Generative AI workloads on AWS are powerful but notoriously expensive. Token usage, model invocations, GPU provisioning, and storage can all scale unpredictably, leaving teams with runaway bills and little clarity on which features or teams are driving costs. Traditional cloud monitoring only scratches the surface — it doesn’t capture the unique cost patterns of GenAI.

In this blog, we’ll break down what GenAI cost optimization means, the benfits, and compare the platforms available — from AWS-native tools like CloudWatch, Budgets, and Cost Explorer, to specialized third-party tools — so you can see the tradeoffs and find the right approach for your workloads.

Benefits of AI Cost Optimization

Optimizing generative AI workloads on AWS helps: 

  • Reduce waste by eliminating idle GPU and storage resources.

  • Understand your costs and see which prompts, models, or applications are consuming the most.

  • Attribute costs to features, products, or business units so spending accountability is clear across the organization.

  • Detect anomalies in token or model usage early to catch and troubleshoot sudden spikes. 

  • Improve forecasting by modeling future token growth and GPU demand based on historical usage patterns.

  • Optimize scaling of inference endpoints and training workloads to avoid paying for unused capacity.

  • Increase price efficiency, making the most of discounts like reserved or Spot capacity.

  • Support compliance and governance by ensuring GenAI costs are tracked and reported accurately.

11 Top GenAI Cost Optimization Platforms

The best generative ai cost management tools include:

1. nOps

nOps helps organizations take control of unpredictable generative AI costs by analyzing current usage across OpenAI, Gemini, Llama, Bedrock, and other providers. The tool identifies the most cost-effective AI models for your workloads, giving you a clear migration roadmap that balances price, performance, and quality.

Pros

  • 100% visibility into GenAI spending with cost by project, API, and business metric
  • Transparent breakdowns of input tokens, output tokens, batch vs cached usage, and more
  • Actionable recommendations to migrate to the most cost-efficient AI models based on actual usage data
  • Benchmarking with MMLU quality scores to evaluate tradeoffs across cost, accuracy, and latency
  • Free migration roadmap with quantified business cases to ensure savings are both immediate and sustainable

Best For
Organizations using OpenAI, Gemini, or Llama that want to cut GenAI costs significantly by moving to Bedrock while maintaining high performance and model quality.

Year Founded
2017

Pricing
Flat, predictable fee — no percentage of your GenAI spend.

G2 Rating
4.8/5 (rated #1 in cloud cost management category)

2. AWS CloudWatch

Tools for GenAI workloads AWS

AWS CloudWatch is AWS’s general monitoring service, recently extended with GenAI observability features for Bedrock and other AI services. It provides prompt tracing, model invocation counts, latency, and error rates so teams can monitor GenAI workloads within the same dashboards and alarms they already use for other AWS resources. While not purpose-built for AI cost optimization, it can be adapted to give partial visibility into GenAI usage.

Pros

  • Native integration with AWS Bedrock, agents, and model invocation APIs

  • Prompt tracing with per-request latency and error visibility

  • Works seamlessly with existing AWS monitoring, IAM, and tagging policies

  • Near real-time streaming of invocation and error metrics

Cons

  • Not designed as a dedicated AI cost tool — primarily a general AWS monitoring service

  • Lacks token-level visibility or cost attribution by feature, product, or team

  • Alerting and sampling granularity can be coarse compared to specialized AI observability tools

  • Does not provide optimization guidance or model migration recommendations

Best For
AWS customers who want to leverage existing CloudWatch dashboards and alerts for GenAI workloads, and don’t require full token-level cost attribution or optimization guidance.

Year Founded
2009 (CloudWatch launch year)

Pricing
Pay-as-you-go, based on CloudWatch metrics, logs, and traces ingested; no dedicated GenAI pricing tier.

