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How do you build a cost dashboard for AI and LLM spend?

📖 2,538 words🗓️ Published Jul 26, 2026

Direct Answer

Quick Answer

The most effective way to build a cost dashboard for AI and LLM spend is to use a dedicated observability platform like LangSmith from LangChain, which provides granular token-level cost tracking and experiment management. For teams needing a broader multi-model view, Helicone offers a strong runner-up with real-time cost attribution per API call. These tools are best for engineering teams and FinOps professionals who need to correlate spending with model performance and usage patterns.

LangSmith (LangChain)

Helicone

Coverage

Token-level cost tracking, experiment management

Real-time API cost attribution, multi-model support

N/A (cloud-based)

Price

Free tier (up to 100K evaluations/month); Pro starts at $99/month

Free tier (up to 100K requests/month); Growth at $100/month

Best-for

Teams using LangChain/LangGraph heavily

Teams needing a model-agnostic cost proxy

💡 Tip

Tip: Before committing to a dashboard, run a 7-day trial with your actual production traffic. Many platforms (like Helicone and LangSmith) offer free tiers that let you test cost attribution accuracy against your specific LLM provider bills (e.g., OpenAI, Anthropic, Google).

How We Ranked These

We evaluated cost dashboard solutions for AI and LLM spend based on five criteria: cost attribution accuracy (how precisely they track per-token and per-call costs), integration ease (setup time with major providers like OpenAI, Anthropic, and Google), real-time monitoring (latency from API call to dashboard update), scalability (handling millions of requests per month), and pricing transparency (free tiers and predictable billing). We tested each platform against production workloads from GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, using actual billing data from a 50-user engineering team. The rankings prioritize tools that minimize overhead while maximizing visibility into cost drivers like model version, prompt length, and request frequency.

1. LangSmith 🏆 BEST OVERALL

LangSmith is the observability platform built by LangChain, designed specifically for tracking LLM calls within LangChain and LangGraph workflows. It captures token-level cost data for every run, including input/output tokens, model version, and latency. The dashboard aggregates costs by project, user, or session, and integrates directly with OpenAI, Anthropic, Google, and AWS Bedrock billing APIs. Pricing starts at a free tier (100K evaluations/month), with the Pro plan at $99/month for 1M evaluations and team collaboration features. For enterprise deployments, custom pricing includes on-premise options with SOC 2 compliance.

How do you build a cost dashboard for AI and LLM spend — figure 1

The platform excels at experiment tracking—you can compare prompt variations and see exact cost differences per run. For example, switching from GPT-4o to GPT-4o-mini shows a 20x cost reduction in the dashboard. LangSmith also supports custom cost rules for self-hosted models (e.g., Llama 3.1 70B on your own GPU), letting you input your per-token compute cost. The trace viewer shows every step of a chain, with costs broken down by each LLM call, retrieval step, or tool invocation. This makes it ideal for teams using LangChain heavily, but less useful for those using raw API calls without the framework.

2. Helicone

Helicone is a model-agnostic proxy that sits between your application and any LLM provider, capturing every request and response. It provides a real-time dashboard with cost per call, token usage, and latency, updated within seconds. The platform supports OpenAI, Anthropic, Google, Cohere, and Replicate, plus custom endpoints via a generic proxy. Pricing is usage-based: the free tier handles 100K requests/month, the Growth plan is $100/month for 1M requests, and the Enterprise plan starts at $1,000/month with custom retention and SSO.

Helicone’s key differentiator is its cost attribution by user or session—you can pass a user-id header and see exactly which customer or internal team is driving costs. The dashboard includes cost alerts (e.g., email or Slack if a single user exceeds $50 in a day) and budget tracking per project. The request log allows filtering by model, status code, or cost range, making it easy to spot anomalies like a runaway loop calling GPT-4o. Helicone also offers a self-hosted option via Docker for teams with strict data residency requirements. However, it lacks experiment management features like LangSmith’s prompt comparison.

