Top 5 Tools for LLM Cost and Usage Monitoring

As LLM-powered applications scale from prototype to production, token costs can spiral quickly. A single unoptimized prompt chain can multiply expenses by 10x, and without real-time visibility into usage patterns, teams often discover budget overruns only after the damage is done. Effective cost and usage monitoring has become a non-negotiable requirement for any team running AI workloads at scale.

This guide covers the five best tools for LLM cost and usage monitoring in 2025, evaluated on cost attribution granularity, multi-provider support, real-time alerting, and ease of integration.

Why LLM Cost and Usage Monitoring Matters

Unlike traditional API services with predictable per-call pricing, LLM costs depend on input tokens, output tokens, model selection, and increasingly, reasoning tokens that are invisible without proper instrumentation. Teams operating without dedicated monitoring infrastructure face several risks:

• Silent cost escalation: Verbose prompts, redundant API calls, and unoptimized context windows drain budgets without any visible error.
• Lack of attribution: Without per-request cost breakdowns, it is impossible to determine which features, users, or workflows are driving the highest spend.
• Provider lock-in: Teams using a single provider have no way to compare cost efficiency across models without a unified monitoring layer.
• Delayed incident response: Cost spikes from misconfigured agents or runaway loops go undetected until monthly invoices arrive.

A purpose-built monitoring tool addresses all of these gaps by providing real-time dashboards, granular cost attribution, and automated alerting across every LLM request in your stack.

1. Bifrost by Maxim AI

Bifrost is a high-performance, open-source AI gateway that delivers built-in cost and usage monitoring as a core feature rather than an afterthought. By routing all LLM traffic through a single unified interface, Bifrost provides comprehensive visibility into token consumption, latency, and spend across every provider and model in your stack.

What makes Bifrost stand out for cost monitoring:

• Multi-provider cost tracking out of the box: Bifrost supports 12+ providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, Mistral, and Groq through a single OpenAI-compatible API. Every request is logged with token counts and associated costs, giving teams a unified view of spend regardless of which provider serves the request.
• Hierarchical budget management: Bifrost's governance features enable teams to set usage limits and budgets at multiple levels — by virtual key, team, or customer. This prevents any single workflow or tenant from consuming disproportionate resources.
• Semantic caching for cost reduction: The built-in semantic caching layer identifies semantically similar requests and serves cached responses, directly reducing redundant API calls and lowering token spend without sacrificing output quality.
• Native observability infrastructure: Bifrost ships with built-in Prometheus metrics, distributed tracing, and comprehensive logging, enabling teams to build real-time cost dashboards and configure alerts on spend anomalies.
• Zero-configuration deployment: Unlike tools that require extensive setup, Bifrost offers zero-config startup and works as a drop-in replacement for existing OpenAI or Anthropic API calls with a single line of code change.

For teams that need cost monitoring embedded directly in their AI infrastructure — without bolting on a separate observability vendor — Bifrost is the most complete solution available. Combined with Maxim AI's observability platform, teams gain full-stack visibility from token-level cost tracking to production quality evaluation.

See more: Bifrost AI Gateway | Bifrost Documentation

2. LiteLLM

LiteLLM is an open-source LLM proxy that provides a unified interface for over 100 LLM providers with built-in spend tracking capabilities. It automatically maps model-specific token pricing and exposes cost data at the key, user, and team level.

• Granular spend attribution: Tracks costs per API key, per user, and per team, with daily activity breakdowns that include prompt tokens, completion tokens, and total spend.
• Custom tagging for cost allocation: Supports metadata tags on requests, enabling teams to categorize spend by application, environment, or business unit.
• Budget enforcement: Allows setting maximum budgets per key or user with automatic enforcement when limits are reached.
• Broad model coverage: Maintains an open-source model cost map that stays current with pricing changes across providers.

LiteLLM works well for engineering teams that need a lightweight, self-hosted proxy with reliable cost tracking and do not require a broader evaluation or quality monitoring platform.

3. Langfuse

Langfuse is an open-source LLM engineering platform that includes detailed token usage and cost tracking as part of its observability suite. It captures cost data at the generation and embedding level, with support for complex token types including cached tokens, audio tokens, and image tokens.

• Automatic cost inference: Ships with predefined tokenizers for popular models from OpenAI, Anthropic, and Google, automatically calculating costs even when the provider does not return token counts.
• Custom cost ingestion: Supports manual cost and usage data ingestion for models with non-standard pricing or reasoning token overhead.
• Trace-level cost breakdown: Attributes costs to individual spans within multi-step agent workflows, making it straightforward to identify which pipeline stage is the most expensive.
• Self-hosting option: Offers full self-hosted deployment for teams with data sovereignty requirements, at no license cost.

Langfuse is a strong choice for teams that want open-source cost tracking integrated with tracing and prompt management, though it lacks the gateway-level features like caching and automatic failover that reduce costs at the infrastructure layer. See how Maxim compares to Langfuse for teams evaluating both platforms.

4. Datadog LLM Observability

Datadog extends its enterprise monitoring platform with dedicated LLM observability features, combining cloud cost management with per-trace token and cost analytics. For organizations already running Datadog for infrastructure monitoring, this provides a unified cost view across both traditional services and AI workloads.

• Real cost data from provider billing: Integrates directly with provider APIs (such as OpenAI) to pull actual billed costs rather than estimates, ensuring accuracy at the organization and model level.
• Per-trace cost attribution: Every prompt trace includes token count and cost figures for each LLM call span, enabling teams to identify the costliest requests within complex workflows.
• Custom tags and team-level reporting: Tag pipelines allow cost allocation by team, project, or environment, with monitor-based alerts for budget overages.
• Unified infrastructure view: Correlates LLM costs with compute, storage, and network spend in a single dashboard.

Datadog LLM Observability is best suited for large enterprises with existing Datadog deployments. The minimum commitment starts at 100K monitored LLM requests per month, which puts it out of reach for smaller teams or early-stage projects.

5. Weights & Biases Weave

Weights & Biases extends its established ML experiment tracking platform with Weave, a framework for LLM observability that includes cost and token usage tracking alongside experimentation and evaluation workflows.

• Integrated experiment and cost tracking: Ties cost data directly to prompt experiments, enabling teams to evaluate not just quality but cost efficiency across prompt and model variations.
• Agent and multi-step tracing: Supports comprehensive tracing for agentic workflows with token and cost data at each step.
• Prompt Canvas collaboration: Provides a visual interface for prompt experimentation where cost implications are visible alongside quality metrics.
• Broad framework support: Works across major LLM providers and popular frameworks without vendor lock-in.

Weave is the right fit for teams already embedded in the Weights & Biases ecosystem for ML workflows who want to extend their existing toolchain to cover LLM cost monitoring. Teams starting fresh may find the ML-centric orientation adds unnecessary complexity.

Choosing the Right Tool

The right LLM cost monitoring tool depends on where your team needs visibility and control. For teams that want cost monitoring embedded directly into their AI gateway infrastructure with built-in caching, failover, and budget enforcement, Bifrost offers the most integrated solution. Paired with Maxim AI's full-stack observability and evaluation platform, it covers both the infrastructure and quality dimensions of LLM cost management.

Regardless of which tool you select, the critical step is implementing monitoring before costs become a problem, not after.