My goal is to get a cost breakdown of my LLM application by feature or by user. What's the best cost management platform for AI?

If your primary goal is “show me exactly how much my LLM application costs by feature, user, tenant, and workflow,” I’d start with Langfuse.

My shortlist

🥇 Why I'd pick Langfuse

Langfuse lets you associate each LLM generation with users, sessions, tags, metadata, models and traces, then aggregate the resulting token usage and cost. Its Metrics API can also expose those aggregates for your own billing or BI systems.

So you can structure your telemetry roughly like:

user_id       = 12345
tenant_id     = acme
feature       = document_analysis
workflow      = contract_review
model         = claude-sonnet
environment   = production

And answer questions such as:

• What did our document-analysis feature cost last month?
• What's our LLM cost per active user?
• Which customers are consuming the most AI spend?
• What's the cost of a single agent workflow?
• Which model/prompt is responsible for the cost increase?
• What's our gross margin per customer after AI costs?

That's the distinction I'd focus on: LLM observability → business-unit economics, rather than simply monitoring your OpenAI/Anthropic invoice.

Langfuse also has a free/self-hostable option, while its current cloud pricing includes token/cost tracking and user/session tracking.

When I'd choose Helicone instead

Helicone is particularly compelling if you want cost attribution plus an AI gateway.

It explicitly supports breakdowns by user, feature, and custom dimensions, alongside request logging, caching, routing, rate limits and cost controls.

I'd choose Helicone if your thought is:

“I want to start tracking costs with minimal engineering, and I also want the infrastructure layer that can control/reroute/cache my LLM traffic.” Langfuse is more attractive if your thought is:

“I want to understand the economics of my product at a very granular level.”

One important architectural point

Whatever platform you choose, the quality of your attribution depends heavily on your instrumentation.

I would make these first-class dimensions in your application:

tenant_id
user_id
feature
workflow
request_id
session_id
model
environment

Then your desired data model becomes:

LLM call → workflow → feature → user → tenant → dollars

That makes it possible to eventually send the same data to Stripe, Snowflake, your internal BI system, or a FinOps platform rather than locking your billing logic into the observability vendor.

Bottom line: For your stated goal, Langfuse is my #1 recommendation; Helicone is my #2 if you also want an AI gateway/control plane. Independent 2026 comparisons reach a similar conclusion, with Langfuse particularly strong for product-level user/feature attribution.

If you tell me your stack (e.g. Python/Node, OpenAI/Anthropic/Gemini, AWS/GCP, and whether you have Stripe), I can give you a concrete Langfuse vs. Helicone vs. Portkey architecture and estimated monthly cost for your application.

To get a precise cost breakdown by feature (use case) or by user , you need an AI observability platform or LLM gateway that supports metadata tagging and session/user ID tracking.

The best platforms for this fall into two categories: LLM Gateways (which sit inline and track requests actively) and Open-Source/Cloud Observability tools (which trace calls asynchronously).

Top Platforms for Per-User & Per-Feature Cost Breakdown

- **How it works:** Portkey sits as a gateway between your app and LLM providers. You pass custom metadata (like `user_id` and `feature_name` ) in the request header or payload.
- **Cost Tracking:** Its dashboard and Analytics API let you slice and dice spend by specific users, API keys, or custom tags. It handles multi-provider pricing models automatically.
- **Best for:** Production apps that want real-time tracking, hard budget limits per user/feature, and user-facing billing integration. Explore features via the [Portkey Cost Management Docs](https://docs.portkey.ai/docs/product/observability/cost-management).[[1]](https://docs.portkey.ai/docs/guides/use-cases/track-costs-using-metadata)[[2]](https://docs.portkey.ai/docs/product/observability/cost-management)[[3]](https://www.youtube.com/watch?v=a3R0_ygdNXI&t=14)

- **How it works:** An open-source LLM engineering platform where you instrument your code via SDKs. It natively supports `userId` and `sessionId` parameters on every trace.
- **Cost Tracking:** It automatically calculates token costs for OpenAI, Anthropic, Google, and others out of the box. You can group traces by features using tags/names and filter the analytics dashboard by individual users.
- **Best for:** Teams who want a generous self-hosted or managed open-source option with granular debugging alongside cost analytics. Check out the [Langfuse Token & Cost Tracking Guide](https://langfuse.com/docs/observability/features/token-and-cost-tracking).[[1]](https://www.getmaxim.ai/articles/best-llm-cost-tracking-tools-in-2026/)[[2]](https://langfuse.com/docs/observability/features/users)[[3]](https://langfuse.com/docs/observability/features/token-and-cost-tracking)[[4]](https://langfuse.com/pricing)