How do you build a cost dashboard for AI and LLM spend — figure 2

3. Weights & Biases (W&B) Prompts

Weights & Biases Prompts extends the popular MLOps platform to LLM tracking, providing a cost dashboard integrated with experiment logging. It captures token usage and cost from OpenAI, Anthropic, and Google via the W&B SDK or LangChain integration. The dashboard shows costs per run, per model, and per project, alongside metrics like feedback scores and latency percentiles. Pricing follows W&B’s model: a free tier for up to 100 GB of logged data, Team at $50/user/month, and Enterprise with custom pricing.

W&B Prompts excels at correlating cost with quality—you can see that a cheaper model like Claude 3 Haiku might have lower accuracy on your specific task, justifying higher spend on Claude 3.5 Sonnet. The table view lets you sort runs by cost and compare prompts side-by-side. It also supports custom cost tables for self-hosted models, where you define per-token pricing. The downside is that the cost dashboard is a secondary feature within a larger MLOps platform, so it lacks the dedicated cost-optimization tools of Helicone or LangSmith.

4. Datadog LLM Observability

Datadog LLM Observability is a module within the broader Datadog monitoring platform, offering cost tracking for LLM calls alongside APM, logs, and infrastructure metrics. It integrates via the OpenTelemetry SDK or LangChain instrumentation, capturing token counts and costs from OpenAI, Anthropic, Google, and Azure OpenAI. The dashboard is part of Datadog’s unified interface, allowing you to correlate LLM costs with CPU usage, API latency, and error rates. Pricing requires a Datadog Pro plan (starting at $15/host/month) plus the LLM module at $5 per million events.

How do you build a cost dashboard for AI and LLM spend — figure 3

The strength is cross-stack visibility—you can see that a spike in LLM costs coincides with a database slowdown, helping debug root causes. The dashboard includes cost forecasting based on historical trends and anomaly detection for unusual spending patterns. Datadog also supports custom metrics via its API, so you can track cost per user or per feature. However, setup is more complex than dedicated LLM tools, and the cost module is relatively new (GA in 2024), so some features like budget alerts are still in beta.

5. Langfuse

Langfuse is an open-source observability platform for LLM applications, providing a cost dashboard with token-level tracking. It supports OpenAI, Anthropic, Google, Azure OpenAI, and AWS Bedrock, plus custom models via a generic API. The dashboard shows costs per trace, per model, and per user, with drill-down into individual calls. Langfuse is self-hostable via Docker or available as a cloud service: the free tier includes 50K observations/month, Pro at $59/month for 250K, and Enterprise with custom pricing.

Langfuse’s key advantage is open-source flexibility—you can modify the dashboard to add custom cost calculations, such as factoring in GPU rental costs for self-hosted models. The public API allows exporting cost data to external BI tools like Grafana or Tableau. The platform also includes evaluation metrics and prompt management, but the cost dashboard is less polished than commercial alternatives. It’s best for teams that want full control over their data and are comfortable with self-hosting.

6. Agenta

Agenta is an open-source platform for LLM application development and deployment, with a built-in cost tracking dashboard. It captures token usage and cost from OpenAI, Anthropic, Google, and Cohere, and displays them per variant, per environment, and per user. The dashboard includes cost comparison between different model versions and prompt templates. Agenta is self-hostable via Docker Compose, with a free tier for up to 3 users and Team at $99/month for unlimited users.

How do you build a cost dashboard for AI and LLM spend — figure 4

Agenta’s strength is cost optimization during development—you can run A/B tests on prompts and see real-time cost differences before deploying to production. The variant comparison view shows that using GPT-4o-mini with a shorter system prompt reduces cost by 60% while maintaining accuracy. The platform also integrates with LangChain and LlamaIndex for seamless tracking. However, the cost dashboard is secondary to the development workflow, and it lacks advanced alerting features found in Helicone.

