5 Enterprise AI Gateways to Control AI Costs

Enterprise AI costs are rising fast. These five AI gateways give platform teams the routing, caching, and budget controls needed to manage LLM spend at scale.

LLM API costs are one of the fastest-growing line items in enterprise technology budgets. A single customer support application handling 10,000 daily conversations can generate thousands of dollars per month in provider API fees. Multiply that across multiple teams, products, and providers, and spend quickly becomes unpredictable. The root cause is architectural: when every application calls LLM providers directly, there is no shared layer to enforce budgets, cache repeated queries, route to cost-optimal models, or attribute spend by team.

An enterprise AI gateway solves this by sitting between your applications and LLM providers, adding cost governance, routing intelligence, and observability through a single infrastructure layer. This post covers five enterprise AI gateways that give engineering and platform teams meaningful control over AI costs, starting with the one that does it most completely.

What to Look For in an Enterprise AI Gateway for Cost Control

Before comparing options, it helps to define what cost control actually requires at the infrastructure layer. A gateway that only exposes spend dashboards is not the same as one that enforces budget limits in real time. The key capabilities are:

• Hierarchical budget management: Set spending limits at the organization, team, consumer, and API key level, not just at the account level with your provider.
• Semantic caching: Serve cached responses for semantically similar queries, not only exact matches, to eliminate redundant API calls.
• Intelligent model routing: Direct requests to cost-optimal models or providers based on defined rules, not just availability.
• Token-based rate limiting: Control token consumption per time window, not just request counts, since tokens are what providers actually bill.
• Per-request cost attribution: Log cost, token usage, and latency on every request so teams can identify where spend is concentrated.

With those criteria in mind, here is how five leading enterprise AI gateways compare.

1. Bifrost

Best for: Enterprise teams that need production-grade cost governance, open-source transparency, and multi-provider access through a single gateway.

Bifrost is an open-source, Go-native AI gateway built by Maxim AI. It unifies access to 20+ LLM providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, Azure OpenAI, Groq, Mistral and 1000+ models through a single OpenAI-compatible API. It adds only 11 microseconds of overhead per request at 5,000 requests per second, making cost controls at the gateway layer effectively invisible to application performance.

Cost Control Capabilities

Bifrost's cost governance operates through a four-tier budget hierarchy: customer (organization level), team (department level), user (individual level), and virtual key (API key level). Every request must pass budget checks at all applicable levels before it proceeds. If any tier has exceeded its budget, the request is blocked and a structured error is returned. This hierarchy means a single product team cannot exhaust a shared budget without platform-level controls catching it first.

Budget and rate limits support both rolling and calendar-aligned reset windows. You can set a team budget that resets at the start of each month or a virtual key budget that rolls over every 24 hours. Both request-count and token-count limits operate in parallel, so teams can enforce spend in terms of dollars, requests per minute, or tokens per hour simultaneously.

Semantic caching reduces costs by serving cached responses for queries that are semantically similar, not just lexically identical. Bifrost uses a dual-layer approach: exact hash matching for perfect repeats and vector similarity search for near-duplicate queries. Cache hits on exact matches cost zero. Semantic hits cost only the embedding lookup. Bifrost supports Weaviate, Qdrant, Redis-compatible endpoints, and Pinecone as vector stores.

For routing, virtual keys support weighted provider routing and automatic fallback chains. A team running 80% of traffic through a cost-optimized Azure deployment can automatically fall back to OpenAI if Azure hits rate limits, without application code changes. Enterprise deployments can enable adaptive load balancing, which dynamically shifts traffic based on real-time provider performance metrics.

Additional cost-relevant capabilities include:

• Built-in observability with per-request cost and token logging, Prometheus metrics, and OpenTelemetry tracing
• Model allow-lists per virtual key, so teams cannot inadvertently route to expensive models they are not authorized to use
• In-VPC deployment for organizations that cannot route data through third-party infrastructure
• Audit logs for SOC 2, GDPR, and HIPAA compliance requirements

Bifrost is open source under Apache 2.0, available on GitHub. The enterprise tier adds clustering, RBAC, vault-backed key management, and identity provider integration with Okta and Microsoft Entra.

Limitations: Semantic caching requires a self-managed vector store. Teams without an existing Weaviate or Qdrant deployment will need to stand one up.

2. Kong AI Gateway

Best for: Enterprises already running Kong for traditional API management that want to extend LLM cost governance under the same operational layer.

Kong AI Gateway extends Kong's mature API management platform to handle LLM traffic. It brings token-based rate limiting, semantic caching, and enterprise analytics dashboards into the same control plane teams use for REST and GraphQL API governance.

Cost Control Capabilities

Kong's AI Rate Limiting Advanced plugin operates on actual token consumption rather than raw request counts, aligning rate limits with how providers bill. Teams can set model-level rate limits per model variant, for example enforcing tighter limits on expensive reasoning models than on smaller, cheaper alternatives. Semantic caching reduces redundant provider calls. Enterprise analytics dashboards track AI consumption across models, teams, and applications.