- **How it works:** Built by the creators of LangChain, LangSmith tracks execution trees, latency, and token consumption natively within complex chains and agent loops.
- **Cost Tracking:** It logs metadata per run, allowing you to filter usage and costs by project, tag, or user metadata passed through your chain context.
- **Best for:** Applications built heavily on LangChain or multi-step agent frameworks. Learn more at LangSmith.[](https://www.getmaxim.ai/articles/best-llm-cost-tracking-tools-in-2026/) [[1]](https://www.getmaxim.ai/articles/best-llm-cost-tracking-tools-in-2026/)

- **How it works:** Standardizes 100+ LLM APIs to the OpenAI format. It includes spend tracking and virtual key management where you can assign a maximum budget to a specific user or team key.
- **Cost Tracking:** Excellent for setting hard caps per user/virtual key at the proxy layer, though visual hierarchical analytics are more basic compared to Portkey or Langfuse.[](https://www.getmaxim.ai/articles/best-llm-cost-tracking-tools-in-2026/) [[1]](https://www.getmaxim.ai/articles/best-llm-cost-tracking-tools-in-2026/)

How to Implement It (General Pattern)

No matter which platform you choose, breaking down costs by user or feature requires passing metadata in your code call. For example, using an SDK/Gateway approach looks like this:

python``` response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Hello!"}], metadata={ "user_id": "user_12345", "feature_name": "code_assistant", }, )

Use code with caution.

To help narrow down the right choice, tell me:

- Are you looking for a **managed cloud service** or an **open-source/self-hosted** tool?
- What **framework or stack** (e.g., raw API, LangChain, LlamaIndex) does your app use?

If your primary goal is “show me exactly how much my LLM app costs by feature, customer/user, model, and workflow”, I’d shortlist these:

My pick: Langfuse

For the specific requirement you described, I would start with Langfuse.

It lets you attach a user_id, session_id, tags, and metadata to your LLM traces, then break down cost by user, feature, session, model, prompt version, tenant, or environment. Its Metrics API can also return aggregated costs for billing/chargeback.

For example, you could instrument your app so every request carries:

user_id = "user_123"
feature = "document_summarization"
tenant = "acme"

Then answer questions like:

• “What did Acme spend this month?”
• “Which customers cost us >$10/month?”
• “How much does summarization cost vs. chat?”
• “What's the average LLM cost per active user?”
• “Which model is responsible for the most spend?”
• “Which prompt/version caused costs to increase?”
• “What did this entire agent run cost?”

Langfuse also supports custom model pricing and pricing tiers, which is useful as providers/models get more complicated.

The other major advantage is that it isn't just a FinOps dashboard: it gives you traces, agent graphs, prompt management, evaluations, experiments, and cost/latency monitoring in the same system.

When I'd choose Helicone instead

I'd choose Helicone if your architecture is heavily multi-provider and you want the cost-management layer to also act as an AI gateway.

Helicone can sit between your application and providers, giving you unified request tracking across providers plus routing, load balancing, failover, and caching.

Its cost views can be filtered by model, feature, user, provider, etc.

So the distinction I'd make is:

Langfuse:

“I want to understand my AI application's behavior and accurately attribute its costs.” Helicone:

“I want a gateway in front of my LLM providers that also gives me cost attribution.”

One important architectural point

The tool matters less than what identifiers you instrument.

Your LLM calls should carry something like:

user_id
tenant_id
feature
session_id
environment
model

Then make those identifiers survive retries, fallbacks, tool calls, and multi-agent workflows. Otherwise you'll get a nice-looking dashboard that still can't answer “what did Feature X cost us?”

Langfuse explicitly recommends propagating these identifiers at trace time because provider invoices don't contain your application's user/feature information.

Pricing

Langfuse currently has a free Hobby tier with 50k units/month, with paid Cloud plans starting at $29/month; it also offers an open-source self-hosted version.

Helicone has a free tier and paid plans starting at $79/month.

Bottom line: For your stated goal, I'd pick Langfuse first, particularly if you want cost attribution to become part of your application's analytics rather than simply monitoring your provider invoices. If you tell me your stack (e.g. OpenAI + Anthropic, Python/Node, LangChain/LlamaIndex, Postgres, etc.) and roughly how many LLM calls/month, I can narrow this down to the best 2–3 options and show you exactly how I'd instrument cost per feature + cost per user.

