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?

To get a granular cost breakdown by user or by feature , you need an AI observability platform or gateway that supports metadata tagging and custom properties at request time. Because standard LLM provider invoices (like OpenAI or Anthropic) only show aggregate token spend, your application must pass identifiers (user_id, feature_name, session_id ) alongside the prompt.docs.helicone.ai+2

The top platforms for solving this specific problem fall into a few clear categories depending on your architecture:

1. Best for Open-Source & Deep Tracing: Langfuse

Langfuse has emerged as an industry favorite for tracking costs as a first-class signal alongside debugging and evaluations.opslyft.com

• How it handles your goal: By passing user_id and tags/metadata (for features) into your trace calls, Langfuse Documentation on Token & Cost Tracking automatically aggregates costs per user, session, and distinct feature.langfuse.com
• Pros: Excellent open-source (MIT licensed) flexibility, self-hosting options, clean UI for slicing data by tags, and robust SDK support.getmaxim.ai
• Cons: Requires adding their SDK and propagating attribution keys directly in your application code.langfuse.com

2. Best for Gateway & Proxy Routing: Portkey

If you want to manage your LLM traffic through an AI gateway (handling fallbacks, caching, and routing), Portkey is a strong contender.opslyft.com

• How it handles your goal: It uses simple header-based metadata or request body parameters. You attach user and feature metadata to requests, and Portkey's Analytics API & Dashboard lets you filter and query costs per user or build user-facing billing dashboards.docs.portkey.ai
• Pros: Acts as an active proxy so you don't have to tightly couple tracking logic deep in your code; great for active governance and rate-limiting.opslyft.com
• Cons: Passing metadata relies on adhering strictly to gateway headers or client initialization parameters.

3. Best for Zero-Config Proxy Tracking: Helicone

Helicone sits as an open-source proxy layer that is exceptionally fast to set up if you proxy your LLM calls.opslyft.com

• How it handles your goal: It features a dedicated Helicone-User-Id header and custom property headers (Helicone-Property-[Name] ) that instantly break down analytics by user ID, customer tiers, or specific app features inside their dashboard.
• Pros: Incredibly fast to implement (often just changing your base API URL and adding a couple of headers).
• Cons: Less suited for complex, multi-step agent tracing compared to dedicated APM tools like Langfuse.docs.helicone.ai+1

4. Best for Enterprise FinOps & Datadog Shops: CloudZero or Datadog LLM Observability

• Datadog LLM Observability: Ideal if your engineering organization is already standardized on Datadog. It correlates token costs right alongside your standard infrastructure metrics, though deep user/feature slicing requires custom tag mapping.
• CloudZero: Excellent if your goal is pure FinOps —mapping cloud, GPU, and LLM spend into unit metrics like cost per customer tenant or feature tier for finance leadership.opslyft.com+1

Recommendation on How to Choose

• Choose Langfuse if you want granular, developer-first open-source tracing where you can explicitly code user_id and feature tags into complex chains.
• Choose Helicone or Portkey if you prefer a proxy/gateway approach where adding custom request headers instantly populates your user-cost analytics dashboard.docs.helicone.ai+4

To help narrow down which platform fits best, tell me:

• Are you routing calls through an AI Gateway / Proxy , or do you prefer a native SDK / code-instrumentation approach?
• Are you looking to self-host the analytics platform or use a managed cloud service?

To get a granular cost breakdown by user or by feature (often referred to as cost attribution or chargeback/showback ), you need a platform that accepts custom metadata or tags (like user_id or feature_name ) alongside your LLM API calls and maps them against real-time token pricing.docs.portkey.ai+1

The top platforms for this specific use case fall into a few clear categories depending on your architecture:

1. Portkey (Best AI Gateway + Cost Management)

Portkey functions as an AI gateway, making it exceptionally easy to track costs per user or feature because cost attribution is baked directly into the proxy layer.portkey.ai+2

