Best AI Cost Management Software for Databricks

There are an estimated 25 million engineers in the world across dozens of distinct functions. As every company becomes a software company, engineers are using New Relic to gather real-time insights and trending data about the performance of their software so they can be more resilient and deliver exceptional customer experiences. Only New Relic provides an all-in-one platform that is built and sold as a unified experience. With New Relic, customers get access to a secure telemetry cloud for all metrics, events, logs, and traces; powerful full-stack analysis tools; and simple, transparent usage-based pricing with only 2 key metrics. New Relic has also curated one of the industry’s largest ecosystems of open source integrations, making it easy for every engineer to get started with observability and use New Relic alongside their other favorite applications.

CloudZero is the leader in proactive cloud cost efficiency. We enable engineers to build cost-efficient software without slowing down innovation. CloudZero's next-generation cloud cost optimization platform automates the collection, allocation, and analysis of cloud costs to uncover savings opportunities and improve unit economics. We are the only platform that enables companies to understand 100% of their operational cloud spend and take an engineering-led approach to optimizing that spend. CloudZero is used by industry leaders worldwide, such as Coinbase, Klaviyo, Miro, Nubank, and Rapid7.

FinOpsly is an AI Cost Governance platform. It brings AI, cloud, data platform and SaaS spend into one attribution, policy and control layer, so enterprises can price a workload before building it, attribute every dollar to an owner, hold spend inside budget under policy, and prove what landed in run-rate. Your AI invoice is not what your AI costs. One request draws on model tokens, retrieval, warehouse queries, GPU capacity and storage, and only the first shows up on the AI bill. FinOpsly resolves all of it, plus the seats in procurement and the compute in an untagged cloud account, to the same dimensions: owner, team, application, line of business, customer and tenant. An AI initiative's full cost becomes one figure, charged back through one hierarchy in one cycle. Workforce AI is the tools employees use: seats and per-user token draw across GitHub Copilot, Cursor, ChatGPT Enterprise and Microsoft 365 Copilot. Application AI is the AI your product ships: tokens, compute and data joined into cost-to-serve across OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex AI, SageMaker and Databricks. PLAN. Price a workload from its architecture before any resource exists, across model APIs, GPU capacity, data platform consumption and storage, with assumptions visible. Compare it across candidate models on your measured usage. EXPLAIN. Attribute spend to owner, team, application, line of business and business unit across 9+ hierarchy levels. Unified tagging reconciles providers that tag inconsistently, and AI-driven bulk labeling closes large key estates. Unattributed spend is reported in dollars. ACT. Budgets per project, team and API key, with daily burn-rate monitoring. Anomaly detection with root cause, routed to the owner. Waste detection using FinOpsly's own algorithms and ML models. Commitment planning across AWS, Azure and Google Cloud. Policy-driven parking of idle compute. PROVE. Chargeback across AI, cloud, data and SaaS in one cycle. Realized savings tracked into run-rate against a no-action baseline. Cost per call, cost per active user, and cost-to-serve per customer and tenant. proof: 100% attribution of AI spend; chargeback from 12.4 days to under one day across 9+ levels; 26% realized savings in AWS and 17%+ in Azure at a payments client. Built for CIOs, CTOs and platform leaders accountable for technology spend, FinOps and finance teams running chargeback, and engineering teams who need cost signal before they decide

StackSpend is a cloud and AI cost management platform that gives engineering, finance, and FinOps teams one daily view of the modern AI stack. It connects through read-only credentials to providers including AWS, Google Cloud, Azure, Snowflake, Vercel, ClickHouse Cloud, Elastic Cloud, OpenAI, Anthropic, Cursor, GitHub, Hugging Face, Grok, and Twilio, then automatically loads historical billing data and normalizes spend across services. Dashboards and explorers break costs down by provider, service, model, project, user, team, feature, and customer, helping teams understand AI COGS, cost per request, and product-level margins. Budgets and pace-to-forecast show where monthly spending is headed, while same-day anomaly detection catches unusual increases caused by traffic, prompt bugs, model changes, deployments, or individual users. Alerts and daily green, amber, or red spend signals can be delivered through Slack, Microsoft Teams, email, or webhooks.

