Can you answer what your company spent on AI last month, broken down by tool, team, and model, without opening five tabs or waiting three days?
That question is why AI cost visibility tools have become a practical requirement for FinOps, engineering, and finance leaders. AI spend now moves through model APIs, cloud-hosted inference, GPUs, agent workflows, developer tools, SaaS AI products, data platforms, and shared infrastructure. Each layer has its own billing logic. Each layer creates a different cost signal.
The FinOps Foundation’s 2026 research shows how quickly the discipline is expanding beyond cloud, with 98% of respondents managing AI spend and many also taking on SaaS, private cloud, data centers, and broader technology value management.
This guide evaluates five AI cost visibility tools worth considering in 2026: Holori, Langfuse, LiteLLM, Vantage, and Mavvrik (our own platform). Each tool is assessed against the same criteria, including category fit, primary persona, cloud integrations, AI coverage, attribution depth, pricing model, and tradeoffs.
Key takeaways:
- AI cost visibility needs both engineering context and finance-ready allocation.
- Token usage is not cost allocation. Teams need to know who owns the cost, what it supported, and whether it affected margin.
- AI FinOps tools vary widely across AWS, Azure, Google Cloud, OpenAI, and Anthropic support, so integration fit matters.
- Agentic AI needs workflow-level tracking because branches, retries, tool calls, and retrieval can multiply cost quickly.
- Native provider dashboards may work early. Dedicated tools become useful when spend spans multiple providers, shared infrastructure, SaaS AI tools, or manual tracking.
Best For: FinOps practitioners, finance and FP&A teams, engineering leaders, platform teams, and IT leaders evaluating AI cost visibility tools across LLMs, GPUs, cloud infrastructure, SaaS AI tools, and agent workflows.
What is AI Cost Visibility and Why is it Important?
AI cost visibility is the ability to see where AI spend is coming from, what is driving it, and who is responsible for it. It includes model API costs, GPU and cloud infrastructure, inference, embeddings, vector databases, orchestration, data platforms, AI developer tools, and agent workflows.
AI costs are consumption-based and unpredictable. A single product feature may generate different costs depending on model choice, prompt length, retrieval volume, retry behavior, user demand, and infrastructure placement.
For a deeper breakdown of the concept, read Mavvrik’s guide to AI cost visibility.
Why Traditional Cloud FinOps is Not Enough for AI
Cloud FinOps was built around infrastructure that could be tagged, allocated, reserved, rightsized, and forecasted from relatively stable usage patterns.
AI costs challenge that model because they are shaped by factors that keep changing inside the environment, from model choice and token volume to prompt length, retrieval, retries, and user demand.
Model APIs are priced by usage. OpenAI pricing varies by model, input tokens, cached input tokens, and output tokens, and tool usage can also generate token-based costs. Amazon Bedrock pricing also varies by model provider, modality, throughput model, and usage tier.
That means two requests to the same AI feature can have very different costs. One user may ask a short question. Another may trigger retrieval, long-context reasoning, tool calls, retries, and a larger output. The invoice may show usage, but it will not automatically explain which team, customer, product feature, model, or workflow created the spend.
The Four Types of AI Cost Visibility Tools
AI cost visibility tools do not all answer the same question. Some sit close to the application, while others work from billing data.
Proxy and gateway tools
Proxy and gateway tools sit between applications and model providers. They route requests, standardize provider access, enforce budgets or rate limits, and track usage as requests happen.
This is a good fit for platform teams that want more control over model access and provider spend. The tradeoff is operational. Traffic has to move through the gateway, and the team needs to maintain that layer.
Trace-level observability tools
Trace-level AI observability tools show what happens inside an LLM application. They capture prompts, completions, model calls, tool calls, retrieval steps, latency, token usage, cost, and session context.
Engineering teams use this level of detail to understand why a workflow became expensive. It helps explain the behavior behind a cost spike, but it usually needs billing data and ownership rules before finance can use it for allocation, chargeback, or cost-to-serve.