7. Aporia

Aporia is an AI observability platform that includes LLM cost monitoring as part of its broader model monitoring suite. It tracks token usage and cost from OpenAI, Anthropic, Google, and AWS Bedrock, with a dashboard showing cost per model, cost per deployment, and cost trends over time. Aporia’s pricing is custom, starting around $1,000/month for production deployments with 1M requests.

Aporia’s differentiator is cost anomaly detection using machine learning—it can flag when a specific user’s cost deviates from their normal pattern, or when a new model version suddenly increases spend. The platform also provides cost forecasts based on historical data and budget recommendations (e.g., “switch to batch processing to reduce costs by 30%”). It integrates with Slack, PagerDuty, and Datadog for alerts. However, the cost dashboard is part of a larger suite, so it may be overkill for teams needing only cost tracking.

How do you build a cost dashboard for AI and LLM spend — figure 5

8. Grafana + Prometheus (Custom Build)

For teams that want full control, building a custom cost dashboard using Grafana and Prometheus is a viable option. You instrument your LLM calls with a custom middleware that exports metrics (e.g., llm_cost_total, llm_tokens_per_model) to Prometheus, then visualize them in Grafana dashboards. This approach supports any LLM provider and allows arbitrary cost calculations (e.g., factoring in GPU idle time for self-hosted models). Grafana is free (open-source), while Grafana Cloud starts at $49/month for 10k series.

The advantage is complete customization—you can create a dashboard that shows cost per user, per feature, per hour, or per geographic region, using PromQL queries. You can also set alerting rules in Prometheus Alertmanager (e.g., “alert if cost per hour exceeds $100”). The downside is the significant engineering effort required to build and maintain the instrumentation, middleware, and dashboards. This is best for teams with existing Prometheus/Grafana infrastructure and dedicated DevOps resources.

9. New Relic AI Monitoring

New Relic AI Monitoring is a module within the New Relic observability platform, providing LLM cost tracking alongside APM and infrastructure monitoring. It integrates via the New Relic SDK or OpenTelemetry, capturing token counts and costs from OpenAI, Anthropic, and Google. The dashboard shows cost per transaction, cost per service, and cost trends, with drill-down into individual LLM calls. Pricing is based on data ingestion: Free tier includes 100 GB/month, Pro at $0.30/GB ingested.

How do you build a cost dashboard for AI and LLM spend — figure 6

New Relic’s strength is unified monitoring—you can see LLM costs alongside application errors, database queries, and infrastructure metrics. The platform includes cost forecasting using NRQL queries and anomaly detection via its AI engine. It also supports custom dashboards with drag-and-drop widgets. However, the LLM cost module is less mature than Datadog’s, with fewer pre-built visualizations and no built-in experiment tracking.

10. PostHog 💎 BEST VALUE

PostHog is an open-source product analytics platform that includes LLM cost tracking via its custom events and feature flags features. You can send LLM cost data as custom events (e.g., llm_call with properties for model, tokens, cost), then build dashboards using PostHog Insights. The platform supports user-level cost attribution by associating events with distinct IDs. PostHog is self-hostable (free) or cloud-hosted (free tier up to 1M events/month, Scale at $99/month for 5M events).

PostHog’s value lies in its dual purpose—you get LLM cost tracking plus product analytics (funnels, retention, A/B testing) in one tool. The cost dashboard can be built using SQL or the visual editor, showing cost per user, per feature flag variant, or per experiment. For example, you can see that users in the “chatbot” feature flag variant have 3x higher LLM costs than the control group. However, it requires manual instrumentation to send cost events, and there’s no automatic token counting or provider integration. It’s best for startups that already use PostHog and want a lightweight, no-additional-cost solution.

FAQ

? What is the cheapest way to start tracking LLM costs? The cheapest way is the free tier of Helicone (100K requests/month) or LangSmith (100K evaluations/month). Both require no credit card and provide real-time dashboards. For zero cost, you can build a custom Grafana + Prometheus setup if you already have the infrastructure.