• How it works for users/features: You pass custom metadata (e.g., user_id, feature: "summarizer" ) in the header or body of your API request. Portkey automatically indexes this.docs.portkey.ai+1
• Key Features:
  • Real-time tracking and visualization on their dashboard via metadata filters.
  • Analytics API allows you to pull these granular metrics directly into your own internal billing or admin dashboards.
  • Massive, actively maintained pricing database covering thousands of models across dozens of providers.docs.portkey.ai+2
• Best for: Production applications wanting a robust gateway that handles routing, fallbacks, and fine-grained cost allocation out-of-the-box.portkey.ai+1

2. LiteLLM (Best Open-Source Proxy & Python Toolkit)

If you want to host your own proxy or manage calls via a lightweight Python package, LiteLLM is a dominant choice.getmaxim.ai+1

• How it works for users/features: LiteLLM Proxy allows you to generate virtual API keys tied to specific budgets, teams, or user IDs. It tracks spend per key, per user, and per team automatically.getmaxim.ai+1
• Key Features:
  • Set hard budget limits/rate limits per user or virtual key.
  • Unified proxy endpoint that translates OpenAI format to Anthropic, Bedrock, Vertex, etc., with built-in spend tracking.getmaxim.ai+1
• Best for: Engineering teams who want programmatic control, budget enforcement (auto-blocking users who exceed limits), and an open-core/self-hostable setup.getmaxim.ai+1

3. Langfuse (Best for Open-Source Observability & Analytics)

Langfuse is an open-source LLM engineering and observability platform that handles token and cost tracking seamlessly.langfuse.com+2

• How it works for users/features: When logging traces/generation calls via the Langfuse SDK, you can attach a userId and custom tags/metadata to the trace. The dashboard aggregates costs by user, session, or generation type over time.langfuse.com+1
• Key Features:
  • Automatic cost calculation based on standard model pricing (OpenAI, Anthropic, Google).
  • Deep traceability into which prompt or chain within a feature is driving up token counts.
  • Generous cloud tier and easily self-hostable if you have data-privacy constraints.langfuse.com+3
• Best for: Developers who want deep debugging/observability combined with clean cost allocation dashboards.langfuse.com+2

4. Braintrust or Galileo (Best for Enterprise Evaluation & Observability)

Platforms like Braintrust and Galileo track cost natively as part of a broader suite focused on quality, evaluations, and production logging.braintrust.dev+1

• How it works for users/features: They log metadata cleanly on every span/trace, allowing you to slice expenditure by logging tags.
• Best for: Enterprise teams prioritizing overall LLM quality assurance, safety, and evaluation alongside financial tracking.braintrust.dev+1

Summary Recommendation

• Choose Portkey if you want a managed API Gateway approach that lets you pipe cost metrics straight into a user-facing or internal analytics dashboard via an API.
• Choose LiteLLM if you need to enforce strict budgets/caps per user or virtual key and prefer a proxy architecture.
• Choose Langfuse if you want top-tier open-source observability where cost breakdown is tied directly to detailed execution traces.langfuse.com+4

To help narrow down the ideal platform, tell me:

• Are you currently routing LLM calls directly via provider SDKs (OpenAI/Anthropic) or through an API gateway/proxy?
• Do you prefer a managed SaaS tool or an open-source/self-hosted solution?

If your main goal is “How much does my LLM application cost by feature, user, customer, or workflow?”, I’d pick Langfuse.

My shortlist

Platform	Best for	Cost by user	Cost by feature	Gateway / controls
Langfuse	Product-level cost attribution	✅ Excellent	✅ Excellent	◐
Helicone	Easiest cost tracking + gateway	✅	✅	✅
LiteLLM	Budgets, routing & enforcement	✅	✅ via tags	✅ Excellent
Portkey	Enterprise gateway/governance	✅	✅	✅
Braintrust	Cost + evaluations	✅	✅	◐

🥇 Langfuse — my recommendation

Langfuse tracks the cost of individual LLM calls and lets you analyze it across models, users, tags, sessions and application use cases. Its Metrics API can also return aggregated costs filtered by user or tags, which is useful if you eventually want to feed the numbers into your own billing or BI system.Langfuse+1

I'd instrument each request with something like:

user_id       = usr_123
tenant_id     = acme
feature       = document_summarization
workflow      = contract_review
session_id    = sess_456
environment   = production
model         = claude/...