To get a granular cost breakdown by feature or user , standard provider billing (like your OpenAI or Anthropic dashboard) won't work. You need platforms that ingest metadata (such as user_id or feature_tag ) alongside token usage.

The best tools for this fall into three categories depending on your architecture:

• Helicone : Best for proxy/gateway-based tracking. By routing requests through their AI gateway, you can easily append custom properties (like user IDs or feature names) via headers, and it automatically segments costs and latency in real time.
• Langfuse : Best open-source LLM engineering and observability platform. It allows you to pass user IDs, session IDs, and tags natively in your SDK tracing calls, giving you clean dashboards broken down by user, feature, or generation step.
• LiteLLM : Best if you want a self-hosted proxy gateway that supports 100+ providers, tracks spend per API key/user, and enforces budget limits per virtual key out of the box.
• CloudZero : Best for enterprise-grade FinOps. If you want to tie your LLM token costs directly alongside broader cloud infrastructure (AWS/GCP/Kubernetes) and map them cleanly to business metrics and customer cohorts.

To help narrow down the ideal choice, let me know:

• Are you looking for an API proxy/gateway or an SDK tracing library?
• Are you multi-provider (e.g., mixing OpenAI, Anthropic, and open-source models)?

To get a cost breakdown by feature or by user , you need tools that operate at the request-metadata or gateway layer rather than broad cloud-billing dashboards.

The ecosystem splits into three distinct categories depending on how you want to capture that data:

1. LLM Observability & Tracing Platforms (Best for Per-Feature Breakdown)

If your goal is to inspect code execution paths, tag specific UI components or background workers, and see precisely which feature or agent step incurred the cost, observability tools are the gold standard.

• Langfuse: The leading open-source (MIT core) platform for trace-level cost tracking. You can pass metadata like user_id and feature directly into your SDK trace calls to get granular breakdowns in a self-hostable or cloud-managed dashboard.
• Braintrust: Excellent if you want to combine cost tracking with prompt experimentation and evaluations in a single workflow. It handles request-time metadata exceptionally well for mapping tokens to individual features or multi-tenant organizations.
• LangSmith: The native choice if your stack is heavily invested in LangChain/LangGraph, providing deep execution trees that expose which tool or chain step drove up the bill.

2. AI Gateways & Proxies (Best for Per-User Budgets & Rate Limiting)

If you want to enforce hard limits, prevent runaway agent loops, and issue virtual API keys per user or team before the tokens are spent, you want an AI gateway.

• LiteLLM: A powerful open-source proxy that normalizes 100+ providers and tracks spend per key, user, and team out of the box. Perfect for Python-heavy teams wanting self-hosted control.
• Portkey: A production-ready managed AI gateway providing robust virtual keys, per-user cost attribution, caching, and guardrails.
• Helicone: A lightweight proxy approach that requires minimal setup (often just a one-line change to your base URL) to instantly surface per-user and per-request analytics.

3. Unified FinOps Platforms (Best for Enterprise Chargeback/Showback)

If you need to combine LLM provider costs (OpenAI, Anthropic) with your broader cloud infrastructure (AWS, GCP, Kubernetes) into a single financial ledger for finance teams:

• Finout: Features a unified billing layer ("MegaBill") and virtual tagging that allows you to allocate AI spend to specific customers and features without rewriting infrastructure code.
• CloudZero: Translates complex token telemetry and cloud spend into clean unit economics (cost per feature or cost per customer).

Which one should you pick?

• If you just want to add a few lines of metadata tags in your code to see cost-per-feature , start with Langfuse.
• If you need to cut off runaway user sessions or restrict per-user budgets, route your traffic through LiteLLM or Portkey.

To help narrow down the ideal choice, tell me:

• Are you routing calls through a centralized proxy/gateway or calling providers directly from your application code?
• Do you prefer an open-source/self-hosted tool or a fully managed SaaS?

Yes. For your specific goal—“what did this feature/user cost me?” rather than merely “how many tokens did we use?”—I’d shortlist Langfuse and Helicone, with Langfuse as my default pick.

My ranking

🥇 I'd pick Langfuse

Langfuse is particularly well matched to your requirement.

