend approaches a defined threshold. Service quotas cap the number of model invocations per account. Cost allocation tags attribute Bedrock spend to projects or teams in AWS Cost Explorer.

Limitations: There are no per-user or per-application spend limits within Bedrock; cost control is at the AWS account level unless multiple accounts are used. Semantic caching is not available. Cross-provider cost visibility (for providers outside AWS) requires manual aggregation. Routing workloads to cost-optimal models requires custom Lambda or Step Functions logic.

3. Azure API Management + Azure OpenAI Cost Controls

Azure provides LLM cost control through Azure API Management (rate limiting and quota enforcement) and Azure Cost Management (spend reporting). For teams using Azure OpenAI, APIM can enforce token-based quotas per subscription.

Best for: Enterprises with Microsoft Azure infrastructure that use Azure OpenAI and want spend controls integrated into Azure's existing cost management framework. Teams with existing APIM deployments who want to apply the same policy framework to AI endpoints.

Cost control capabilities: APIM policies enforce rate limits and token quotas per API subscription. Azure Cost Management provides spend reporting and budget alerts across Azure OpenAI and other Azure AI services. Reserved capacity options allow teams to pre-purchase token capacity at reduced rates.

Limitations: Cost controls apply at the APIM subscription level rather than per user or per application within a subscription. Cross-provider spend visibility (to non-Azure providers) is not available natively. Semantic caching requires custom APIM policy development. Routing workloads to cost-optimal models requires custom policy logic.

4. Google Cloud Billing + Vertex AI Quotas

Google Cloud provides LLM cost control through Vertex AI service quotas, Cloud Billing budgets, and Cost Allocation Labels. For teams using Gemini models on Vertex AI, budget alerts can trigger when spend approaches a defined threshold.

Best for: Google Cloud-committed organizations using Vertex AI models who want LLM spend visibility integrated into Google Cloud Billing alongside other GCP service costs. Teams with existing GCP cost management infrastructure.

Cost control capabilities: Vertex AI service quotas cap requests per minute per project. Cloud Billing budgets alert when AI spend approaches a threshold. Cost Allocation Labels attribute spend to teams or projects. Organization Policies restrict which Vertex AI models are accessible.

Limitations: No per-user or per-application spend limits within Vertex AI projects. Cross-provider cost visibility requires separate tooling. Semantic caching is not a Vertex AI native feature. Workload routing to cost-optimal models requires custom infrastructure.

5. Kong AI Gateway with Token Rate Limiting

Kong AI Gateway extends the Kong API proxy with AI-specific plugins, including token-based rate limiting and cost tracking. For organizations running Kong as their API gateway, this extends existing infrastructure to cover AI spend management.

Best for: Organizations with existing Kong API gateway deployments that want to apply the same gateway infrastructure to LLM endpoints. Teams with Kong Enterprise expertise who want consistent tooling across all API types.

Cost control capabilities: Token-based rate limiting plugins cap per-consumer token usage per period. Kong's logging plugins route request data to cost tracking systems. Budget alerts can be built through Kong's event system and external alerting infrastructure. Multi-provider routing to cost-optimal models is possible through Kong's routing plugins.

Limitations: Per-consumer AI budgets and semantic caching require custom plugin development rather than built-in features. Cross-provider cost visibility requires external aggregation. MCP governance is not natively available, meaning agent-based LLM cost control requires a separate solution.

LLM Cost Control Feature Comparison

Choosing an AI Gateway for LLM Cost Control

For enterprises that need per-consumer budget enforcement, semantic caching, cross-provider cost visibility, and routing to cost-optimal models without cloud lock-in, Bifrost is the most complete option. It is the only platform in this comparison with built-in semantic caching, per-consumer budget limits that enforce rather than just alert, and Code Mode for MCP token cost reduction.

Cloud-native options (AWS, Azure, GCP) are appropriate for organizations deeply committed to a specific cloud provider who accept the governance and cost-control limitations that come with provider lock-in.

For a detailed evaluation of AI gateway cost control capabilities, the LLM Gateway Buyer's Guide provides a structured decision framework. For enterprise deployments requiring VPC isolation or compliance logging alongside cost controls, the Bifrost Enterprise page covers the full enterprise feature set.

Start Controlling LLM Spend with Bifrost

Book a demo with the Bifrost team to see how per-consumer budgets, semantic caching, and cross-provider routing reduce LLM spend across your organization.