Top 5 Enterprise AI Gateways to Track LLM Cost

The best enterprise AI gateways for LLM cost tracking give teams hierarchical budget controls, per-request attribution, and real-time enforcement before overages hit. Here are five worth evaluating.

LLM API costs are one of the fastest-growing line items in enterprise technology budgets. A customer support agent handling 10,000 daily conversations can generate over $7,500 per month in API costs alone, and that is a single use case. Scale that across multiple teams, products, and providers, and spend becomes genuinely difficult to attribute or control. The root problem is architectural: when applications call LLM providers directly, there is no shared layer to enforce budgets, cache repeated queries, route to cost-optimal models, or even track where tokens are being consumed.

Enterprise AI gateways solve this by sitting between applications and LLM providers, centralizing cost attribution, budget enforcement, and traffic optimization behind a single control plane. This guide evaluates five options on the dimensions that matter most for finance and platform teams: attribution granularity, budget enforcement mechanisms, caching capabilities, and observability integration.

What Cost Tracking Actually Requires in Production

Basic spend dashboards are not enough for production AI deployments. Enterprise cost tracking needs to answer four questions:

• Who is consuming what? Attribution at the team, application, virtual key, and model level
• Are budgets being enforced before overages occur? Real-time limits, not post-bill reconciliation
• Are we paying for redundant work? Semantic caching to eliminate repeated identical queries
• Where is the hidden spend? Token overhead from embeddings, retries, and unoptimized prompts

The gateways below take materially different approaches to each of these.

1. Bifrost

Best for: enterprises that need hierarchical budget enforcement, real-time cost attribution, and semantic caching in a single open-source gateway with sub-millisecond overhead

Bifrost is a Go-native, open-source AI gateway built by Maxim AI that routes traffic across 20+ LLM providers through a single OpenAI-compatible API. Cost tracking is built in as a core capability, not a reporting add-on. Every request that passes through Bifrost is automatically logged with token counts, model selection, provider, latency, and computed cost, giving teams a unified ledger of spend regardless of which provider served the request.

Four-tier budget hierarchy

Bifrost's governance model uses virtual keys as the primary cost control unit. Each virtual key carries independent budget limits, rate limits, and reset durations. Budget thresholds operate across four tiers:

• Virtual key level: per-consumer or per-application spend caps
• Team level: aggregated limits across a group of virtual keys
• Customer level: budget controls for external customers or business units
• Organization level: enterprise-wide ceiling

This hierarchy lets finance teams set top-level constraints while platform teams manage allocation downstream, without any layer having to coordinate with another to enforce its own limits.

Semantic caching

Bifrost's semantic caching uses a dual-layer approach: exact hash matching for identical queries (zero cost on cache hit) and semantic similarity search for near-identical queries (only the embedding lookup is charged). For production workloads where a meaningful share of traffic is semantically equivalent queries phrased differently, this directly reduces token spend without any application code changes.

Observability and cost attribution

Built-in observability exposes native Prometheus metrics and OpenTelemetry traces that carry cost and token metadata per request. Teams running Grafana, Datadog, or New Relic can ingest these metrics into existing dashboards without building custom instrumentation. The performance benchmarks show Bifrost adds 11 microseconds of overhead at 5,000 RPS, which means the gateway itself does not contribute meaningfully to token spend or latency costs.

For enterprise deployments, Bifrost adds audit logs that produce immutable records of every request, including cost metadata, sufficient for SOC 2, HIPAA, and GDPR compliance requirements. The LLM Gateway Buyer's Guide covers how these governance features compare across gateway categories in more depth.

Bifrost is open source under Apache 2.0 on GitHub.

2. Kong AI Gateway

Best for: organizations already routing API traffic through Kong Konnect who want to extend existing API governance to cover LLM cost controls

Kong's AI Gateway, introduced with version 3.0 and expanded significantly in 2025, adds LLM-specific cost controls to Kong's mature API management platform. The primary cost-relevant capability is the AI Rate Limiting Advanced plugin, which enforces limits based on token consumption rather than raw request counts, aligning controls with how providers actually bill.

Per-model rate limits allow different thresholds for different models (GPT-4o versus Claude Sonnet, for example), which is important when a single application mixes high-cost and low-cost models in the same request flow. Semantic caching reduces redundant provider calls, and Kong's enterprise analytics dashboards track AI consumption as both API requests and token usage for FinOps reporting.