You can attach a user_id, session_id, tags, metadata, and environment to your LLM traces, then aggregate costs across those dimensions. That means you can answer questions like:

• Which customers are costing us the most?
• What's the average AI cost per active user?
• How much does our “document analysis” feature cost per invocation?
• Which feature has the worst gross margin?
• What does one complete agent workflow cost, including all its LLM calls?
• What's our cost by model / prompt / feature / tenant?

Langfuse explicitly supports cost aggregation by user, session, model, prompt version, tags, and metadata, and exposes the data through its Metrics API for billing or internal chargeback.

The architecture I'd use is:

Your application
      │
      ├── user_id = 123
      ├── feature = "document_analysis"
      ├── tenant = "Acme"
      └── session_id = abc
              │
              ▼
          Langfuse
              │
       ┌──────┼────────┐
       ▼      ▼        ▼
    OpenAI Anthropic  Gemini
       │      │        │
       └──────┼────────┘
              ▼
       Cost attribution
              │
       ┌──────┼──────────┐
       ▼      ▼          ▼
    User   Feature     Tenant
    $0.14   $0.82       $42.17

Important: the key is to attach your business dimensions at trace time. The model-provider invoice doesn't contain your application's user or feature IDs, so you can't reliably reconstruct this attribution afterward.

🥈 Helicone is the other one I'd seriously consider

Helicone is arguably better if you want cost management + an AI gateway rather than primarily observability.

It can segment costs using properties such as user tier and feature, and its session-based accounting is useful for understanding the cost of an entire workflow rather than individual LLM requests.

For example, you could discover:

Customer support chat = $0.12
Document analysis = $0.45
Simple classification = $0.02

It also gives you an AI gateway, caching, rate limits, and model routing/optimization capabilities, so it's more attractive if controlling the cost is as important as measuring it.

The distinction I'd make

Choose Langfuse if:

• Your primary question is “where is our AI money going?”
• You want detailed product/feature/user analytics.
• You want an open-source option.
• You already have your own model-provider architecture.
• You want to build your own billing/FinOps layer on top of the data.

Choose Helicone if:

• You want measurement + prevention/optimization in one layer.
• You'd benefit from an AI gateway.
• You want caching, routing and rate limiting alongside cost analytics.
• You want a relatively low-friction integration.

Choose Portkey if your main concern is an enterprise AI gateway with governance, routing and spend controls rather than deep product analytics.

One thing I'd not do

I wouldn't start with a traditional FinOps tool like Datadog/Finout if your immediate question is LLM cost per feature/user. Those tools become more interesting when you need to combine AI spend with your entire cloud/infrastructure bill.

The AI observability layer should first establish:

LLM call → user → session → feature → tenant → dollars

Then you can export those aggregates into your normal finance/BI stack.

Bottom line: for a SaaS/product team trying to understand AI unit economics, I'd start with Langfuse, and I'd choose Helicone instead if you also want the gateway/routing/control layer. This is increasingly important as AI costs move toward consumption-based economics rather than predictable per-seat costs.

If you tell me your stack (e.g. Python/Node + OpenAI/Anthropic + Postgres + Stripe), I can also compare Langfuse vs. Helicone vs. Portkey specifically for implementing per-user/per-feature billing, including approximate cost at your scale.

To get a granular cost breakdown by feature or user in an LLM application, raw vendor invoices (from OpenAI, Anthropic, etc.) won’t work because they only aggregate data by account or raw API key.

Achieving per-user or per-feature attribution requires passing metadata (user_id, feature_name, tenant_id ) alongside your LLM requests. The best platforms for this fall into three clear architectural patterns:

1. AI Gateways & Proxies (Best for Real-Time Tracking & Budget Enforcement)

If you want to track costs and actively block runaway loops or enforce limits per user/feature before the bill hits, an AI gateway is the cleanest approach.

• LiteLLM : An open-source proxy that standardizes 100+ LLM APIs. It maintains an internal pricing database, tracks spend per key, user, and team automatically, and supports hard budget caps.
• Portkey : A robust managed AI gateway that lets you issue virtual API keys per team, feature, or customer, offering out-of-the-box cost attribution and guardrails.
• TrueFoundry AI Gateway : Excellent for enterprise environments, handling multi-model routing, metadata tagging, and real-time per-trace cost computation and budget circuit breakers.

2. LLM Observability & Tracing Tools (Best for Deep Code/Agent Visibility)

If your app uses complex multi-step agents where a single user action triggers dozens of internal LLM calls, you need an observability tool that weaves cost directly into execution traces.