G2 Rating
4.3/5 (general AWS CloudWatch rating; GenAI observability features are too new to be separately rated)

3. AWS Cost Explorer + Budgets

AWS Cost Explorer and Budgets are native AWS billing and governance tools. Cost Explorer lets you visualize spend trends and filter by tags, accounts, and services, while Budgets allows you to set alerts when costs exceed defined thresholds. These services are not built specifically to reduce GenAI cost, but they can still be used to track AI-related costs if you tag workloads consistently or separate Bedrock usage into its own accounts or projects.

Pros

  • Native AWS integration with no extra setup or third-party tools

  • Provides spend history and trends broken down by account, region, or tag

  • Supports daily granularity for cost data and budget checks

  • Works across all AWS services, not just AI

  • Can be combined with tagging strategies to approximate GenAI cost attribution

Cons

  • Not AI-native — no token-level or model-level metrics

  • Cost and usage data is delayed (typically 8–24 hours, not real-time)

  • Budgets only send alerts when thresholds are crossed; no anomaly detection

  • Provides spend visibility but no optimization guidance or savings recommendations

Best For
Finance or FinOps teams that need high-level AWS billing visibility and budgeting controls, and are willing to use tagging or account separation to approximate GenAI cost tracking.

Year Founded
2014 (Cost Explorer), 2017 (Budgets)

Pricing
Free for Cost Explorer; AWS Budgets includes 62 free budget checks per month, with additional checks billed at $0.02 each.

G2 Rating
4.4/5 (general AWS billing tools rating; not AI-specific)

4. Datadog

AWS cost optimization using generative AI

Datadog has extended its observability platform with LLM Observability and Cloud Cost Management features. These capabilities allow teams to track token usage and costs for OpenAI workloads, view latency and error metrics alongside application traces, and visualize costs in unified dashboards. While Datadog isn’t AWS-native, it integrates with AWS services and can monitor both infrastructure and AI usage.

Pros

  • Tracks OpenAI token usage and cost per prompt or request

  • Integrates latency, error, and throughput metrics into the same traces as costs

  • Rich dashboards and visualizations for linking cost with application performance

  • Can unify GenAI metrics with broader infra and application monitoring in Datadog platform

Cons

  • Primarily focused on OpenAI APIs today — less coverage for AWS Bedrock and other model providers

  • Token cost tracking relies on instrumented calls; not automatically integrated into AWS billing data

  • Strong observability but limited FinOps features like commitment management or optimization guidance

Best For
Engineering and platform teams already using Datadog who want to extend observability to LLM usage

Year Founded
2010

Pricing
Usage-based pricing depending on ingested metrics, logs, and traces; no dedicated AI cost tier.

G2 Rating
4.3/5 

5. Coralogix

Coralogix has introduced AI Cost Tracking as part of its observability platform, extending its real-time logging and monitoring into LLM workloads. It provides token-level usage visibility, cost attribution by agent or feature, anomaly detection, and budget enforcement. Coralogix is not AWS-native, but it integrates with AWS services and is purpose-built to surface the unique patterns of GenAI costs.

Pros

  • Token and API-level usage tracking for LLM workloads

  • Cost attribution down to agents, features, or teams

  • Anomaly detection, budgets alerts for unusual spikes in token consumption 

Cons

  • Narrower focus than full FinOps platforms — less depth in areas like commitments or enterprise-wide AWS billing integration

  • Works best for teams already using Coralogix for observability; adoption may require adding another platform if not

Best For
Teams that want real-time token-level AI cost monitoring and anomaly detection, especially those already using Coralogix for observability.

Year Founded
2014

Pricing
Usage-based pricing tied to metrics and logs ingested; AI cost tracking is bundled into the platform rather than priced separately.

G2 Rating
4.6/5 

6. Weights & Biases

Weights & Biases (W&B) extends its experiment tracking platform with cost monitoring features for both training and inference. It lets ML and GenAI teams log resource usage, track cost per run, and compare efficiency across models or configurations. 

