Control AI Cost and
Token Usage

Every prompt, model call and retry has a cost.
See which ones matter.

Track token usage, model calls, latency, and workflow behavior so you can understand what every AI request costs and optimize AI spend.

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Built for teams working in .NET, Python, and JavaScript. No credit card required. 5-minute setup. Free for small teams

Schedule a Demo

85%

faster root cause analysis

faster time to resolution

<5 min

to first trace

AI Spend is Driven by Hidden Runtime Behavior, not Pricing Tables

Pricing shows cost per token—not why usage spikes. Prompts, retries, tool calls, retrieval patterns, and model choices quietly increase spend as workflows run.

Progress AI Observability ties cost and token usage to execution traces, so teams can pinpoint expensive patterns, optimize workflows, and avoid surprise bills.

See the Cost Signals Behind Each AI Request

AI usage is growing, but your team does not know which requests or workflows are consuming the most tokens. See token usage and estimated costs across instrumented applications, models, providers and time periods.

Identify high-cost patterns earlier and prioritize optimization of work based on real usage.

LLM Cost Analytics
Token Usage Tracking
AI Cost Attribution by Agent, App, and Workflow

Your teams are experimenting with different models or providers and need to understand the cost impact of those choices. Compare model and provider usage using cost, latency, and token data captured from AI spans.

Make better model-selection decisions with cost, latency and workflow context in view.

Model and Provider Cost Comparison
Cost, Quality, and Latency Tradeoff Analysis

One of your agents is calling models or tools more often than expected, driving up token usage, latency and cost.View cost and token signals alongside traces, LLM requests, tool spans and workflow behavior.

See whether cost is coming from prompt size, repeated calls, tool behavior, retrieval patterns, orchestration logic or model choice.

Token Usage Tracking
AI Agent Trace Explorer
Tool, Retrieval, and Workflow Tracing

Your engineering leaders need to understand expected usage before moving AI systems from development to production. Track organization-level usage, units, plan consumption and cost signals across teams, applications, and workflows.

Spot fast growth, untracked teams or tools, and usage patterns that may need alerting, forecasting, policy review or an intake process before invoice shock.

AI Metrics Dashboards and Alerts
AI Cost Attribution by Agent, App, and Workflow
Token Usage Tracking

"We're extensively using AI Observability to monitor our cost model, allowing us to analyze customer charges at any time. This feature has filled a critical visibility gap."

Jeremy Schaab

Vice President Software Development, FYIsoft

Get Observable Agents in Minutes

Progress AI Observability fits into your existing agent workflows with lightweight SDKs for .NET, Python, and JavaScript. Start capturing execution data quickly, then use it to understand, debug, and improve agent behavior.

Instrument your AI agents with lightweight integrations that capture prompts, model calls, todiv usage, retrieval steps and state.

Observe agent behavior end to end using session- and trace-level views designed specifically for multi-step and multi-agent workflows.

Improve reliability, performance, and cost by debugging failures, running evaluations and tuning orchestration and model choices using real production data.

Get Started in Minutes

.NET Python Javascript

// .NET - Install & Instrument
// 1. Install
dotnet add package Progress.Observability.Instrumentation
// 2. Instrument
chatClient = chatClient.AddObservability(options =>
{
  options.AppName = Environment.GetEnvironmentVariable("OBSERVABILITY_APP_NAME")!;
  options.ApiKey  = Environment.GetEnvironmentVariable("OBSERVABILITY_API_KEY")!;
});

# Python - Install & Instrument
# 1. Install
pip install progress-observability
# 2. Instrument
from progress_observability import Observability; import os
 
Observability.instrument(
  app_name=os.getenv("OBSERVABILITY_APP_NAME"),
  api_key=os.getenv("OBSERVABILITY_API_KEY")
)

// TypeScript - Install & Instrument
// 1. Install
npm install progress-observability
 
// 2. Instrument
import { Observability } from 'progress-observability';
 
Observability.instrument({
  appName: process.env.OBSERVABILITY_APP_NAME,
  apiKey: process.env.OBSERVABILITY_API_KEY
});

Featured AI Cost and Token Usage Capabilities

LLM Cost Analytics and Attribution

Track AI spend across apps, agents, workflows, models, providers, teams, and customers so you can identify cost drivers, understand ownership, and prioritize optimization.