Billing-based visibility platforms
Billing-based platforms ingest cost and usage data from cloud providers, SaaS tools, and direct API providers. They help FinOps and finance teams see spend trends, budgets, reports, and allocation across services.
This category is strongest for understanding where spend is going across providers and accounts. The limitation is timing and detail. Billing data often arrives after usage happens, and it may not explain the prompt, request, workflow, or agent behavior that caused the spend.
Full-stack AI FinOps platforms
Full-stack AI FinOps platforms connect cloud, on-prem, GPUs, Kubernetes, model APIs, SaaS, agent workflows, and business context into one cost model.
This category is built for production AI environments where spend needs to be allocated to teams, products, customers, features, tenants, agents, or workflows. It supports showback, chargeback, cost-to-serve, forecasting, anomaly detection, and margin management across shared infrastructure.
How We Evaluated These AI Cost Visibility Tools
- Category fit: Each tool was evaluated based on what it is built to solve: gateway control, trace-level observability, billing visibility, or full-stack AI FinOps.
- Cost coverage: We looked at whether the tool covers model APIs, cloud providers, Kubernetes, GPUs, SaaS, data platforms, and agentic workflows.
- Attribution depth: The strongest tools connect spend to teams, products, customers, models, agents, features, and workflows rather than stopping at provider totals.
- Engineering usefulness: AI observability tools need to help engineers diagnose request-level behavior, including latency, prompt changes, retries, tool calls, and model choice.
- Finance readiness: Finance and FP&A teams need allocation, reporting, budgets, chargeback, cost-to-serve, and forecasting.
- Integration support: We checked AWS, Azure, and Google Cloud support, plus direct OpenAI, Anthropic, and other model provider coverage where available.
- Deployment and operational burden: Open-source and self-hosted tools can be flexible, but they also introduce maintenance. SaaS tools reduce operational overhead but may be less customizable.
- Agentic AI support: Agentic workflows introduce branching, tool usage, retries, and session-level cost patterns. We looked at whether tools can track this layer directly or only approximate it from billing data.
Best AI Cost Visibility Tools in 2026
The best AI cost visibility tools in 2026 fall into different categories. Some help engineers understand request behavior. Others help FinOps and finance teams allocate spend. A few are built for full-stack AI cost governance across infrastructure, models, SaaS, and agents.
| Tool | Category | Primary Persona | AWS, Azure, GCP Support | Key Capability | Honest Limitation |
|---|---|---|---|---|---|
| Holori | Billing-based cloud visibility | FinOps, DevOps, cloud engineering | AWS, Azure, GCP | Multi-cloud cost dashboards, virtual tagging, and graphical cost allocation | Public positioning is strongest for cloud and infrastructure visibility, not trace-level LLM or agent cost tracking |
| Langfuse | Trace-level AI observability | AI engineers, product engineering, platform teams | Supports model/provider integrations including AWS Bedrock, Azure OpenAI, Google AI Studio, and Vertex AI, but not cloud billing allocation | LLM traces, token and cost tracking, prompts, evaluations, and session analytics | Strong for explaining model and workflow behavior, but not a full finance allocation or chargeback platform |
| LiteLLM | Proxy and gateway | Platform engineering, AI platform teams | Supports providers such as AWS Bedrock, Azure OpenAI, Vertex AI, Gemini, OpenAI, and Anthropic | Unified model gateway with spend tracking, budgets, rate limits, routing, and virtual keys | Requires routing traffic through the gateway and operational ownership of that layer |
| Vantage | Billing-based FinOps visibility | FinOps, cloud cost teams, engineering managers | AWS, Azure, GCP | Cost reports, budgets, provider billing visibility, usage reports, and unit cost metrics | Billing-centric visibility does not replace request-level LLM observability or workflow-level agent tracking |
| Mavvrik | Full-stack AI FinOps platform | Finance, FP&A, FinOps, engineering, partners | AWS, Azure, GCP, plus on-prem, Kubernetes, GPUs, SaaS, LLM APIs, and agent workflows | Unified AI cost visibility, attribution, chargeback, cost-to-serve, forecasting, and agent-level cost tracking | Best fit for production AI and shared infrastructure; may be more than needed for a single-provider experiment |
Holori
In practice, Holori works best when the core need is multi-cloud infrastructure cost visibility, visual allocation, and optimization across AWS, Azure, and Google Cloud.