Then your cost hierarchy becomes:

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

That lets you answer questions such as:

• “What did customer Acme cost us this month?”
• “How much does document summarization cost per user?”
• “Which feature consumes 50% of our AI budget?”
• “What's our average AI cost per paying customer?”
• “Which model/prompt is driving the cost increase?”
• “What's the cost of an entire agent workflow?”

Langfuse currently has a free Hobby tier, with Core at $29/month and Pro at $199/month according to its pricing page.Langfuse

langfuse.com

🥈 Helicone — if you want the least engineering

Helicone would be my second choice.

Its big advantage is that it can sit in front of your LLM providers as a proxy/gateway, so you can get cost/request analytics with relatively little application instrumentation. It's particularly attractive if you want cost tracking + caching + routing/control rather than just observability.

I'd choose Helicone if your reaction is:

“I want to start seeing exactly where my LLM money goes without instrumenting my entire application.”

🥉 LiteLLM — if you want to control spending

LiteLLM is slightly different. It's less of a product analytics platform and more of a gateway/control plane.

It provides virtual keys, users/teams, spend tracking, budgets, rate limits, routing and fallbacks. Its open-source gateway is currently free to self-host.LiteLLM

So:

Langfuse: “Where did my money go?” LiteLLM: “Control where the money can go.” Helicone: “Give me both, with an easy proxy-based setup.”

You can also use LiteLLM + Langfuse together if you eventually need both hard budget enforcement and deep application-level tracing.

What I'd do for your application

If you're building an AI SaaS/product, I'd start with:

Application → Langfuse instrumentation → OpenAI/Anthropic/Gemini

and make user_id, tenant_id, and feature mandatory metadata on every LLM operation.

If you later need hard limits:

Application → LiteLLM → providers ↘ Langfuse

That gives you both accurate product-level unit economics and spend enforcement.

Bottom line: start with Langfuse. It is the closest match to your specific requirement of breaking LLM costs down by feature and user, rather than merely monitoring your provider invoice.Langfuse+1

The "best" platform for breaking down your LLM costs by user or feature depends heavily on where you want the tracking to happen (at the API proxy layer vs. the application/observability layer ) and your budget.

Because tracking by user or feature requires passing metadata (like user_id or feature_name ) with your LLM calls, the top platforms approach this via AI Gateways, LLM Observability tools , or FinOps platforms.superpenguin.ai+1

Top Choices for Per-User & Per-Feature Cost Tracking

1. LiteLLM (Best Open-Source Proxy / Gateway)

If you want to self-host or control your infrastructure entirely with zero licensing fees, LiteLLM is a premier choice.kosmoy.com+1

• How it tracks by user/feature: It acts as a proxy in front of 100+ LLM providers. You can pass metadata tags (such as user IDs or project/feature tags) in your API headers or requests, and LiteLLM logs and tracks spend, latency, and tokens per virtual key, user, or team.
• Pros: Highly customizable, open-source (MIT licensed), great for budget control and setting spend limits per user/team.
• Cons: Requires you to manage the proxy infrastructure yourself.truefoundry.com+3

2. Portkey (Best Managed Gateway & Guardrails)

If you prefer a robust, production-ready managed SaaS gateway rather than self-hosting, Portkey excels at operational visibility.kosmoy.com+1

• How it tracks by user/feature: Portkey logs granular request metadata. You can segment analytics by metadata tags to see exact cost breakdowns per feature, user ID, or workflow variant.
• Pros: Excellent dashboard UI, advanced caching, fallback management, and built-in guardrails alongside deep cost attribution.
• Cons: Paid tiers scale up as your request volume grows.portkey.ai+3