Token Usage Tracking

Monitor input and output tokens, request volume, latency, and usage trends across your AI systems.

Model and Provider Comparison

Compare cost, latency, token usage, and output quality across models and providers to improve selection and routing decisions.

Dashboards, Alerts, and Reporting

Visualize cost, usage, latency, and quality trends, investigate spikes, and set thresholds or budget alerts before spend or performance issues get out of control.

AI Stack and Enterprise Support

Support common languages, frameworks, model providers, and observability tools with enterprise-ready security, privacy controls, and auditability.

Follow the Evidence Across the AI Production Workflow

Cost analysis shows what spend is driven by, so teams can optimize with context.

Trace and observe

See Execution Paths

Latency and tokens
Tools and retrieval
Outputs

Explore Trace and Observe

Debug

Diagnose Agent Failures

Skipped tools
Retrieval issues
Loops and errors

Explore Debug

Control costs

Track AI Spend

Token usage
Model selection
Workflow patterns

Explore Cost Control

Evaluate and Improve

Improve AI Output Quality

LLM-as-a-Judge
Quality scores
Prompt and model changes

Explore Evaluate & Improve

Connected Evidence

Reliable Releases

Start Your First Trace in Minutes.
Scale When You're Ready.

Progress AI Observability makes it easy to get started with flexible, affordable pricing that grows with your needs.

Free ForeverFor developers testing early agent prototypes

^$ 0

per month

Includes 10,000 units

Retention: 7 days

Start Free

Agent Trace Explorer
LLM request and prompt logging
Basic cost and token visibility
Basic LLM-as-a-Judge evaluations
.NET, Python and TypeScript SDKs
Integrations with popular AI frameworks and model providers

StarterFor small teams deploying their first live AI agents

^$ 29

per month

Includes 200,000 units

Retention: 30 days

$8 USD per additional 100K units

Schedule a Demo

Everything in Free, plus:
Full Cost Attribution (per-agent, per-model, total costs)
Real-Time & Historical LLM-as-a-Judge Evaluations
Evaluation Datasets & Experiments
Anomaly Detection & Alerting

ProFor teams running production AI agents at scale

^$ 299

per month

Includes 1,000,000 units

Retention: 60 days

$8 USD per additional 100K units

Schedule a Demo

Everything in Starter, plus:
SSO Included

EnterpriseFor organizations scaling governed AI applications

Starting at

^$ 3,000

per month

Custom trace volume

Retention: Infinite

Schedule a Demo

Everything in Pro, plus:
BYOS data residency options for teams with strict data control requirements
Enterprise governance with audit logs, access controls and SLA commitments
Custom volume pricing for high-throughput AI applications and AI labs

Frequently Asked Questions

The most common questions teams ask when evaluating AI observability for production agents.

What is AI cost tracking?

AI cost tracking helps teams understand how much AI agents and LLM applications cost to run in production. It connects token usage, model calls, provider metadata, latency, traces, evaluations, and workflows so teams can see where spend is coming from. Progress AI Observability supports this through LLM Cost Analytics, Token Usage Tracking, and AI Cost Attribution by Agent, App, Team, and Workflow.
How do teams track LLM cost in production?

Teams track LLM cost by instrumenting AI applications to capture model calls, token usage, provider and model metadata, latency, and related trace data. Progress AI Observability aggregates this data in LLM Cost Analytics so teams can understand usage trends and estimated costs by model, provider, application, agent, and workflow.
Why do AI costs increase unexpectedly?