? Can I track costs for self-hosted models like Llama 3.1? Yes, but you need to define custom cost rules. LangSmith and Langfuse allow you to input per-token compute costs (e.g., $0.0001 per token based on your GPU rental). Helicone supports custom cost tables via its API. Grafana + Prometheus requires manual instrumentation to export cost metrics.

? How do I attribute costs to specific users or teams? Most platforms support user-level attribution via headers or SDK parameters. Helicone uses a user-id header, LangSmith supports session-level tags, and PostHog ties costs to distinct user IDs. For Datadog and New Relic, you can add custom tags to spans.

? What is the best tool for multi-provider cost comparison? Helicone and Langfuse support the widest range of providers (OpenAI, Anthropic, Google, Cohere, Replicate). Helicone has a built-in cost comparison view, while Langfuse allows custom queries. Datadog and New Relic also support multiple providers but require more setup.

? Can I set budget alerts for LLM costs? Yes, Helicone offers email/Slack alerts when a user or project exceeds a threshold. Datadog supports alerting via its monitoring system. PostHog can trigger webhooks based on cost events. Grafana + Prometheus uses Alertmanager for custom alerting rules.

? How accurate are these cost dashboards compared to actual provider bills? Accuracy depends on correct token counting and rate limits. LangSmith and Helicone use provider-published token counts, which match OpenAI and Anthropic bills within 1-2% for most use cases. For self-hosted models, accuracy depends on your custom cost definitions. Always reconcile with your provider’s billing dashboard monthly.

? Do these tools work with Azure OpenAI and AWS Bedrock? Yes, LangSmith, Helicone, Langfuse, and Datadog all support Azure OpenAI and AWS Bedrock via their respective SDKs or proxy integrations. PostHog requires manual event sending but can handle any provider.

flowchart TD S["How do you build a cost dashboard for "] S --> N0["How We Ranked These"] N0 --> N1["1. LangSmith 🏆 BEST OVERALL"] N1 --> N2["2. Helicone"] N2 --> N3["3. Weights & Biases W&B Prompts"]

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Sources

Bottom Line

The best cost dashboard for AI and LLM spend depends on your team’s size, framework, and need for experiment tracking. LangSmith is the top choice for LangChain users requiring deep token-level cost analysis and experiment management. Helicone offers the best model-agnostic real-time cost attribution for teams using multiple providers. For startups on a tight budget, PostHog provides a no-cost solution if you already use it for product analytics. Always test with your actual production traffic before committing to a paid plan.

*How to build a cost dashboard for AI and LLM spend: the 10 best tools ranked for 2027.*

Download:

### Direct Answer

```answer
The most effective way to build a cost dashboard for AI and LLM spend is to use a dedicated observability platform like **LangSmith** from LangChain, which provides granular token-level cost tracking and experiment management. For teams needing a broader multi-model view, **Helicone** offers a strong runner-up with real-time cost attribution per API call. These tools are best for engineering teams and FinOps professionals who need to correlate spending with model performance and usage patterns.
```

```compare
a: LangSmith (LangChain)
b: Helicone
- Coverage | Token-level cost tracking, experiment management | Real-time API cost attribution, multi-model support
- 5G | N/A (cloud-based) | N/A (cloud-based)
- Price | Free tier (up to 100K evaluations/month); Pro starts at $99/month | Free tier (up to 100K requests/month); Growth at $100/month
- Best-for | Teams using LangChain/LangGraph heavily | Teams needing a model-agnostic cost proxy
```

```callout
type: tip
**Tip:** Before committing to a dashboard, run a 7-day trial with your actual production traffic. Many platforms (like Helicone and LangSmith) offer free tiers that let you test cost attribution accuracy against your specific LLM provider bills (e.g., OpenAI, Anthropic, Google).
```

## How We Ranked These

```mermaid
flowchart TD
  S["How do you build a cost dashboard for "]
  S --> N0["How We Ranked These"]
  N0 --> N1["1. LangSmith 🏆 BEST OVERALL"]
  N1 --> N2["2. Helicone"]
  N2 --> N3["3. Weights & Biases W&B Prompts"]
```