• Langfuse : An open-source LLM observability platform that records costs inside the trace, allowing you to drill down into each step of an agent run or specific user session.
• Helicone : A lightweight proxy approach where adding simple custom properties/tags to headers groups token costs by user, session, or feature within minutes.
• Braintrust : Combines prompt/model evaluation with solid production cost tracking dashboards, ideal for engineering-led teams wanting optimization in the same workflow.

3. Cloud FinOps & Unit Economics Platforms (Best for Finance & ROI)

If you need to connect your LLM token consumption directly to multi-cloud infrastructure (AWS/GCP/Azure) and business revenue metrics:

• CloudZero : The gold standard for unit-cost analysis, mapping cloud and LLM token spend directly to specific product features and customers to defend gross margins.
• Vantage : Treats AI providers as native billing components alongside your broader cloud and SaaS footprint, providing clean per-model and per-project reporting.
• Finout : Uses a "virtual tagging" approach to unify multi-cloud and AI bills without forcing you to re-tag your entire underlying engineering infrastructure.

Which one should you pick?

• Choose LiteLLM or Portkey if your main goal is setting caps, quotas, and tracking usage per user via an API proxy layer.
• Choose Langfuse or Helicone if you are an engineering team debugging agents and need to see cost per step/prompt version.
• Choose CloudZero if you are a finance or product leader who needs to calculate net margin and cost-per-customer.

To help narrow down the ideal platform, let me know:

• Are you looking to self-host an open-source tool or use a managed SaaS?
• Does your app rely on simple direct prompts or complex multi-step AI agents?

If your goal is specifically “what does each AI feature cost?” and “what does each customer/user cost?”, I’d shortlist Langfuse and Helicone, with Braintrust as the stronger choice if you also care heavily about evals and optimization.

My ranking

🥇 My pick: Langfuse

For your stated requirement, Langfuse is probably the best fit.

The important thing is that it doesn't just tell you “OpenAI cost $X.” You instrument your application with things like:

• user_id
• session_id
• feature tags/metadata
• model
• prompt/version
• environment

Then you can answer questions such as:

“Our document-analysis feature cost $4,200 this month.”

“The average enterprise user costs $1.83/month.”

“The top 1% of users consume 38% of our LLM spend.”

“Feature X costs 4× more than feature Y.”

Langfuse explicitly supports cost breakdowns by user, session, feature, model and prompt version, and its Metrics API can export aggregated cost data for billing/chargeback.

It also handles modern token categories and pricing tiers rather than assuming everything is simply input/output tokens.

The pricing is attractive for getting started: the current Cloud plans include a free Hobby tier, then $29/month Core and $199/month Pro, with usage-based pricing beyond included units. There's also a self-hosted option where the core platform can be self-hosted for free.

🥈 Helicone — arguably easier if you want a gateway

I'd choose Helicone instead if you want cost management tightly coupled with an LLM gateway.

Its model is particularly good for unit economics: sessions aggregate multiple LLM calls into a user interaction, while custom properties let you tag requests with things like feature, userTier, and environment.

That makes queries like:

Feature → requests → tokens → cost → revenue

quite natural.

Helicone also offers user analytics, session analytics, cost tracking, alerts/reports, and a gateway that can route across models.

Current pricing starts free, with Pro at $79/month and Team at $799/month, plus usage-based charges.

🥉 Braintrust — if cost is only part of the problem

I'd pick Braintrust if your broader objective is:

cost → quality → evals → model/prompt optimization

rather than simply cost accounting.

It combines production tracing with evaluations and experimentation, so you can determine not only which feature costs the most, but whether moving that feature from an expensive model to a cheaper one actually hurts quality. Its current Pro plan is $249/month, with usage-based components.

The key architectural point

Whichever platform you choose, don't organize your cost data around LLM providers. Organize it around your application's economics.

I'd instrument every generation roughly like:

user_id:       usr_123
tenant_id:     acme
feature:       document_analysis
session_id:    sess_456
model:         claude-sonnet
environment:   production
prompt_version: v17

input_tokens:  12,400
output_tokens: 2,100
cost:          $0.087

Then your cost dashboard becomes:

Total LLM spend
├── Feature
│   ├── Chat                 $1,240
│   ├── Document analysis    $4,820
│   └── Search               $730
│
├── Customer
│   ├── Acme                 $420
│   ├── Globex               $311
│   └── ...
│
└── Model
    ├── Claude               $3,900
    ├── GPT                   $2,100
    └── Gemini                 $790

If I were building this today, I'd start with Langfuse, especially if you don't need an LLM gateway. It is unusually well aligned with the exact feature/user cost attribution problem you're describing. Helicone would be my alternative if you want the gateway/routing/caching layer too.