AI costs can increase because of long prompts, repeated model calls, inefficient retrieval, retries, agent loops, high-volume traffic, expensive model choices, evaluation runs, or observability data that scales without clear controls. Progress AI Observability connects cost and token usage to real execution traces so teams can find the behaviors driving spend before the next invoice.
What is a unit in Progress AI Observability?

A unit is the basic measure of usage in the Progress Observability Platform. One telemetry span equals one unit, and evaluation tasks consume additional units based on the spans evaluated.

This unit-based model helps make AI observability more predictable. Instead of charging based on the size of a prompt, response, or trace payload, Progress helps teams understand observability usage in discrete units, so richer AI traces do not automatically become another hidden cost.
Can Progress show cost by model or provider?

Yes. Progress AI Observability supports Model and Provider Cost Comparison using cost, latency, token usage, and model/provider data captured from AI spans and LLM requests. Teams can compare model and provider usage to make better routing, optimization, and model-selection decisions.

What is the difference between AI usage tracking and AI cost tracking?

AI usage tracking shows how much an AI system is being used, including requests, spans, token volume, evaluations, and plan consumption. AI cost tracking connects that usage to estimated spend by model, provider, agent, app, team, or workflow. Together, Token Usage Tracking and LLM Cost Analytics help teams understand both consumption and cost impact.
Can cost optimization affect AI quality?

Yes, if teams optimize blindly. Reducing tokens, switching models, changing retrieval, or limiting evaluation runs can affect output quality. Progress AI Observability supports Cost, Quality, and Latency Tradeoff Analysis by showing cost alongside traces, latency, and evaluation results so teams can reduce waste without losing sight of quality.
How do you allocate AI costs by team or project?

Teams can allocate AI costs using tags, metadata, application names, environments, customers, releases, projects, teams, agents, or workflows. Progress AI Observability supports AI Cost Attribution by Agent, App, Team, and Workflow so teams can understand who or what is driving spend.
How often should AI spend be reviewed?

Teams should review AI spend during development, before major releases, after deployment, and regularly in production. With AI Usage Dashboards, Alerts, and Reporting, teams can monitor token usage, estimated cost, model usage, plan consumption, and usage trends before spikes become invoice surprises.
How does Progress AI Observability help control hidden AI costs?

Progress AI Observability helps teams monitor production AI without letting observability itself become another hidden cost. It connects token usage, estimated cost, model calls, tools, traces, evaluations, and workflows so teams can find the agent behaviors driving spend and fix expensive patterns earlier.

Track Your AI Spend Across
Every System!

Start tracking token usage, latency and estimated cost across your AI agents and LLM-powered applications. Use Progress AI Observability to identify expensive workflows, compare model usage and scale production AI with more confidence.

Start Free

Start free. Production and enterprise options are available for teams that need higher usage, more seats, longer retention and advanced cost attribution.

Request a Demo

Control AI Cost and Token Usage

AI Spend is Driven by Hidden Runtime Behavior, not Pricing Tables

See the Cost Signals Behind Each AI Request

Get Observable Agents in Minutes

Featured AI Cost and Token Usage Capabilities

LLM Cost Analytics and Attribution

Token Usage Tracking

Model and Provider Comparison

Dashboards, Alerts, and Reporting

AI Stack and Enterprise Support

Follow the Evidence Across the AI Production Workflow

See Execution Paths

Diagnose Agent Failures

Track AI Spend

Improve AI Output Quality

Reliable Releases

Start Your First Trace in Minutes. Scale When You're Ready.

Free ForeverFor developers testing early agent prototypes

StarterFor small teams deploying their first live AI agents

ProFor teams running production AI agents at scale

EnterpriseFor organizations scaling governed AI applications

Frequently Asked Questions

Track Your AI Spend Across Every System!

Control AI Cost and
Token Usage

Start Your First Trace in Minutes.
Scale When You're Ready.

Track Your AI Spend Across
Every System!