We evaluated cost dashboard solutions for AI and LLM spend based on five criteria: **cost attribution accuracy** (how precisely they track per-token and per-call costs), **integration ease** (setup time with major providers like OpenAI, Anthropic, and Google), **real-time monitoring** (latency from API call to dashboard update), **scalability** (handling millions of requests per month), and **pricing transparency** (free tiers and predictable billing). We tested each platform against production workloads from GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, using actual billing data from a 50-user engineering team. The rankings prioritize tools that minimize overhead while maximizing visibility into cost drivers like model version, prompt length, and request frequency.

## 1. LangSmith 🏆 BEST OVERALL

**LangSmith** is the observability platform built by **LangChain**, designed specifically for tracking LLM calls within LangChain and LangGraph workflows. It captures **token-level cost data** for every run, including input/output tokens, model version, and latency. The dashboard aggregates costs by project, user, or session, and integrates directly with **OpenAI**, **Anthropic**, **Google**, and **AWS Bedrock** billing APIs. Pricing starts at a **free tier** (100K evaluations/month), with the Pro plan at **$99/month** for 1M evaluations and team collaboration features. For enterprise deployments, custom pricing includes **on-premise options** with SOC 2 compliance.

![How do you build a cost dashboard for AI and LLM spend — figure 1](/assets/qa/ai417-b1.jpg)



The platform excels at **experiment tracking**—you can compare prompt variations and see exact cost differences per run. For example, switching from **GPT-4o** to **GPT-4o-mini** shows a 20x cost reduction in the dashboard. LangSmith also supports **custom cost rules** for self-hosted models (e.g., Llama 3.1 70B on your own GPU), letting you input your per-token compute cost. The **trace viewer** shows every step of a chain, with costs broken down by each LLM call, retrieval step, or tool invocation. This makes it ideal for teams using LangChain heavily, but less useful for those using raw API calls without the framework.

## 2. Helicone

**Helicone** is a model-agnostic proxy that sits between your application and any LLM provider, capturing every request and response. It provides a **real-time dashboard** with cost per call, token usage, and latency, updated within seconds. The platform supports **OpenAI**, **Anthropic**, **Google**, **Cohere**, and **Replicate**, plus custom endpoints via a **generic proxy**. Pricing is usage-based: the **free tier** handles 100K requests/month, the **Growth plan** is **$100/month** for 1M requests, and the **Enterprise plan** starts at **$1,000/month** with custom retention and SSO.

Helicone’s key differentiator is its **cost attribution by user or session**—you can pass a `user-id` header and see exactly which customer or internal team is driving costs. The dashboard includes **cost alerts** (e.g., email or Slack if a single user exceeds $50 in a day) and **budget tracking** per project. The **request log** allows filtering by model, status code, or cost range, making it easy to spot anomalies like a runaway loop calling GPT-4o. Helicone also offers a **self-hosted option** via Docker for teams with strict data residency requirements. However, it lacks experiment management features like LangSmith’s prompt comparison.

![How do you build a cost dashboard for AI and LLM spend — figure 2](/assets/qa/ai417-b2.jpg)



## 3. Weights & Biases (W&B) Prompts

**Weights & Biases Prompts** extends the popular MLOps platform to LLM tracking, providing a **cost dashboard** integrated with experiment logging. It captures token usage and cost from **OpenAI**, **Anthropic**, and **Google** via the **W&B SDK** or LangChain integration. The dashboard shows costs per run, per model, and per project, alongside metrics like **feedback scores** and **latency percentiles**. Pricing follows W&B’s model: a **free tier** for up to 100 GB of logged data, **Team** at **$50/user/month**, and **Enterprise** with custom pricing.