Primary persona: FinOps practitioners, DevOps teams, cloud engineers, and infrastructure leaders.
Strengths:
- Holori consolidates expenses from multiple cloud providers and supports filtering, allocation, and connection to AWS, Azure, and Google Cloud accounts.
- Its visual approach helps infrastructure teams map cloud cost data to architecture, resources, and ownership models.
- Holori supports virtual tagging and cost allocation workflows, which can help when native cloud tags are inconsistent or incomplete.
Limitation: Holori is strongest as a cloud and infrastructure visibility platform. It is not positioned as a trace-level AI observability tool for prompt, session, agent, or workflow-level LLM cost analysis.
Pricing model: SaaS, with a 14-day trial and a free tier for lower monthly cloud spend. Holori’s public pricing explains that tiering is based on the previous month’s total cloud costs.
Key integrations: AWS, Azure, and Google Cloud cost management and diagram integrations.
Best fit: Holori is a good fit when AI spend is mainly showing up as cloud infrastructure cost and the team needs clearer multi-cloud allocation, visual mapping, and optimization.
Langfuse
In practice, Langfuse works best when engineers need to understand why an LLM application, prompt, workflow, or agent step became expensive.
Primary persona: AI engineers, product engineers, platform teams, and teams building LLM applications.
Strengths:
- Langfuse captures hierarchical traces across LLM calls, tool invocations, retrieval, cost, latency, user, session, and metadata.
- It combines observability with prompt management, evaluations, analytics, and dashboards for LLM application development.
- Langfuse supports LLM integrations across providers including OpenAI, Anthropic, Google AI Studio, Vertex AI, and AWS Bedrock, and it can ingest usage and infer cost for supported models.
Limitation: Langfuse gives engineering teams strong workflow-level visibility, but it is not a full cloud AI spend management platform for enterprise allocation, chargeback, on-prem GPU cost modeling, or SaaS cost governance.
Pricing model: Open-source and SaaS. Langfuse offers a free Hobby plan, with paid plans for higher usage and business features.
Key integrations: OpenAI, Anthropic, Google AI Studio, Google Vertex AI, AWS Bedrock, Azure OpenAI, and OpenTelemetry-oriented workflows.
Best fit: Langfuse is a strong fit when the question is, “Which request, prompt, retrieval step, or model behavior caused this cost spike?”
LiteLLM
In practice, LiteLLM works best when platform teams want a central gateway for model access, usage tracking, spend controls, provider abstraction, and routing across many LLM providers.
Primary persona: Platform engineering teams, AI infrastructure teams, and developers managing multi-provider LLM access.
Strengths:
- LiteLLM provides an OpenAI-compatible gateway for 100+ providers, including OpenAI, Anthropic, Gemini, Bedrock, Vertex AI, and Azure.
- It supports spend tracking across keys, users, teams, models, and providers, including provider-specific tracking for Vertex AI, Bedrock, and Azure.
- It includes gateway features such as virtual keys, budgets, rate limits, routing, guardrails, load balancing, and an admin dashboard.
Limitation: LiteLLM requires teams to route model traffic through the gateway and operate that layer. It controls model access well, but it does not replace a full financial allocation system across cloud, SaaS, GPUs, Kubernetes, on-prem, and business ownership.
Pricing model: Open-source, self-hosted, and hosted options. Public enterprise guidance says pricing depends on deployment size and setup.
Key integrations: OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex AI, Gemini, and many additional LLM providers. Enterprise deployment options also reference AWS, Azure, and Google secret management integrations.
Best fit: LiteLLM is a strong fit when the priority is controlling provider access and routing LLM traffic through a shared gateway with spend limits.
Vantage
In practice, Vantage works best when FinOps and engineering teams need broad billing visibility across cloud providers, SaaS tools, Kubernetes, and direct API providers.
