Tracking and containing LLM spending across multiple providers is impossible without a centralized control point. Bifrost is the best choice for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability, with per-consumer budgets, semantic caching, and unified cost visibility spanning every provider.
Enterprise AI budgets grow faster than the teams responsible for them can follow. With several providers (OpenAI, Anthropic, Google Vertex, AWS Bedrock), dozens of applications, and scores of developers each making independent API calls, monthly LLM spend regularly catches finance teams off guard. The structural root cause is direct provider API access: it offers no centralized cost attribution, no per-consumer budget enforcement, and no way to stop any individual service or developer from consuming an outsized share of quota. An AI gateway solves this by routing every AI request through a single control point where spending limits, routing decisions, and cost visibility are enforced automatically.
This guide compares the five most capable enterprise AI gateways for controlling LLM spend across providers in 2026.
What an AI Gateway Must Provide to Control LLM Costs
A gateway earns the "LLM cost control" label only when it delivers:
- Per-consumer budget enforcement: Daily or monthly token or dollar spend caps per user, team, or application, enforced automatically rather than simply reported after the fact.
- Semantic caching: Cached responses for similar queries to eliminate redundant API calls.
- Cross-provider cost visibility: A single cost view across all providers, not per-provider dashboards that require manual consolidation.
- Cost-optimal model routing: Rules that send different workload types to the most cost-appropriate model, not always the highest-capability one.
- Rate limiting: Per-consumer request controls that stop throughput spikes from generating unexpected costs.
- Real-time alerts: Spend notifications that fire before monthly budgets are exhausted.
1. Bifrost
Bifrost is the open-source AI gateway written in Go by Maxim AI. It delivers 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 cost control mechanism in Bifrost. Each consumer (user, team, service, or application) receives a virtual key with an explicit budget limit: a daily or monthly cap on token spend or dollar spend. When a virtual key hits its limit, requests are blocked at the gateway before reaching any provider. There are no retroactive overages.
Rate limits complement budget limits by capping throughput: a batch job with a high token budget can still be rate-limited to prevent bursts that crowd out interactive workloads sharing the same provider quota.
Semantic caching reduces API calls by returning cached responses for semantically equivalent queries. For applications with repeated query patterns (support bots, FAQ assistants, document analysis pipelines, code review workflows), this is the highest-leverage cost reduction available.
Routing rules and provider routing direct workloads to cost-appropriate models automatically:
- Batch summarization routes to lower-cost models (GPT-4o-mini, Claude 3 Haiku)
- High-complexity reasoning routes 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%, cutting inference costs directly for agent-based workflows. The MCP Gateway resource page and the MCP token cost analysis blog document those savings in detail.
Bifrost's built-in observability provides real-time cost breakdowns by virtual key, model, and provider, with export to Prometheus, OpenTelemetry, Grafana, and Datadog through the Datadog connector.
Access profiles in the enterprise tier let teams apply reusable budget policy templates at scale, avoiding 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 inside their AWS billing dashboards. Teams using Bedrock-native models (Claude, Titan, Llama on Bedrock) who want consolidated spend reporting alongside other AWS services.
Cost control capabilities: AWS Budgets alert when Bedrock spend approaches a defined threshold. Service quotas cap 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 sits at the AWS account level unless multiple accounts are used. Semantic caching is not available. Cross-provider 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 using Azure OpenAI who want spend controls inside Azure's existing cost management framework. Teams with existing APIM deployments who want consistent policy application across all API types including AI endpoints.
Cost control capabilities: APIM policies enforce rate limits and token quotas per API subscription. Azure Cost Management supplies spend reporting and budget alerts across Azure OpenAI and other Azure AI services. Reserved capacity options let teams 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 natively available. 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 fire 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 time window. 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
| Capability | Bifrost | AWS Bedrock | Azure AI Foundry | GCP Vertex AI | Kong AI |
|---|---|---|---|---|---|
| Per-consumer budget limits | Yes | No | No | No | Plugin |
| Per-consumer rate limits | Yes | Service quotas | APIM quotas | Service quotas | Plugin |
| Semantic caching | Yes | No | No | No | Plugin |
| Cross-provider cost visibility | Yes | AWS only | Azure only | GCP only | Yes |
| Routing to cost-optimal models | Yes | Manual | Manual | Manual | Plugin |
| MCP token cost reduction (Code Mode) | Yes | No | No | No | No |
| Real-time cost alerts | Yes | AWS Budgets | Azure Budgets | Cloud Budgets | External |
| Open source | Yes | No | No | No | Partial |
| Self-hosted / VPC | Yes | AWS only | Azure only | GCP only | Yes |
How to Choose 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 available. It is the only platform in this comparison with built-in semantic caching, budget limits that enforce rather than just alert, and Code Mode for MCP token cost reduction.
Cloud-native options (AWS, Azure, GCP) suit organizations deeply committed to a specific cloud provider who accept the governance and cost-control trade-offs that come with that dependency.
The LLM Gateway Buyer's Guide provides a structured framework for evaluating AI gateway cost control capabilities in detail. For enterprise deployments that require VPC isolation or compliance logging alongside cost controls, the Bifrost Enterprise page covers the full enterprise feature set.
Bring LLM Spend Under Control with Bifrost
Request a demo with the Bifrost team to see how per-consumer budgets, semantic caching, and cross-provider routing reduce LLM costs across your organization.
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