Pros

  • Tracks compute, storage, and runtime costs alongside model training runs

  • Supports token usage logging for LLM inference experiments

  • Dashboards to compare performance, accuracy, and cost across runs

  • Helps identify the most efficient models and hyperparameter settings

  • Integrates with popular ML frameworks and pipelines for seamless setup

Cons

  • Primarily focused on experimentation and training workflows — less suited for production-scale cost allocation by team or feature

  • Does not include financial governance features such as budgets, commitments, or chargeback models

Best For
ML and GenAI research teams running frequent experiments who want to understand the cost-performance tradeoff of different models, training runs, and inference settings.

Year Founded
2018

Pricing
Free tier available for individuals; team and enterprise plans priced by seats and usage.

G2 Rating
4.5/5

7. Moesif

Moesif is an API analytics and monetization platform that extends into AI cost tracking by monitoring usage and spend on LLM and GenAI APIs. It helps product and engineering teams attribute costs to endpoints, users, and features, making it easier to understand how AI usage translates into business expenses.

Pros

  • Tracks API calls and token consumption at the endpoint and user level

  • Dashboards that link usage, latency, and cost for full visibility

  • Anomaly detection and alerts for sudden spikes in API or token consumption

  • Helps product teams understand business impact and cost-to-value ratio of AI features

Cons

  • Focused on API usage — less visibility into infrastructure costs like GPUs or storage

  • Does not provide optimization guidance for model selection or cloud commitments

Best For
Product and engineering teams that expose GenAI features via APIs and want to understand usage and cost drivers at the endpoint and customer level.

Year Founded
2017

Pricing
Usage-based pricing with a free developer plan; scales with API call volume and features enabled.

G2 Rating
4.6/5

8. Humanloop

Humanloop is a developer-first LLMOps platform that provides observability, prompt versioning, and cost tracking for GenAI applications. It logs inputs, outputs, and metadata for every model invocation, helping teams track token usage and costs at a granular level while iterating quickly on prompts and deployments.

Pros

  • Logs prompt inputs, outputs, and metadata with token usage and cost

  • Supports prompt versioning and experiment tracking for controlled iteration

  • Easy integration into GenAI applications through SDKs and APIs

  • Helps developers see how cost changes across prompt variations and model choices

Cons

  • Primarily designed for developers — less suited for Finance or FinOps teams needing business-level cost attribution

  • Limited support for enterprise financial controls like budgets, chargeback, or commitment optimization

  • Cost insights focus on tokens and prompts, not broader infrastructure or multi-cloud costs

  • May require additional tools for anomaly detection or large-scale reporting across business units

Best For
Engineering and product teams experimenting with prompts and models who want to monitor usage and cost during development and early production.

Year Founded
2020

Pricing
Free tier available for developers; paid plans scale with usage and collaboration features.

G2 Rating
4.7/5

9. Langfuse

Langfuse is an open-source observability and analytics platform for LLM applications. It captures token usage, latency, error rates, and traces across multiple models and frameworks, giving developers transparency into how prompts and models perform in production. Langfuse can be self-hosted or run as a managed service.

Pros

  • Tracks input/output tokens, latency, and errors at the invocation level; traces and dashboards for analyzing model performance and usage patterns

  • Open-source and customizable, with option to self-host for full data control

  • Active community support and frequent updates from contributors

Cons

  • Requires engineering effort to deploy, maintain, and integrate into workflows

  • Does not provide built-in billing integration or financial governance features

  • Limited enterprise features like budgets, chargeback, or commitment optimization

  • Best suited for technical users; less accessible for Finance or business stakeholders

Best For
Developer and platform teams that want detailed observability for LLM applications, and prefer an open-source solution they can customize or self-host.

Year Founded
2022

Pricing
Free and open-source; managed cloud service available with paid plans based on usage.

G2 Rating
4.6/5

10. Traceloop

Traceloop is an open-source observability platform designed for LLM applications. It provides tracing for prompts, responses, token usage, latency, and errors, allowing developers to understand how GenAI models behave in production. With SDKs and integrations, Traceloop makes it easier to debug, monitor, and attribute costs across different LLM providers.