3. Langfuse (Best Open-Source LLM Observability)

If you are already instrumenting your application with tracing (using LangChain, LlamaIndex, or native SDKs) and want deep insights into what your application is doing, Langfuse is a top pick.langfuse.com+1

• How it tracks by user/feature: Langfuse allows you to attach userId and arbitrary metadata tags/session IDs to traces. Its dashboard automatically aggregates token usage and translates it into hard dollar costs per user or feature.langfuse.com+2
• Pros: Generous free tier/open-source model, beautiful tracing UI, and deep native SDK integrations.langfuse.com+2
• Cons: It is an observability and debugging platform first, so it doesn't block or route traffic like a traditional gateway (though it integrates nicely with them).

4. CloudZero or Finout (Best for Enterprise FinOps & Unit Economics)

If your primary stakeholder asking for this breakdown is your Finance or FinOps team who want to view LLM spend side-by-side with your broader AWS/GCP cloud bill.finout.io+2

• How it tracks by user/feature: These platforms ingest telemetry data or logs from your AI applications and map them to business metrics (e.g., cost per active user, cost per specific feature usage).
• Pros: Connects engineering costs directly to business unit economics without needing custom internal dashboards.
• Cons: Enterprise-focused pricing; overkill if you just need simple developer-level token tracking.finout.io+2

Which one should you pick?

• Go with LiteLLM if you want a free, self-hosted proxy to control and tag traffic before it hits OpenAI/Anthropic.
• Go with Langfuse if you want to debug your LLM app and trace costs down to individual user sessions and prompts.
• Go with Portkey if you want a fully managed, plug-and-play gateway with immediate analytics dashboards.langfuse.com+2

To help narrow this down, could you tell me:

• Are you looking to self-host the tool or use a managed cloud service?
• Are your LLM calls already wrapped in an observability framework (like LangChain) or a proxy?

If your main goal is “How much does each AI feature/user cost me?”, rather than just monitoring API spend, I’d shortlist Helicone, Langfuse, and Portkey.

My pick: Helicone

Helicone is probably the best fit for your specific use case.

It lets you attach metadata such as:

• user_id
• feature
• plan
• organization
• environment
• workflow

and then analyze costs along those dimensions. Helicone explicitly supports unit economics such as cost per user, conversation, and feature, including grouping multi-call workflows into sessions.Helicone OSS LLM Observability+1

For example, you could answer:

“Our 10,000 Pro users cost us $8,400/month in LLM spend, and the document-analysis feature accounts for $5,100 of that.” That's the kind of analysis I'd optimize for.

helicone.ai

How I'd compare them

Langfuse is arguably the strongest alternative. It tracks cost at the individual LLM-generation level and lets you aggregate by users, tags, use cases, etc. Its Metrics API can also give you cost data programmatically for billing or your own dashboards. Langfuse It has a particularly attractive option if you want to self-host.Langfuse

langfuse.com

Portkey is especially compelling if you also want the platform to sit between your application and model providers. Its metadata system supports user/team/workflow attribution, while its analytics dashboard provides cost, token, user, model, and metadata breakdowns.Portkey+1

portkey.ai

What I'd choose

• Primarily want cost accounting / unit economics → Helicone
• Want deep LLM observability + evaluations + cost → Langfuse
• Want an AI gateway + routing/fallbacks + cost governance → Portkey
• Already heavily invested in LangChain → LangSmith

One important architectural point: the platform is only half the solution. Whichever you choose, instrument every LLM request with business dimensions such as:

user_id
organization_id
feature
workflow
plan
environment

Then your cost data becomes genuinely useful—for example:

Total LLM cost
├── Feature: Chat              $2,140
├── Feature: Document analysis $5,820
├── Feature: Search             $930
└── Feature: Summarization      $410

Cost by customer
├── Acme Corp                   $842
├── Beta Inc                    $391
└── ...

