If you run LLM workloads at any scale, you have probably already had the awkward Slack message: "why did our OpenAI bill jump 40% last month?" That conversation is now a monthly ritual at most engineering orgs. Per Menlo Ventures' 2025 State of Generative AI in the Enterprise report, enterprise AI spend reached $37 billion in 2025 (triple the prior year), with $12.5 billion of it flowing through foundation model APIs. Three providers in production, half a dozen coding agents firing autonomous request chains, finance asking who actually spent what. Without proper LLM cost controls, that question simply has no clean answer.
This post walks through five tools for LLM cost controls in enterprises, leading with Bifrost, the open-source AI gateway from Maxim AI. Each tool sits at a different layer of the stack: gateway-level enforcement, observability-based attribution, lightweight Python proxying, APM-native monitoring, and multi-cloud FinOps rollups. Most teams end up combining two or three rather than picking one.
What Effective LLM Cost Controls Look Like
LLM cost controls are the policies, telemetry, and enforcement points that decide how much your org spends on inference, where that spend gets attributed, and whether usage can be stopped before the bill arrives. Real controls combine three things: per-request attribution, in-path budget enforcement, and hierarchical limits that map to your org chart.
In production, that translates into:
- Attribution per request, scoped to a team, project, customer, or feature
- Budget logic that blocks the provider call before it happens, not after the invoice
- Layered caps so a virtual key budget, a team budget, and an org-wide ceiling all apply at once
- Cross-provider visibility for OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google Vertex, and the rest
- Audit trails strong enough to satisfy SOC 2, GDPR, and HIPAA reviewers
The five tools below differ on which of these they enforce, which they only observe, and which they push out to other systems entirely.
1. Bifrost: Real-Time Budget Enforcement at the Gateway
Bifrost is the open-source AI gateway built specifically for production LLM stacks. Of the tools listed here, it is the only one that enforces LLM cost controls in the request path itself, before any token is shipped to a provider. Each request enters Bifrost's governance layer, runs through budget checks, rate limits, and access policies across four scopes (customer, team, virtual key, and provider config), and gets rejected inline if any cap is hit.
The core abstraction is the virtual key. One virtual key bundles together provider access, model permissions, rate limits, and a budget into a single credential. Platform teams hand out distinct virtual keys per team, project, or customer, so every request carries clean attribution metadata from the second it lands at the gateway. Provider API keys themselves stay encrypted inside Bifrost and never reach end users.
The budget management system supports calendar-aligned reset cycles (daily at midnight UTC, weekly on Mondays, monthly on the 1st, or yearly) and enforces budgets hierarchically:
- Org-level budgets covering company-wide monthly spend caps
- Team-level budgets that aggregate across all virtual keys belonging to a team
- Virtual key budgets for per-app, per-developer, or per-environment limits
- Provider config budgets for per-provider ceilings inside one key (e.g., Anthropic capped at $200/month, OpenAI at $300/month)
On top of budget enforcement, Bifrost ships two cost-reduction levers that need zero application changes. Semantic caching returns cached results for semantically similar queries, killing duplicate provider calls outright. Automatic fallbacks shift traffic to cheaper models or alternate providers as budgets tighten or primary providers degrade. The Bifrost governance resource page documents the full enterprise governance surface, including RBAC, SSO via Okta and Entra, and immutable audit logging.
Performance overhead is the question every infra team raises first. Bifrost's published benchmarks record 11 microseconds of overhead per request at 5,000 RPS in sustained tests. Cost enforcement adds nothing measurable to production latency.
Best for: Platform teams running multi-team or multi-tenant LLM deployments, enterprises that need real-time enforcement (not retroactive alerting), and orgs migrating off Python-based proxies. If you are evaluating gateway alternatives, the LiteLLM alternative comparison lays out the migration path.
2. Langfuse: Cost Attribution at the Observability Layer
Langfuse is an open-source LLM observability platform that records every LLM call as a trace, attaching token counts, model name, latency, and cost to each span. Cost calculation happens at ingestion time by matching the model identifier against an internal pricing database covering OpenAI, Anthropic, Google, and the rest of the major providers, including pricing tiers, reasoning tokens, and cached tokens. Cost data sits next to quality and latency telemetry inside the same dashboard.
The strong suit of Langfuse is depth of attribution. Cost can be sliced by individual request, user, session, or any custom dimension a team chooses to attach to a trace. That makes the "which feature is burning the most tokens" question actually answerable without rebuilding a logging stack. The trade-off is that Langfuse is observability-first: it surfaces and dashboards spend, it does not enforce it. Budget caps, rate limits, and request-blocking are not in scope.