The cost tracking capabilities are solid for teams already invested in Kong's platform. The main constraint for teams evaluating Kong specifically for LLM cost management is that the full feature set assumes an existing Kong deployment; teams without prior Kong infrastructure will pay for API management capabilities beyond what LLM cost tracking requires. Enterprise licensing for Kong runs above $50,000 per year.

3. LiteLLM

Best for: Python-heavy teams that need basic multi-provider spend tracking during development and prototyping, where enterprise-grade budget hierarchy is not an immediate requirement

LiteLLM is an open-source Python library and proxy server that standardizes access to 100+ LLM providers. Its cost tracking features cover per-API-key and per-team spend with basic budget limits and virtual key management. For teams starting out on LLM cost visibility, these features are functional and straightforward to configure.

LiteLLM's limitations become more relevant as workloads scale. The Python-based architecture introduces measurable latency at high concurrency due to the Global Interpreter Lock: benchmarks show P99 latency increasing significantly at 500+ RPS compared to Go-based alternatives. That latency overhead itself has cost implications, since slower responses at scale require more infrastructure to maintain throughput targets. Enterprise budget features including SSO, RBAC, and team-level enforcement are behind the paid Enterprise license rather than included in the open-source version.

LiteLLM is a reasonable starting point for teams that need provider unification and basic spend visibility during early-stage deployments, with the understanding that cost control sophistication will need to grow as usage scales.

4. MuleSoft AI Gateway

Best for: enterprises running MuleSoft's integration platform that want LLM cost attribution integrated with their existing API management and FinOps infrastructure

MuleSoft's AI Gateway, which reached general availability in 2025, brings LLM cost tracking into MuleSoft's existing governance model. Token consumption is tracked at three levels: by LLM proxy, by application, and by business group, with daily and monthly reporting that aligns with how enterprise FinOps teams structure budget reviews.

Token budgets and rate limits are enforced at the gateway before overages occur rather than discovered after the fact in a cloud bill, which addresses one of the most common failure modes in enterprise LLM deployments: teams learning about spend overruns from the monthly invoice rather than a real-time alert. MuleSoft's approach to policy enforcement also ensures that governance is consistent across teams rather than implemented differently by each group building on the platform.

The practical constraint is integration dependency. MuleSoft AI Gateway's cost tracking capabilities assume you are already on Anypoint Platform. Teams evaluating it as a standalone LLM cost management layer will encounter significant platform overhead. It is most compelling for organizations where MuleSoft already manages integration infrastructure and LLM traffic needs to join an existing governance model rather than introduce a new one.

5. Datadog LLM Observability

Best for: enterprises already running Datadog for infrastructure monitoring that want unified cost visibility across traditional services and AI workloads in a single platform

Datadog's LLM Observability module extends the Datadog platform with per-trace token and cost analytics, pulling actual billing data from provider APIs rather than estimating costs from token counts. For organizations where LLM spend needs to be analyzed alongside infrastructure costs (compute, storage, API calls), this unified view reduces the reporting overhead of reconciling data from separate systems.

Every prompt trace includes token count and cost figures for each LLM call span, enabling teams to identify which specific prompts or agent steps drive disproportionate spend. This trace-level attribution is particularly useful for debugging cost spikes in multi-step agent workflows where the expensive operation may not be obvious from aggregate metrics.

Datadog LLM Observability is an observability-layer cost tracking tool rather than a gateway. It does not enforce budget limits, route traffic, or cache responses at the infrastructure level. Teams using it for cost management will still need a gateway layer for enforcement; Datadog handles the attribution and analysis, not the control plane that stops overages before they occur.

Comparing the Five on Cost-Critical Dimensions

How to Choose

Choose Bifrost if you need a standalone cost control layer with no platform dependency, hierarchical budget enforcement across teams and consumers, semantic caching to reduce redundant spend, and open-source transparency with enterprise support available.

Choose Kong if LLM cost controls need to sit inside an existing Kong Konnect deployment and consolidating AI governance with API governance is a priority.

Choose LiteLLM if your team is early in LLM deployment, primarily uses Python tooling, and needs basic spend visibility before investing in enterprise-grade governance infrastructure.

Choose MuleSoft AI Gateway if MuleSoft already manages your integration layer and LLM spend attribution needs to join an existing FinOps model rather than create a parallel one.

Choose Datadog if your priority is trace-level cost attribution and analysis within an existing Datadog deployment, and enforcement (budget limits, caching) will be handled by a separate gateway layer.

Get Started with Bifrost

Bifrost's hierarchical budget management, semantic caching, and per-request cost attribution are available in the open-source build on GitHub. To see how enterprise cost controls work across teams, providers, and compliance requirements, book a demo with the Bifrost team.