Zelaros connects to your billing APIs and surfaces cost data by provider, model, team, and seat. The initial connection takes 15 minutes. The data available to your CTO, Engineering leads, and Finance team is immediate and does not require ongoing maintenance.
Why Zelaros
Direct API providers and wrapped SaaS tools in one view with consistent spend data, trend lines, and attribution. No instrumentation. No code to deploy.
For the CTO →Map billing data to your team structure so squad leads, service owners, and platform teams see exactly what they are consuming. No tagging conventions to enforce.
For VP Engineering →Department-level cost allocation, 12 months of unified spend history, and exportable board-ready summaries. Forecast AI spend with the same discipline as every other budget category.
For Finance →Features
Zelaros aggregates billing data across all connected providers into a single view with a consistent data model. Provider-level spend, model-level breakdown, month-over-month trend, and the split between API costs and SaaS subscription costs all appear in one place. No toggling between dashboards. No manual reconciliation.
Michael opens Zelaros on the Monday before a board meeting and sees the trailing 90 days of AI spend across OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Cursor, and GitHub Copilot on a single screen. Total spend is $47,200 for the month. Anthropic API usage is up 34% month-over-month, driven by a new document processing feature. He exports a two-page summary and sends it to the CFO before the pre-board prep call.
The CTO view in Zelaros is designed for strategic oversight and executive communication. It shows total AI spend by provider and business function, month-over-month trend with percentage change, cost-per-workflow estimates for key product surfaces, and a side-by-side view of direct API costs versus SaaS subscription spend. Reports export as clean PDFs formatted for executive presentation.
Michael sets a monthly export to go to the CFO automatically on the first business day of each month. The report shows total AI spend, the three largest cost drivers, and a 12-month trend chart. Before Zelaros, this took half a day to assemble. Now it generates without any manual work.
The Engineering view maps API spend to team and service-level structures using your existing billing metadata. Squad leads see their own costs. Platform engineers see cross-team totals. Service-level attribution shows which product features and API endpoints are driving the highest costs. No custom tagging infrastructure required.
Sarah gives each of her five squad leads access to their own Zelaros view. The search squad lead notices their Anthropic spend is up 60% in two weeks, traces it to a new reranking model added to the search pipeline, and reduces the call frequency before the billing cycle closes. Sarah finds out about the spike and the fix from the squad lead directly, not from an invoice.
The Finance view provides department-level cost allocation, a rolling 12-month spend history, month-end projection based on current run rate, and exportable cost allocation reports for chargeback. Every export is formatted for use in budget decks, board presentations, and FP&A models without additional formatting work.
Rachel's FP&A analyst runs the monthly AI cost allocation report from Zelaros on the last business day of each month. Each department head receives their allocation automatically. Rachel uses the 12-month trend data to build the AI line item in the annual budget, benchmarked against actual usage growth rather than a guess. The board AI spend slide is generated from Zelaros in three minutes.
Configure spend thresholds by provider, service, team, or total portfolio. Zelaros monitors daily consumption against your thresholds and sends alerts via email or Slack when a limit is crossed. Alerts include the provider, the magnitude of the spike, and the time window, so the first response can be investigation rather than reconstruction.
Sarah configures a daily alert for any provider where spend exceeds 150% of the trailing 7-day average. On a Tuesday morning she receives an alert: OpenAI spend is up 210% from the previous day's baseline. She traces it to a batch job accidentally deployed to production instead of staging. The job is stopped within two hours. Without the alert, she would have found out at month end.
Zelaros projects month-end spend for each provider based on current daily run rate, adjusted for day-of-week patterns observed in your historical data. Finance can see projected totals updating in real time throughout the month. The projection model accounts for the difference between fixed SaaS subscription costs and variable API consumption costs.
By the 10th of the month, Rachel can see that total AI spend is tracking to $52,000 against a $45,000 budget. Anthropic API costs are the variance driver. She flags it to Engineering on day 12. They identify a recently shipped feature running more inference than estimated and put a temporary rate limit in place. The month closes at $47,800.
Within each provider, Zelaros breaks cost down to the model level. You can see how much of your OpenAI spend is GPT-4o versus GPT-4o mini, or how much of your Anthropic spend is Claude Opus versus Claude Sonnet. Model-level data is essential for making intelligent decisions about which models to use for which workflows based on cost and capability.
Michael reviews the model-level breakdown and notices that 70% of the company's Anthropic spend is on Claude Opus for a summarization workflow that was originally scoped as a prototype. Switching to Claude Sonnet for that workflow reduces the monthly Anthropic bill by $8,400 without measurable quality impact.
Zelaros uses existing API key structure and billing metadata to attribute costs to the teams and services that generate them. Where API keys are already scoped by team or service, attribution is automatic. Where keys are shared, Zelaros provides a key management view to help engineering teams establish cleaner attribution going forward.
Sarah has three squads sharing a single OpenAI API key because the keys were set up before the team scaled. Zelaros surfaces this as an attribution gap and recommends creating squad-scoped keys. The platform team implements the change in one sprint. Within 30 days, Sarah has clean squad-level attribution for all OpenAI usage.
Zelaros pulls seat-level utilization data for wrapped SaaS tools including Cursor, GitHub Copilot, and Claude.ai Teams. You can see last active date, activity frequency, and utilization tier for each seat. Filter for inactive seats above a threshold you define and export the list for renewal decisions.
Sarah runs the Cursor utilization report two weeks before the annual renewal. Of 48 seats, 11 have had no activity in the past 45 days. She removes those 11 seats from the renewal, saving $4,840 per year. She presents utilization data for the remaining 37 seats to justify the renewal to Finance with specific usage metrics rather than a general assertion that the team finds it useful.
Every Zelaros report exports to CSV for import into FP&A tools, ERP systems, and spreadsheet models, and to PDF for board decks, audit documentation, and vendor review packages. Export templates are structured to match standard cost allocation formats so Finance teams do not need to reformat data after exporting.
Rachel's Controller exports the monthly AI cost allocation report to CSV and imports it directly into the company's FP&A model. Department heads receive PDF summaries automatically on the first business day of each month. The export takes 90 seconds. The previous process of manual data collection and formatting took 4 to 6 hours.
Roadmap
The current product gives you full billing-layer visibility with no engineering work required. The roadmap adds depth at each layer: feature-level attribution, spend governance, and ROI measurement.
The billing API layer tells you what each provider charged. The SDK layer tells you which specific features, workflows, and user actions generated those charges. Zelaros will offer an optional lightweight SDK that wraps your existing AI API calls and attributes costs to the feature or workflow context you define. This answers the question 'what does it cost to run our document summarization pipeline per document processed' rather than just 'what did we spend on Anthropic this month.'
Once you have attribution data, the next step is acting on it systematically. Zelaros will add policy rules that enforce spend behavior at the API call level: block calls to specified models above a cost threshold, require approval for model upgrades, and route requests to lower-cost models when usage exceeds a budget. This moves Zelaros from a visibility tool to a governance layer.
Spend visibility without output measurement answers half the question. The ROI measurement layer will connect AI cost data to business outcome metrics you define, so you can see cost per output for the workflows that matter most: cost per support ticket resolved, cost per document processed, cost per API call in your product's critical path. This is the layer that turns the board's 'what are we getting for this' question into a data-based answer.
Integrations
Zelaros is expanding integration coverage to additional direct API providers and enterprise AI platforms as they reach sufficient adoption in the mid-market. If your stack includes a provider not listed here, contact us and we will prioritize accordingly.