Best for: Engineering teams that want request-level cost visibility correlated with quality and performance metrics, and that are willing to instrument their app via the Langfuse SDK or OpenTelemetry. Teams that need both deep attribution and active enforcement typically run Langfuse alongside a gateway like Bifrost.
3. LiteLLM: Per-Key Budgets in a Python Proxy
LiteLLM is a Python proxy that fronts 100+ LLM providers behind a unified OpenAI-compatible API. Its cost-control story is simpler than Bifrost's: budgets are configured at the API key, user, team, or project level via virtual API keys, with usage logging and per-key spend limits. Once a key burns through its budget, requests fail until the cycle resets.
LiteLLM works fine as a lightweight proxy for early-stage LLM consolidation, especially in Python-heavy stacks. Where it falls down is at scale: the Python runtime adds gateway overhead in the hundreds of microseconds to milliseconds per request, and the budget hierarchy lacks the customer-level and provider-config-level granularity that real enterprise governance demands. Teams running coding agents or high-throughput RAG pipelines tend to hit operational limits and migrate to a Go-based gateway. The Bifrost migration guide for LiteLLM walks through feature parity and the cutover path.
Best for: Smaller teams in Python-first stacks that need basic per-key budget caps and broad provider coverage, and that are not yet ready for enterprise governance hierarchies.
4. Datadog LLM Observability: Cost Inside Your APM Stack
Datadog LLM Observability extends Datadog's APM platform to LLM workloads, capturing prompts, completions, token counts, and cost data in the same dashboards that already host application performance and infrastructure metrics. For shops already standardized on Datadog, this removes the need for a separate LLM-specific observability vendor and ties cost spikes directly to the app traces driving them.
What it cannot do is enforce. Datadog is observability-first; it gives you cost dashboards and alerting, but does not block requests when budgets break or run hierarchical budget logic at the request layer. Pricing also scales with ingestion volume, so it gets expensive quickly as call counts move into millions per month. Bifrost integrates with Datadog through a native connector for APM traces and LLM metrics, which is a common stack: Bifrost handles enforcement, Datadog handles the unified observability pane.
Best for: Orgs already running Datadog as their primary observability platform that want LLM cost data in the same UI as their application performance metrics.
5. CloudZero: Multi-Cloud FinOps for AI Spend
CloudZero is a FinOps platform built around unified cost visibility across AWS, Azure, GCP, and other cloud infrastructure. For LLM workloads routed through cloud-hosted model APIs (Azure OpenAI, AWS Bedrock, Google Vertex AI), CloudZero ingests provider invoices and allocates the costs alongside the rest of cloud spend, applying the same tagging, anomaly detection, and chargeback workflows used for compute and storage.
The benefit here is consolidation: AI spend lands in the same FinOps platform that finance and engineering already use to govern the rest of cloud infrastructure. The constraint is that CloudZero works at the billing aggregate level rather than the request level. It can show that the AI engineering team spent $40,000 on Bedrock last month, but it cannot block the next request that would push them past budget, and it has limited visibility into direct OpenAI or Anthropic API calls that bypass cloud billing channels.
Best for: Engineering and finance teams managing multi-cloud budgets that want AI spend folded into their existing cloud cost allocation framework, particularly when most LLM traffic flows through Azure OpenAI, Bedrock, or Vertex AI.
How Most Enterprises Combine LLM Cost Control Tools
In practice, almost no enterprise picks just one tool for LLM cost controls. The pattern that shows up over and over is a layered stack:
- A gateway layer (Bifrost) for real-time enforcement, virtual key attribution, and provider routing
- An observability layer (Langfuse or Datadog) for feature-level cost attribution and quality correlation
- A FinOps layer (CloudZero) for multi-cloud rollups and finance-team chargeback
The first call is whether the team needs enforcement or only visibility. Visibility tools tell you spend went up; enforcement tools stop it from going up past the cap. As enterprise AI spend keeps scaling (Menlo's mid-year 2025 update clocked LLM API spend doubling in just six months), the cost of relying on retroactive observability has gone up just as fast. One agent loop deployed to prod incorrectly can vaporize a quarterly budget overnight.
For teams that need real enforcement in the request path, gateway-layer cost controls are the foundation. Visibility and FinOps tools layer cleanly on top, but they cannot stand in for a gateway that blocks the call before it ever leaves your infrastructure.
Get Started With Bifrost for Enterprise LLM Cost Controls
Bifrost is open source, deploys in under 30 seconds with no configuration, and runs as a drop-in replacement for existing OpenAI, Anthropic, AWS Bedrock, and LangChain SDKs. Point your applications at the Bifrost endpoint, create virtual keys per team or project, set budget ceilings that match your billing cycle, and enforcement is on. No application code changes beyond the base URL.
To see how Bifrost handles LLM cost controls in your enterprise stack, book a demo with the Bifrost team or sign up to get started.
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