If you tell me your stack (OpenAI/Anthropic/etc., Python/Node, LangChain/LlamaIndex/custom, and roughly how many LLM calls/month), I can recommend the best one for you and compare the actual pricing at your expected scale.

Langfuse is arguably the strongest alternative. It tracks cost at the individual LLM-generation level and lets you aggregate by users, tags, use cases, etc. Its Metrics API can also give you cost data programmatically for billing or your own dashboards. Langfuse It has a particularly attractive option if you want to self-host.Langfuse

langfuse.com

Portkey is especially compelling if you also want the platform to sit between your application and model providers. Its metadata system supports user/team/workflow attribution, while its analytics dashboard provides cost, token, user, model, and metadata breakdowns.portkey.ai+1

To get a granular cost breakdown by user or by feature , you need an LLM observability platform or AI gateway that supports custom metadata, user tagging, and trace-level attribution . Tracking aggregate API spend isn't enough; you need to pass identifiers like user_id and feature tags at runtime.langfuse.com+1

The top platforms best suited for this level of detailed attribution include:

• Langfuse (Open Source / Managed) : Widely considered a gold standard for flexible cost attribution. By passing user_id, session_id , and custom metadata/tags with your traces, Langfuse automatically aggregates and breaks down costs by user, specific feature, model, or prompt version. It offers a generous free tier, an MIT-licensed self-hosted option, and affordable core/pro plans.superpenguin.ai+2
• Helicone (Open Source / Cloud) : An open-source LLM proxy/gateway that sits between your app and the model provider. Helicone excels at out-of-the-box request logging and cost analytics. By including user headers or properties in your API requests, it instantly surfaces user-level costs, cost per request, and spending trends categorized by custom tags.helicone.ai+2
• LangSmith (by LangChain) : Excellent if your stack is built heavily around LangChain or complex agentic workflows. It tracks token counts and costs natively across runs, though deep custom metadata filtering for user/feature billing analytics can feel more tightly coupled to debugging and evaluation workflows compared to dedicated cost-attribution tools like Langfuse.
• Portkey : Another robust AI gateway option that provides granular cost tracking, request routing, and guardrails, allowing you to segment and monitor usage by virtual keys, tags, and specific users.

How to set it up effectively:

No matter which platform you choose, the platform only knows what you send it. To get accurate breakdowns:

1. Inject User IDs: Pass your internal user_id into the tracing/logging SDK on every invocation.
2. Tag Features: Use metadata fields or tags (e.g., feature: "summarizer" or feature: "code_interpreter" ) so the dashboard can slice the financial data by use case.langfuse.com+1

If you want to narrow this down, tell me:

• Are you looking for a cloud-managed service or an open-source/self-hosted solution?
• What framework or libraries (e.g., LangChain, LlamaIndex, pure OpenAI/Anthropic SDKs) does your app use?

I can help recommend the easiest integration path for your stack.

If your main goal is “How much does my AI app cost per feature, user, customer, or workflow?”, I’d put Langfuse at the top of the list.

My recommendation

🥇 Langfuse — best overall for cost attribution

Langfuse is particularly well suited because it lets you attach application-level dimensions such as:

• user_id
• session_id
• feature/use-case tags
• customer/tenant
• model
• prompt/version
• environment

It then tracks token usage and cost at the trace/call level and lets you aggregate that data by users, tags, models, and use cases. Its Metrics API can also feed the numbers into your own billing or analytics system.Langfuse+1

So you can get reports like:

Feature A: $4,200/month Feature B: $1,150/month Average cost/user: $0.84 Customer Acme: $173/month Agent workflow X: $0.032/run That's much closer to AI unit economics than simply looking at your OpenAI/Anthropic invoice.