Primary persona: FinOps teams, cloud cost engineers, engineering managers, and finance stakeholders who need cost reporting across many services.
Strengths:
- Vantage positions itself as a cloud cost observability and FinOps platform with integrations across AWS, Azure, Google Cloud, Kubernetes, Snowflake, OpenAI, and other services.
- Its API integration list includes providers such as AWS, Azure, GCP, Snowflake, Databricks, Datadog, OpenAI, Kubernetes, Anthropic, and Cursor.
- Vantage supports Cost Reports, budgets, alerts, usage reporting, and business metrics for unit cost analysis such as cost per user, customer, or API request.
Limitation: Vantage is strongest as a billing and cost visibility platform. It does not replace trace-level LLM observability, and its Anthropic documentation notes scope limits for Claude usage hosted through Bedrock or non-API subscription and seat-based costs.
Pricing model: SaaS, with public tiers including Starter, Pro, Business, and Enterprise options.
Key integrations: AWS, Azure, Google Cloud, Kubernetes, OpenAI, Anthropic, Cursor, Databricks, Snowflake, Datadog, and many additional providers.
Best fit: Vantage is a good fit when the organization needs broad provider billing visibility and unit cost reporting across cloud, SaaS, and direct API sources.
Mavvrik
In practice, Mavvrik works best when AI cost visibility needs to become financial control across cloud, on-prem, GPUs, Kubernetes, SaaS, LLM APIs, and agentic workflows.
Primary persona: Finance leaders, FP&A teams, FinOps practitioners, engineering leaders, platform teams, product leaders, and partners managing customer environments.
Strengths:
- Mavvrik provides full-stack AI cost governance across GenAI services, agentic workloads, infrastructure, and SaaS/data platforms. Its 2026 product release covers token-level cost tracking, agent/session-level tracking, infrastructure across AWS, Azure, GCP, and private environments, plus SaaS and data platforms.
- Mavvrik connects cost visibility to attribution, chargeback, budgets, anomaly alerts, and cost-to-serve, which helps teams move from spend reporting to accountability.
- Its Agentic Cost Intelligence SDK captures token usage, latency, tool calls, costs, retry overhead, and business context across multi-step agent workflows using OpenTelemetry-native instrumentation.
Limitation: Mavvrik is best suited for teams operating AI in production, managing multiple providers, or allocating shared infrastructure. For a single team experimenting with one API provider, native billing plus a lightweight observability tool may be enough at first.
Pricing model: SaaS, with flat-rate pricing based on customer requirements rather than a percentage of savings.
Key integrations: AWS, Azure, Google Cloud, Oracle Cloud Infrastructure, Kubernetes, on-prem infrastructure, GPUs, LLM APIs, SaaS tools, data platforms, and agent workflows.
For SDLC visibility, Mavvrik also connects Claude Code and Claude Cowork spend to session-level activity, helping teams analyze usage by user, team, session, model, operation, LLM call, and tool call.
Best fit: Mavvrik is a strong fit when the question is, “How do we map every AI dollar to the team, product, customer, feature, agent, or workflow that created it?”
How to Choose the Right AI Cost Visibility Tool for Your Team
Start with the pain point, not the feature list. The right tool depends on where the cost problem shows up first: in the application, at the provider access layer, across infrastructure, or in finance reporting.