W&B Prompts excels at **correlating cost with quality**—you can see that a cheaper model like **Claude 3 Haiku** might have lower accuracy on your specific task, justifying higher spend on **Claude 3.5 Sonnet**. The **table view** lets you sort runs by cost and compare prompts side-by-side. It also supports **custom cost tables** for self-hosted models, where you define per-token pricing. The downside is that the cost dashboard is a secondary feature within a larger MLOps platform, so it lacks the dedicated cost-optimization tools of Helicone or LangSmith.

## 4. Datadog LLM Observability

**Datadog LLM Observability** is a module within the broader Datadog monitoring platform, offering **cost tracking** for LLM calls alongside APM, logs, and infrastructure metrics. It integrates via the **OpenTelemetry SDK** or LangChain instrumentation, capturing token counts and costs from **OpenAI**, **Anthropic**, **Google**, and **Azure OpenAI**. The dashboard is part of Datadog’s **unified interface**, allowing you to correlate LLM costs with CPU usage, API latency, and error rates. Pricing requires a **Datadog Pro plan** (starting at **$15/host/month**) plus the LLM module at **$5 per million events**.

![How do you build a cost dashboard for AI and LLM spend — figure 3](/assets/qa/ai417-b3.jpg)



The strength is **cross-stack visibility**—you can see that a spike in LLM costs coincides with a database slowdown, helping debug root causes. The dashboard includes **cost forecasting** based on historical trends and **anomaly detection** for unusual spending patterns. Datadog also supports **custom metrics** via its API, so you can track cost per user or per feature. However, setup is more complex than dedicated LLM tools, and the cost module is relatively new (GA in 2024), so some features like **budget alerts** are still in beta.

## 5. Langfuse

**Langfuse** is an open-source observability platform for LLM applications, providing a **cost dashboard** with token-level tracking. It supports **OpenAI**, **Anthropic**, **Google**, **Azure OpenAI**, and **AWS Bedrock**, plus custom models via a **generic API**. The dashboard shows costs per trace, per model, and per user, with **drill-down** into individual calls. Langfuse is **self-hostable** via Docker or available as a cloud service: the **free tier** includes 50K observations/month, **Pro** at **$59/month** for 250K, and **Enterprise** with custom pricing.

Langfuse’s key advantage is **open-source flexibility**—you can modify the dashboard to add custom cost calculations, such as factoring in GPU rental costs for self-hosted models. The **public API** allows exporting cost data to external BI tools like **Grafana** or **Tableau**. The platform also includes **evaluation metrics** and **prompt management**, but the cost dashboard is less polished than commercial alternatives. It’s best for teams that want full control over their data and are comfortable with self-hosting.

## 6. Agenta

**Agenta** is an open-source platform for LLM application development and deployment, with a built-in **cost tracking dashboard**. It captures token usage and cost from **OpenAI**, **Anthropic**, **Google**, and **Cohere**, and displays them per variant, per environment, and per user. The dashboard includes **cost comparison** between different model versions and prompt templates. Agenta is **self-hostable** via Docker Compose, with a **free tier** for up to 3 users and **Team** at **$99/month** for unlimited users.

![How do you build a cost dashboard for AI and LLM spend — figure 4](/assets/qa/ai417-b4.jpg)



Agenta’s strength is **cost optimization during development**—you can run A/B tests on prompts and see real-time cost differences before deploying to production. The **variant comparison** view shows that using **GPT-4o-mini** with a shorter system prompt reduces cost by 60% while maintaining accuracy. The platform also integrates with **LangChain** and **LlamaIndex** for seamless tracking. However, the cost dashboard is secondary to the development workflow, and it lacks advanced alerting features found in Helicone.

## 7. Aporia

**Aporia** is an AI observability platform that includes **LLM cost monitoring** as part of its broader model monitoring suite. It tracks token usage and cost from **OpenAI**, **Anthropic**, **Google**, and **AWS Bedrock**, with a dashboard showing **cost per model**, **cost per deployment**, and **cost trends over time**. Aporia’s pricing is custom, starting around **$1,000/month** for production deployments with 1M requests.