The shortlist

Platform	Best for	Per-user/feature attribution	Gateway / budgets
Langfuse	Cost attribution + observability	⭐⭐⭐⭐⭐	⭐⭐⭐
Helicone	Easy cost tracking + gateway	⭐⭐⭐⭐⭐	⭐⭐⭐⭐

When I'd pick something else

Helicone if you want the quickest implementation. It operates as a proxy/gateway, so you can get request and cost analytics with relatively little application instrumentation. Its current Pro plan is $79/month plus usage-based pricing.Helicone.ai

LiteLLM if your bigger concern is preventing spend, rather than merely measuring it. It acts as a gateway and supports routing and budget controls, making it useful when you need things like “this customer cannot spend more than $50/month.”LLM CFO

Braintrust if cost is one part of a larger LLM evaluation/experimentation workflow.GenAI QA

LangSmith if you're already heavily invested in LangChain/LangGraph.

One important distinction

I'd separate cost attribution from cost enforcement:

Attribution:

“Which users/features generated the $27,000 bill?” → Langfuse / Helicone

Enforcement:

“Don't let customer X spend more than $100, or route expensive requests to a cheaper model.” → LiteLLM / Portkey / gateway layer

For a serious production application, using Langfuse + a gateway such as LiteLLM can actually make more sense than trying to force one product to do everything.TECHSY

What I'd choose for your use case

Start with Langfuse. It has the strongest fit for the specific question you're asking: “What does each feature/user/customer actually cost us?” It is also open source/self-hostable, while the hosted version provides managed tracing and cost tracking. Current hosted pricing includes 50k units on Hobby and 100k units on Core/Pro, with additional usage priced per 100k units.Langfuse+1

If you tell me your stack (OpenAI/Anthropic/etc., Python/Node, framework, and whether you have agents), I can recommend the exact setup and show you how I'd instrument it to produce a per-user + per-feature P&L dashboard.

For your goal — “show me my LLM spend broken down by feature, workflow, tenant, or user” — you probably want an LLM observability / AI cost attribution platform, not a traditional cloud FinOps tool.

My shortlist:

Platform	Best for	Feature/user cost breakdown	My take
Langfuse	Product teams that need deep attribution + debugging	Excellent (user, session, trace, prompt, workflow)	Best overall choice
Helicone	Fast setup and cost dashboards	Excellent (user, feature, custom metadata)	Best if cost visibility is the priority
LangSmith	LangChain-heavy teams	Good	Best if you already use LangChain
Portkey	Enterprise AI gateway/routing	Good	Strong for multi-model infrastructure
Datadog	Enterprises already standardized on Datadog	Good when combined with existing APM	Usually overkill for LLM-only cost tracking

AI Coolies Future AGI## My recommendation

1. Start with Langfuse if you want product-level economics

I would choose Langfuse for most AI applications because it lets you answer questions like:

• “How much did our PDF summarization feature cost last month?”
• “Which customers consume the most tokens?”
• “What is the cost per successful support ticket?”
• “Which prompt version increased spend?”
• “What does one agent workflow cost end-to-end?”

The important concept is that you tag every LLM call with business dimensions:

user_id
tenant_id
feature
workflow
model
environment
request_id

Then your cost model becomes:

LLM call
  → trace
    → workflow
      → feature
        → customer
          → dollars

Langfuse is particularly good when you care about understanding why costs happen, not just where the bill went.AI Coolies

2. Pick Helicone if you want the quickest path to a dashboard

Helicone is attractive if you want to get visibility quickly:

• cost per request
• cost by user
• cost by custom properties
• model spend
• latency
• caching opportunities

It can work as a proxy/gateway, which means you can often add tracking with minimal application changes.Helicone

A common pattern:

Your App
   |
   v
Helicone Gateway
   |
   +--> OpenAI
   +--> Anthropic
   +--> Gemini

This is great for answering “who is burning tokens?” quickly.

3. If you are building a SaaS product, add your own cost ledger

Regardless of platform, I would avoid making the observability vendor your billing system.

