The Best AI Gateways for Cost Control and Observability in 2026

There's a moment in most AI programs when finance forwards the monthly model bill and asks a question nobody can answer: which feature is responsible for most of this? LLM spend has a way of climbing quietly. A single buggy loop, an over-eager agent, or an embedding workload that quietly dominates traffic can run up a bill, and if requests aren't attributed to anything, you can't tell which team or feature to talk to. The same blind spot applies to debugging: when a response goes wrong, you need to see exactly what happened, and a raw provider integration leaves no trace.

An AI gateway fixes both problems by sitting in the request path, where it can meter every call and record every trace. Here are five gateways that do cost control and observability well, ranked for teams where visibility and spend discipline are the priority.

What to look for

Good cost and observability support has a recognizable shape: spend attribution broken down by team, feature, model, and user, not just one aggregate number; budgets and rate limits you can attach to those same identities to cap runaway usage; request-level traces capturing prompts, responses, latency, tokens, and errors; and metrics that plug into the observability stack you already run, usually via OpenTelemetry. The five below all offer visibility; they differ in granularity and in how neutral they are across your whole stack.

1. TrueFoundry — attribution and tracing across everything

TrueFoundry leads because it makes cost and behavior visible across models, agents, and teams from one place, wherever your traffic runs. Its AI Gateway attributes spend by user, team, and feature, lets you attach budgets and rate limits to those identities so a runaway job hits a ceiling instead of the whole budget, and emits OpenTelemetry-compliant metrics, traces, and request logs so LLM traffic lands in the same observability discipline as the rest of your systems.

The proof is in how customers use it. FloQast layers per-feature accounts on top of the gateway to see spend per product feature and know which ones earn their budget, tracking inference cost attributable feature by feature. Staffbase, running enormous volume dominated by embeddings, gets a single attributable view across roughly 22 features and teams, which is how it keeps spend far below what raw request counts would suggest. That per-feature clarity, at scale, is documented in TrueFoundry's Staffbase case study.

Best for: teams that need spend attribution and tracing across many features, providers, and agents at once. Watch-out: the depth is worth a proper setup to exploit fully.

2. Cloudflare AI Gateway — clear visibility with almost no setup

Cloudflare AI Gateway gives you observability and cost tracking fast. Its dashboard logs individual requests with the prompt, response, provider, timestamp, status, token usage, cost, and duration, and surfaces per-provider cost analytics, request volumes, and error rates, with OpenTelemetry integration for monitoring. Response caching cuts spend directly by serving repeat prompts without hitting the provider.

For getting real visibility quickly, especially if you're on Cloudflare, it's excellent. The trade-off is granularity and reach: its attribution is lighter than a platform purpose-built for per-feature and per-team chargebacks, and as a managed edge service it's less of a neutral control plane across your whole multi-cloud footprint.

Best for: teams wanting solid request-level visibility and caching-driven savings quickly. Watch-out: lighter on fine-grained team and feature attribution.

3. Databricks Mosaic AI Gateway — observability inside your data platform

If your data already lives in Databricks, its Mosaic AI Gateway keeps AI observability in the same place. It centralizes routing with usage tracking, rate limits, and policy enforcement, and captures requests and responses in Unity Catalog tables so you can monitor, audit, and run team-level chargebacks with the familiar data tools you already use. Inference tables continuously record inputs, outputs, status codes, and latency for each endpoint.

That's powerful for teams standardized on Databricks, because your AI telemetry becomes just another governed dataset you can query and visualize. The consideration is that this strength is ecosystem-bound: as a neutral observability layer spanning providers and apps outside Databricks, it's less flexible than a platform-independent gateway.

Best for: Databricks-native teams that want AI telemetry in Unity Catalog. Watch-out: value is tied to the Databricks ecosystem.

4. Apigee — consumption analytics and cost management

Google Cloud's Apigee brings mature analytics to LLM traffic. Used as an AI gateway, it offers deep insights into LLM consumption through custom analytics and data collectors, paving the way for cost management and even internal monetization, alongside quotas and spike-arrest policies that keep usage and spend inside defined tiers. For organizations that already run Apigee, this extends familiar API analytics to model traffic.

Its strength is a proven, full-lifecycle analytics and governance platform. The consideration is that it treats AI as an extension of API management, so some LLM-native cost views come through configuration and custom collectors rather than as purpose-built, out-of-the-box AI dashboards.

Best for: enterprises already on Apigee wanting mature consumption analytics over LLMs. Watch-out: expect an API-analytics model rather than an AI-first one.

5. Azure API Management AI Gateway — cost governance in the Microsoft stack

For Microsoft-centric teams, the AI gateway capabilities in Azure API Management provide cost and observability control within Azure. It mediates interactions between apps, agents, and models with consistent enforcement of cost controls and observability, and logs traces over OpenTelemetry to Application Insights for deep correlation between API and agent execution. Exposing multiple backends through one endpoint means governance and monitoring policies apply once across models.

The appeal is tight integration with Azure monitoring and identity, so AI telemetry sits alongside everything else you already watch in Azure. The familiar caveat applies: it's most powerful inside the Azure and Foundry ecosystem, and less of a neutral cost-and-observability plane across a broader multi-cloud estate.

Best for: Microsoft-centric teams governing AI cost and telemetry within Azure. Watch-out: strongest inside the Azure ecosystem.

How to choose

If you want visibility fast and light, Cloudflare gets you request-level logs and caching savings in minutes. If your telemetry should live where your data already is, Databricks (Unity Catalog) or Azure (Application Insights) keep it in the platform you run, and Apigee extends analytics you may already operate. Each is strongest inside its own world. But if you need spend attributed by feature and team across every provider and agent, in one neutral place, with traces in your own OpenTelemetry stack, TrueFoundry covers the widest surface, backed by customers doing exactly that at scale.

The bottom line

You can't control what you can't see, and with LLMs the two things worth seeing are where the money goes and what actually happened on each request. Every gateway here delivers visibility, and several keep it neatly inside a platform you already run. The differentiator is granularity and reach: whether you can attribute spend feature by feature across your whole stack and trace any call end to end. For teams serious about cost discipline and observability, TrueFoundry is the one to beat.