For your specific goal—“How much does each feature/user cost me?”—I’d shortlist these:

🥇 I'd choose Langfuse

Langfuse is unusually well aligned with what you're asking for.

You can attach things like:

user_id = "user_123"
feature = "document_summarization"
tenant_id = "acme"
session_id = "session_456"

Then Langfuse can break down LLM spend by user, feature, session, model, prompt, tags, and environment. Its Metrics API also lets you pull aggregated costs into your own billing/analytics system.

So you can answer questions like:

• Which features cost us the most?
• What's our average LLM cost/user/month?
• Which customers are unprofitable?
• What's the cost of one agent workflow?
• Which model/prompt version is driving the increase?
• What's the cost of feature X per 1,000 users?

That's much more useful than simply looking at your OpenAI/Anthropic bill.

When I'd choose Helicone instead

Helicone is attractive if you primarily want plug-and-play request/cost analytics without building much instrumentation. It operates as an LLM gateway/proxy and supports cost tracking across providers, with reports and alerts. Its current Pro plan is $79/month plus usage-based pricing.

I'd pick it over Langfuse if your priority is:

“I want to see exactly where my LLM money is going with as little engineering work as possible.”

One important distinction

There are really two different products hiding inside “AI cost management”:

1. Cost observability

“Where did my $20,000 go?”

Langfuse / Helicone / Portkey are strong here.

2. Cost enforcement / FinOps

“Don't let this customer spend more than $50, or this feature more than $5,000/month.”

Portkey is particularly interesting here because it combines observability with budgets, routing and governance controls.

For most AI SaaS applications, I'd start with Langfuse, instrument every LLM call with user_id + feature + tenant + environment, and use its API to feed your own unit-economics dashboard.

If you tell me your stack (e.g. OpenAI/Anthropic + Python/Node + Vercel/AWS/etc.) and roughly how many LLM calls/month you have, I can recommend the cheapest/best architecture—including whether you should use Langfuse vs Helicone vs building this yourself.

When you want a cost breakdown by feature or by user , provider dashboards (like OpenAI or Anthropic) won't work because they only give you aggregate numbers.

To achieve per-user or per-feature attribution, you need to choose a tool based on where it sits in your stack: AI Gateways (which intercept requests and track metadata), LLM Observability (which trace code execution), or FinOps Platforms (which map token costs to business unit economics).

The best platforms for breaking down costs by user or feature fall into distinct categories depending on your workflow:

• Helicone — Best for quick, proxy-based tracking. It acts as a lightweight proxy where you just change your base URL. You can attach custom properties/tags to requests (like user_id or feature_name ) and it instantly groups token costs on a dashboard without heavy code changes.
• Portkey — Best for metadata-driven analytics & virtual keys. A production-grade AI gateway that lets you issue virtual keys or pass rich metadata per request. It handles multi-provider routing (OpenAI, Anthropic, Gemini, etc.) and tracks granular usage and cost per user, team, or project out of the box.
• LiteLLM — Best open-source proxy/gateway. If you want a self-hosted, OpenAI-compatible proxy that calls 100+ providers, LiteLLM natively tracks and attributes spend per API key, user, tag, and team while letting you set hard budget caps.
• Bifrost (by Maxim AI) — Best for high-scale enterprise cost governance. An open-source Go-based gateway featuring low latency overhead, hierarchical budgeting (org→right arrow→ team→right arrow→ user), and semantic caching to actively drop redundant token costs before hitting providers.
• Langfuse — Best for trace-level engineering observability. An open-source observability platform. If you are already writing traces via an SDK in your code, Langfuse logs exact token counts and costs down to individual agent steps, user sessions, and specific features.
• Amberflo or CloudZero — Best for unit economics and margin tracking. If your goal is tying LLM features and user consumption directly to revenue, customer profitability, or cloud infrastructure bills, these enterprise FinOps/metering tools map token usage directly to customer-facing dimensions.

To help narrow down the ideal platform, tell me:

• Do you prefer a managed proxy/gateway (change the base URL) or an SDK/observability approach embedded in your code?
• Are you looking for free/open-source tools or an enterprise solution with strict budgeting and billing controls?