| Pain point | What it usually means | Tool category to evaluate first | Tools in this guide |
|---|---|---|---|
| Why are my AI costs suddenly so high? | You need to trace a spike back to the prompt, model call, retrieval step, tool action, or agent workflow that caused it. | Trace-level observability | Langfuse |
| How do I find what is driving LLM spend? | You need model usage, token activity, latency, cost, and session context at the request layer. | Trace-level observability | Langfuse |
| How do I track AI usage by team or project? | You need usage and spend mapped to owners, projects, products, customers, features, or agents. | Full-stack AI FinOps | Mavvrik |
| How do I stop AI spend from getting out of control? | You need budgets, rate limits, routing rules, alerts, or policy controls before spend turns into an invoice problem. | Proxy, gateway, or full-stack AI FinOps | LiteLLM or Mavvrik |
| How do I see AI costs without living in spreadsheets? | You need billing and usage data pulled from cloud accounts, model providers, SaaS tools, GPUs, and shared infrastructure. | Billing-based visibility or full-stack AI FinOps | Holori, Vantage, or Mavvrik |
| How do I track AI coding tool spend across developers, sessions, and workflows? | Developer AI usage is moving into the SDLC, but the cost data is still sitting at the invoice or provider-reporting level. | Full-stack AI FinOps with session-level attribution | Mavvrik |
Start with the pain point, not the feature list. The right tool depends on where the cost problem shows up first: in the application, at the provider access layer, across infrastructure, or in finance reporting.A small team using one API provider may be able to rely on native billing dashboards for a while. Dedicated AI cost visibility tools become more useful when spend crosses providers, teams, shared infrastructure, SaaS AI tools, or agent workflows.
These categories can also work together. An engineering team may use Langfuse to diagnose LLM behavior, while finance uses Mavvrik to connect those cost signals to allocation, budgets, chargeback, and cost-to-serve.
Building an internal AI cost tracking layer can still make sense when the use case is narrow and stable. It becomes harder when AI spend crosses clouds, direct model APIs, shared GPUs, SaaS tools, agent workflows, and chargeback. Mavvrik’s guide to FinOps for AI build vs. buy explains how to evaluate that tradeoff.
What are the next steps?
Three ways to continue from here:
1. Quantify your AI cost exposure
Use the Mavvrik AI ROI Calculator to estimate how unmanaged AI spend may be affecting your budget, margins, and forecasting.
2. See full-stack AI cost visibility in action
Explore how Mavvrik AI Cost Management connects spend across GPUs, LLMs, tokens, agents, cloud, on-prem, SaaS, and shared infrastructure.
Explore AI Cost Management →
3. Build your AI cost tracking foundation
Read the practical guide on how to track AI costs across requests, workflows, features, customers, and cost-to-serve.
FAQs
What is the difference between AI cost visibility and cloud cost management?
Cloud cost management focuses on infrastructure spend. AI cost visibility extends that view to model APIs, tokens, prompts, inference, GPUs, SaaS AI tools, data platforms, and agent workflows, then connects that spend to teams, products, customers, or agents.
Do I need a separate tool for LLM observability?
You may need one if engineering teams need to debug prompts, latency, retrieval steps, model quality, and session-level cost. For finance-ready allocation, chargeback, forecasting, and cost-to-serve, those signals need to connect back to billing data and ownership.
How do AI cost visibility tools handle AWS, Azure, and Google Cloud?
It depends on the tool. Billing-based platforms ingest cloud cost data, gateway tools connect to provider-hosted models, and observability tools trace application behavior. For hybrid environments, normalized cost data matters because each provider structures billing differently.
Should we build our own AI cost tracking layer?
Building can work for one provider, one product, or one team. It becomes harder when AI spend crosses clouds, direct model APIs, shared GPUs, SaaS tools, agent workflows, and chargeback requirements.
The pace of change matters too. New models, pricing structures, telemetry fields, SDKs, and provider billing formats keep emerging. An internal system has to keep up with all of that while still producing cost data finance and engineering can trust.
What triggers companies to look for AI cost visibility tools?
Common triggers include forecast misses, rising model bills, unexplained GPU spend, shared infrastructure with no clear owner, margin pressure, and the need to calculate cost-to-serve by product or customer.
What does it cost to run an AI agent, and how do I track it?
Agent cost is the total cost of the full workflow, including model calls, tokens, retrieval, tools, APIs, retries, compute, GPUs, storage, and orchestration. Track it at the session or workflow level so cost can be tied back to the agent, team, customer, feature, and outcome.
Alexa Abbruscato
Certified FinOps Practitioner & Content Strategist
Alexa is a certified FinOps Practitioner and FOCUS Analyst who translates complex concepts into language that resonates across engineering, finance, and procurement.