Finout combines Cloud Providers, Data Warehouses, and CDNs into one mega bill, enabling an unparalleled business context view of your cloud spend with no heavy lifting in minutes. Monitor anomalies, view recommendations and forecast cost per growth. While AWS charges you by the instance, you genuinely care about your pod cost. With no-agent integration, utilize your existing Datadog or Prometheus to get a pod-level granularity of your spend in minutes. Forget about absolute cloud cost. See the cost of what you are utilizing and not only what you are paying for. For example, view Kubernetes pods instead of EC2 instances and DynamoDB indexes. Finout can give you one unified language the entire company can talk in, not only DevOps.

Cost Reports are easy to use dashboards that provide complex reporting and filtering for accrued costs. Set filters to see day-to-day cost trends per service, business unit, tag or account. Chain complex logic to cover any reporting use case. Forecasts have confidence intervals that will adjust every day as your infrastructure changes to help you understand where you will end up. Get notified in Slack, Teams, or by email on a daily, weekly, or monthly basis about costs and trends. Receive alerts for cost anomalies. Autopilot analyzes your EC2 workloads and purchases 3 year, no-upfront reserved instances to save you money. Control which compute categories or regions Autopilot manages. Manage commitment and infrastructure changes with ease.

​LiteLLM is a versatile platform designed to streamline interactions with over 100 Large Language Models (LLMs) through a unified interface. It offers both a Proxy Server (LLM Gateway) and a Python SDK, enabling developers to integrate various LLMs seamlessly into their applications. The Proxy Server facilitates centralized management, allowing for load balancing, cost tracking across projects, and consistent input/output formatting compatible with OpenAI standards. This setup supports multiple providers. It ensures robust observability by generating unique call IDs for each request, aiding in precise tracking and logging across systems. Developers can leverage pre-defined callbacks to log data using various tools. For enterprise users, LiteLLM offers advanced features like Single Sign-On (SSO), user management, and professional support through dedicated channels like Discord and Slack.

Cloudgov.ai is an agentic AI FinOps platform for continuous cost and policy governance across cloud, multicloud, data, container, and AI environments. It brings AWS, Azure, Google Cloud, Oracle Cloud, Snowflake, Databricks, Kubernetes, OpenAI, Anthropic, and Gemini into one control plane, giving teams a live view of cost, allocation, policy, and risk. Continuous Multicloud Observability connects accounts, analyzes historical spending, filters costs by region, account, and service, and forecasts future spend from history. AI-driven insights identify waste and optimization opportunities, while anomaly detection highlights unexpected spending surges and their financial impact. Ready-to-use Infrastructure as Code remediation snippets help engineering teams apply recommended changes, and Jira integration turns insights and anomalies into assignable work.

Mavvrik is an AI and hybrid infrastructure cost management platform that gives finance, FinOps, IT, and engineering teams one control center for GenAI, autonomous agents, GPUs, cloud, on-premises systems, Kubernetes, data platforms, and SaaS. It unifies cost, usage, and telemetry signals from AWS, Azure, Google Cloud, Oracle, VMware, NVIDIA, OpenAI, Anthropic, Gemini, Snowflake, Databricks, and LiteLLM, creating a single source of truth across the technology stack. Teams can track every model call, agent interaction, GPU hour, workload, service, and resource, then allocate spending by customer, product, feature, project, application, environment, team, or cost center. Cost-to-serve and unit-economics analysis reveal margin drains, expensive workloads, and the true cost of delivering each offering. Real-time anomaly detection and alerts identify usage before it becomes a budget surprise, while predictive forecasting helps organizations model cloud, GPU, and AI expenses.