Store normalized usage events yourself:

ai_usage_events

id
timestamp
user_id
organization_id
feature
model
input_tokens
output_tokens
cost_usd
latency_ms
success

Then you can later calculate:

• AI cost per customer
• gross margin per plan
• AI cost as % of revenue
• chargeback/showback reports

A practical stack

For a startup/mid-size AI SaaS:

• Langfuse → tracing + attribution + debugging
• Postgres/Snowflake/BigQuery → financial reporting
• Stripe → customer billing
• Grafana/Metabase/Looker → dashboards

For a large enterprise:

• Helicone or Portkey → gateway/control plane
• Datadog/Splunk/CloudZero → enterprise cost governance
• internal warehouse → source of truth

Bottom line

For your exact requirement:

1. 🥇 Langfuse — best for “what feature/user caused this AI spend?”
2. 🥈 Helicone — best for “show me LLM costs immediately with minimal setup”
3. 🥉 LangSmith — best if your app is already built around LangChain

The deciding question is: Are you trying to manage internal AI costs, or do you need to bill customers for AI usage? The architecture changes quite a bit.

To get a granular cost breakdown by feature (use case) or by user , traditional cloud cost management tools (like AWS Cost Explorer or Datadog) fall short because they don't understand LLM tokens, prompts, or semantic routing.langfuse.com+1

Instead, you need an LLM Gateway or Observability Platform that supports metadata tagging and token-level accounting. The top platforms for this specific requirement include:

1. Portkey (Best for Gateway-Driven Cost Governance)

Portkey combines an AI gateway with deep request-level observability. It is exceptionally strong for attribution because it lets you attach metadata directly to your LLM requests to segment costs by user or feature.docs.portkey.ai+2

• How it tracks by user/feature: You pass custom metadata (e.g., user_id or feature_name ) alongside your LLM payload. Portkey indexes this data into real-time analytics dashboards.
• Standout features: Budget enforcement/spend limits per virtual key, workspace, or user; automated tracking of cached vs. completion tokens; and an Analytics API if you want to pipe those metrics into your own user-facing billing dashboards.docs.portkey.ai+3
• Explore further: Check out the Portkey Cost Management Documentation.docs.portkey.ai

2. Langfuse (Best Open-Source & Observability-First Approach)

Langfuse is an open-source LLM engineering platform that excels at detailed trace-level cost tracking. It automatically infers costs for major providers (OpenAI, Anthropic, Google) and maps them across nested application traces.langfuse.com+1

• How it tracks by user/feature: Langfuse sessions and traces natively accept userId and tags/metadata parameters. When you log a generation or a multi-step chain, attributing it to a specific feature workflow or end-user happens natively within the trace hierarchy.
• Standout features: Granular handling of complex modern pricing types (cached input tokens, reasoning tokens, and multimodal audio tokens); highly flexible open-source self-hosting or managed cloud options.langfuse.com+1
• Explore further: Read more on Langfuse Token and Cost Tracking.langfuse.com

3. LangSmith (Best if You Are in the LangChain Ecosystem)

If your app is built using LangChain or you want pre-baked guardrails and centralized gateway controls, LangSmith’s LLM Gateway provides robust accounting features.youtube.com+1

• How it tracks by user/feature: By routing your requests through the LangSmith Gateway using distinct API keys or workspace identifiers, you can track token spend and enforce hard spending limits granularly by user workspace or project team before the request goes out.
• Standout features: Real-time audit trails showing exact models, keys, token consumption, and costs; active prevention of runaway agent loops via pre-request spend ceilings.
• Explore further: See the announcement on the LangSmith LLM Gateway.youtube.com

How to Choose:

• Choose Portkey if your primary goal is real-time rate-limiting, proxy-level routing, and user-level budget caps.
• Choose Langfuse if you want an open-source, deeply analytical trace breakdown of what specific prompts, chains, and features cost under the hood.langfuse.com+2

If you tell me what framework or SDK you are using (e.g., raw OpenAI/Anthropic client, LangChain, LlamaIndex) and whether you prefer an open-source or managed SaaS tool, I can recommend the easiest one to integrate into your stack.