The primary trade-off is infrastructure dependency. Kong AI Gateway requires an existing Kong deployment. Teams without prior Kong infrastructure face a significant adoption curve before they can use its AI cost controls. Advanced AI-specific features are restricted to the Enterprise tier, whose pricing targets large organizations. The total cost of running Kong infrastructure can offset savings on LLM spend for smaller deployments.

3. Cloudflare AI Gateway

Best for: Teams already on Cloudflare's infrastructure that need lightweight cost visibility and basic caching with minimal operational overhead.

Cloudflare AI Gateway sits at the edge and adds a centralized logging and caching layer for LLM requests. Because it operates as a proxy between your application and LLM providers, setup requires only a URL change in most SDK configurations.

Cost Control Capabilities

Cloudflare AI Gateway provides request and token logging across providers, with usage dashboards that show spend aggregated by model and provider. Response caching reduces redundant API calls for repeated queries. Rate limiting is configurable per gateway instance.

The trade-offs are meaningful for enterprise use. Cloudflare AI Gateway is a managed service, meaning all LLM traffic routes through Cloudflare's infrastructure. Budget enforcement is limited compared to gateways that offer multi-tier spend controls. Organizations with data residency requirements or strict compliance obligations (SOC 2, HIPAA) will find governance capabilities less extensive than dedicated enterprise AI gateways.

4. AWS Bedrock

Best for: Organizations deeply invested in the AWS ecosystem that need predictable, capacity-based pricing for high-volume, stable workloads.

AWS Bedrock is a managed multi-model service rather than a standalone gateway. It provides access to foundation models from Anthropic, Meta, Mistral, Cohere, and Amazon through AWS's infrastructure, with billing and cost controls integrated into standard AWS account governance.

Cost Control Capabilities

Bedrock supports provisioned throughput pricing, which lets teams reserve model capacity at a fixed monthly rate rather than paying variable per-token fees. For workloads with predictable, stable volume, this can reduce costs materially compared to on-demand pricing. AWS CloudWatch integration provides monitoring of token usage, request counts, and estimated spend with native AWS alerting.

The cost control model differs from dedicated AI gateways. Bedrock does not offer hierarchical team-level budget enforcement or semantic caching as a native feature. Organizations running multiple teams or consumer segments need to implement budget attribution through AWS account structure or cost allocation tags, which requires more manual overhead than a gateway-native budget hierarchy. Bedrock is also provider-scoped to AWS, so multi-provider routing to OpenAI, Groq, or other non-AWS providers requires additional infrastructure.

5. LiteLLM

Best for: Python-heavy development teams that need broad model compatibility and basic spend tracking during development and early production.

LiteLLM is an open-source Python library that provides a unified interface for 100+ LLMs. It includes a proxy server that adds basic budget controls, logging, and routing on top of its model abstraction layer.

Cost Control Capabilities

LiteLLM's proxy supports per-key spend limits, request and token rate limits, and basic cost tracking dashboards. It integrates with a wide range of providers and includes fallback routing. For development teams that need a quick path to multi-provider access with basic spend visibility, it is a practical starting point.

At high concurrency and production scale, LiteLLM's Python-based runtime introduces meaningful latency overhead compared to Go-based gateways. In benchmark comparisons, Bifrost adds 11 microseconds of overhead at 5,000 RPS, while LiteLLM's overhead is substantially higher at equivalent throughput levels. For latency-sensitive production workloads, this difference compounds across millions of requests. Enterprise-grade features such as hierarchical budget enforcement, semantic caching, RBAC, and in-VPC deployment are not part of LiteLLM's core offering.

How to Choose an Enterprise AI Gateway for Cost Control

The right gateway depends on your team's scale, compliance requirements, and existing infrastructure:

• Full-stack cost governance with multi-tier budgets, semantic caching, and compliance-ready audit logs: Bifrost
• Cost controls embedded in an existing Kong deployment: Kong AI Gateway
• Edge-layer caching and basic logging for Cloudflare users: Cloudflare AI Gateway
• Capacity-based pricing for stable AWS workloads: AWS Bedrock
• Basic spend tracking during early development: LiteLLM

For most engineering teams scaling LLM infrastructure beyond a handful of services, the core requirements converge: hierarchical budget enforcement that blocks overspend in real time, caching that reduces redundant API calls at the semantic level, and per-request cost attribution that tells you exactly where tokens are going. Gateways that treat cost control as a first-class infrastructure concern, rather than a reporting layer bolted on after routing, are the ones that hold up in production.

Start Controlling AI Costs with Bifrost

Bifrost is available as open source on GitHub and can be running in minutes via a single npx command or Docker container. Its virtual key governance system, semantic caching, and hierarchical budget management give enterprise teams the cost controls that matter most in production, without requiring a platform rebuild.

To see how Bifrost handles enterprise AI cost control at scale, book a demo with the Bifrost team.