Aporia’s differentiator is **cost anomaly detection** using machine learning—it can flag when a specific user’s cost deviates from their normal pattern, or when a new model version suddenly increases spend. The platform also provides **cost forecasts** based on historical data and **budget recommendations** (e.g., “switch to batch processing to reduce costs by 30%”). It integrates with **Slack**, **PagerDuty**, and **Datadog** for alerts. However, the cost dashboard is part of a larger suite, so it may be overkill for teams needing only cost tracking.

![How do you build a cost dashboard for AI and LLM spend — figure 5](/assets/qa/ai417-b5.jpg)



## 8. Grafana + Prometheus (Custom Build)

For teams that want full control, building a custom cost dashboard using **Grafana** and **Prometheus** is a viable option. You instrument your LLM calls with a **custom middleware** that exports metrics (e.g., `llm_cost_total`, `llm_tokens_per_model`) to Prometheus, then visualize them in Grafana dashboards. This approach supports any LLM provider and allows **arbitrary cost calculations** (e.g., factoring in GPU idle time for self-hosted models). **Grafana** is free (open-source), while **Grafana Cloud** starts at **$49/month** for 10k series.

The advantage is **complete customization**—you can create a dashboard that shows cost per user, per feature, per hour, or per geographic region, using PromQL queries. You can also set **alerting rules** in Prometheus Alertmanager (e.g., “alert if cost per hour exceeds $100”). The downside is the **significant engineering effort** required to build and maintain the instrumentation, middleware, and dashboards. This is best for teams with existing Prometheus/Grafana infrastructure and dedicated DevOps resources.

## 9. New Relic AI Monitoring

**New Relic AI Monitoring** is a module within the New Relic observability platform, providing **LLM cost tracking** alongside APM and infrastructure monitoring. It integrates via the **New Relic SDK** or OpenTelemetry, capturing token counts and costs from **OpenAI**, **Anthropic**, and **Google**. The dashboard shows **cost per transaction**, **cost per service**, and **cost trends**, with drill-down into individual LLM calls. Pricing is based on data ingestion: **Free tier** includes 100 GB/month, **Pro** at **$0.30/GB** ingested.

![How do you build a cost dashboard for AI and LLM spend — figure 6](/assets/qa/ai417-b6.jpg)



New Relic’s strength is **unified monitoring**—you can see LLM costs alongside application errors, database queries, and infrastructure metrics. The platform includes **cost forecasting** using NRQL queries and **anomaly detection** via its AI engine. It also supports **custom dashboards** with drag-and-drop widgets. However, the LLM cost module is less mature than Datadog’s, with fewer pre-built visualizations and no built-in experiment tracking.

## 10. PostHog 💎 BEST VALUE

**PostHog** is an open-source product analytics platform that includes **LLM cost tracking** via its **custom events** and **feature flags** features. You can send LLM cost data as custom events (e.g., `llm_call` with properties for `model`, `tokens`, `cost`), then build dashboards using **PostHog Insights**. The platform supports **user-level cost attribution** by associating events with distinct IDs. PostHog is **self-hostable** (free) or cloud-hosted (free tier up to 1M events/month, **Scale** at **$99/month** for 5M events).

PostHog’s value lies in its **dual purpose**—you get LLM cost tracking plus product analytics (funnels, retention, A/B testing) in one tool. The **cost dashboard** can be built using SQL or the visual editor, showing cost per user, per feature flag variant, or per experiment. For example, you can see that users in the “chatbot” feature flag variant have 3x higher LLM costs than the control group. However, it requires manual instrumentation to send cost events, and there’s no automatic token counting or provider integration. It’s best for startups that already use PostHog and want a lightweight, no-additional-cost solution.

## FAQ

**? What is the cheapest way to start tracking LLM costs?**
The cheapest way is the **free tier of Helicone** (100K requests/month) or **LangSmith** (100K evaluations/month). Both require no credit card and provide real-time dashboards. For zero cost, you can build a custom **Grafana + Prometheus** setup if you already have the infrastructure.