Pros

  • Captures detailed traces of prompts, responses, tokens, latency, and errors

  • Works across multiple LLM providers through simple SDK integrations

  • Provides dashboards to analyze usage and performance trends

  • Open-source and extensible, with options to self-host or integrate with existing observability stacks

Cons

  • Developer-focused — lacks enterprise financial features like budgets, chargeback, or commitment optimization

  • Requires setup and ongoing engineering effort to deploy and maintain

  • Cost visibility is tied to usage metrics; no direct integration with AWS billing data

  • Smaller ecosystem and community compared to more established observability platforms

Best For
Developer teams building and operating LLM-based applications who want transparent observability and token-level insights without relying on commercial SaaS tools.

Year Founded
2022

Pricing
Free and open-source; enterprise support available through managed offerings.

G2 Rating
4.5/5

11. Arthur AI

Arthur AI is a GenAI observability and monitoring platform available in the AWS Marketplace. It helps teams track the performance, reliability, and costs of generative AI applications running on Amazon Bedrock. Arthur provides detailed monitoring of model invocations, latency, and error rates, along with governance features for responsible AI use.

Pros

  • Available directly through the AWS Marketplace with native Bedrock integration

  • Tracks invocation metrics, latency, and error rates for GenAI workloads

  • Includes governance and compliance tooling alongside observability

  • Designed specifically for generative AI applications rather than general infra

Cons

  • More focused on model reliability and governance than deep FinOps cost attribution

  • Limited optimization features compared to full cost platforms like nOps

Best For
Organizations running GenAI workloads on Amazon Bedrock that want an AWS-native observability option with governance features.

Year Founded
2018

Pricing
Marketplace-based pricing, billed through AWS; usage-based tiers.

G2 Rating
4.5/5

The Bottom Line

Many tools in the GenAI cost space focus on narrow aspects of efficiency — from Datadog’s token and latency tracing, to Coralogix’s real-time anomaly detection, Moesif’s API-level attribution, and developer-first platforms like Langfuse or Humanloop for logging prompts and usage. 

nOps is the only truly comprehensive GenAI optimization platform, including: 

  • Token-level visibility across OpenAI, Bedrock, Gemini, and other models

  • Migration assessments to identify the most cost-effective LLMs

  • Unified reporting for GenAI, multi-cloud, SaaS, and infrastructure costs

  • Built-in anomaly detection and cost allocation for Finance and Engineering

  • Automated commitment management for RIs, Savings Plans, and Spot

  • A FinOps AI agent for natural language queries and automated FinOps tasks

nOps was recently ranked #1 with five stars in G2’s cloud cost management category, and we optimize $2+ billion in cloud spend for our customers — book a demo with one of our AWS experts or get a quick tour below. 

Demo

AI-Powered Cost Management Platform

Discover how much you can save in just 10 minutes!

Book a Demo

Frequently Asked Questions

Let’s dive into some FAQ about GenAI cost monitoring tools.

What are GenAI cost optimization strategies?

GenAI cost optimization strategies are methods to reduce the unpredictable expenses of running large language models and AI workloads. They include rightsizing GPU and storage, tracking token usage, optimizing inference scaling, using Spot or reserved capacity, and allocating costs by team or feature for accountability.

How to save AWS cost on GenAI applications?

Generative AI workloads can quickly become expensive because of GPU demand, large-scale training, and storage needs. To save costs, rightsize resources, use Spot Instances strategically, leverage auto-scaling, and schedule workloads during off-peak hours. 

How to track your Generative AI cost?

Tracking GenAI cost requires visibility into GPU, storage, and token usage across teams and workloads. Use detailed tagging, allocation by project, and monitoring dashboards. nOps provides unified reporting that breaks down GenAI expenses by application, business unit, or customer, giving Finance and Engineering accurate insight into usage trends.