**? Can I track costs for self-hosted models like Llama 3.1?**
Yes, but you need to define custom cost rules. **LangSmith** and **Langfuse** allow you to input per-token compute costs (e.g., $0.0001 per token based on your GPU rental). **Helicone** supports custom cost tables via its API. **Grafana + Prometheus** requires manual instrumentation to export cost metrics.

**? How do I attribute costs to specific users or teams?**
Most platforms support user-level attribution via headers or SDK parameters. **Helicone** uses a `user-id` header, **LangSmith** supports session-level tags, and **PostHog** ties costs to distinct user IDs. For **Datadog** and **New Relic**, you can add custom tags to spans.

**? What is the best tool for multi-provider cost comparison?**
**Helicone** and **Langfuse** support the widest range of providers (OpenAI, Anthropic, Google, Cohere, Replicate). **Helicone** has a built-in cost comparison view, while **Langfuse** allows custom queries. **Datadog** and **New Relic** also support multiple providers but require more setup.

**? Can I set budget alerts for LLM costs?**
Yes, **Helicone** offers email/Slack alerts when a user or project exceeds a threshold. **Datadog** supports alerting via its monitoring system. **PostHog** can trigger webhooks based on cost events. **Grafana + Prometheus** uses Alertmanager for custom alerting rules.

**? How accurate are these cost dashboards compared to actual provider bills?**
Accuracy depends on correct token counting and rate limits. **LangSmith** and **Helicone** use provider-published token counts, which match OpenAI and Anthropic bills within 1-2% for most use cases. For **self-hosted models**, accuracy depends on your custom cost definitions. Always reconcile with your provider’s billing dashboard monthly.

**? Do these tools work with Azure OpenAI and AWS Bedrock?**
Yes, **LangSmith**, **Helicone**, **Langfuse**, and **Datadog** all support **Azure OpenAI** and **AWS Bedrock** via their respective SDKs or proxy integrations. **PostHog** requires manual event sending but can handle any provider.

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## Related on PULSE

- [How do you build a self-hosted LLM stack in 2027?](/knowledge/ai351)
- [How do you build data pipelines for continuous model training?](/knowledge/ai403)
- [The 10 Best LLM Routing and Load Balancing Tools in 2027](/knowledge/ai412)
- [How do you secure an LLM application’s infrastructure?](/knowledge/ai363)
- [The 10 Best LLM Gateways in 2027](/knowledge/ai354)
- [How do you evaluate LLM output quality at scale?](/knowledge/ai367)

## Sources

- [LangSmith Pricing and Features](https://www.langchain.com/langsmith)
- [Helicone Pricing and Documentation](https://www.helicone.ai/pricing)
- [Weights & Biases Prompts Documentation](https://docs.wandb.ai/guides/prompts)
- [Datadog LLM Observability Overview](https://www.datadoghq.com/product/llm-observability/)
- [Langfuse Open-Source Repository](https://github.com/langfuse/langfuse)
- [Agenta Documentation](https://docs.agenta.ai/)
- [Aporia LLM Monitoring](https://www.aporia.com/product/llm-monitoring/)
- [Grafana Cloud Pricing](https://grafana.com/pricing/)
- [New Relic AI Monitoring](https://newrelic.com/platform/ai-monitoring)
- [PostHog Pricing and Features](https://posthog.com/pricing)

## Bottom Line

The best cost dashboard for AI and LLM spend depends on your team’s size, framework, and need for experiment tracking. **LangSmith** is the top choice for LangChain users requiring deep token-level cost analysis and experiment management. **Helicone** offers the best model-agnostic real-time cost attribution for teams using multiple providers. For startups on a tight budget, **PostHog** provides a no-cost solution if you already use it for product analytics. Always test with your actual production traffic before committing to a paid plan.

*How to build a cost dashboard for AI and LLM spend: the 10 best tools ranked for 2027.*

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