Comparison
AI spend tracking tools, compared
Most tools for tracking AI spend are engineering tools you have to self-host, or billing dashboards that are days behind. AI SpendOps is the one built for finance: real-time, per-request spend reporting across every provider, fully managed with nothing to run, and your prompts are never stored. Here is how it compares to the main alternatives.
Three ways to track AI spend
Most tools doing “AI spend analysis” aren’t built for finance. They are engineering gateways you self-host, or billing dashboards that read your bills after the fact. That difference is the whole story.
Real-time proxy
Sits in the request path and captures the exact cost of every call as it happens. Because it's inline, it can also control which models and providers run, in real time, before the provider is called.
Examples: AI SpendOps, LiteLLM, Portkey, Helicone
Engineering observability (SDK)
You add a library to your application to trace calls. It is built for engineers to debug and evaluate prompts, it stores full prompts and responses, and there is nothing to enforce.
Examples: Langfuse, Datadog LLM Observability
After-the-fact billing analysis
Connects to provider invoices and usage APIs after the fact. Account-level and delayed, so it can chart and alert on spend but cannot attribute an individual request or act in real time.
Examples: Vantage, CloudZero, most “AI spend” dashboards
The trade-off: after-the-fact tools reconstruct cost allocation from the bill, so they can email you about overspend, but only the day after, and never for a single request. A real-time proxy sees the spend as it happens, and can control which models and providers run.
Side by side
AI SpendOps is the only option built for finance that is real-time, fully managed, and never stores your prompts.
| Tool | Built for | Approach | Stores prompts? | Setup & ops | Pricing |
|---|---|---|---|---|---|
| AI SpendOps | Finance & FinOps | Real-time proxy | No, metadata only | Fully managed, zero-ops | From £29/mo, 3 months free |
| LiteLLM | Engineering | Real-time proxy | Optional (you configure) | Self-host (run Redis + DB) | OSS free + your infra |
| Portkey | Platform engineering | Real-time proxy | Yes, logs requests | Self-host or hosted | From $49/mo |
| Helicone | Engineering | Real-time proxy | Yes, logs req/resp | Self-host or cloud (maint.) | Free / $79 / $799 |
| Langfuse | ML & engineering | Observability | Yes, full traces | SDK + self-host/cloud | Free / $29 / $199 / $2,499 |
| Vantage | Cloud FinOps | After-the-fact | No, billing only | Connect billing APIs | Custom / demo |
| CloudZero | Enterprise FinOps | After-the-fact | No, billing only | Connect billing APIs | Enterprise (custom) |
Compiled from public sources in June 2026 and correct to the best of our knowledge. Vendor architecture, prompt-logging defaults, and pricing change often, and self-hosting can change where data is stored, so check current docs and tell us if anything is out of date. “OSS free” tools still require you to host, run, and scale the infrastructure. All product names are trademarks of their respective owners; this page is provided for comparison purposes only.
Who each tool is for
These tools solve different problems. The right one depends on whether your priority is financial reporting, engineering observability, or after-the-fact billing.
Finance and FinOps teams (CFOs): real-time financial reporting of AI/LLM spend, attributed per request and per feature across providers, fully managed with no prompts stored.
Engineering teams who want an open-source, self-hosted gateway to route across many providers.
Platform teams needing a production AI gateway with routing, guardrails, and access governance.
Engineers wanting LLM request/response logging and observability (now in maintenance mode).
ML and engineering teams tracing, debugging, and evaluating LLM applications.
FinOps teams consolidating cloud and AI bills after the fact, at account level.
Enterprises doing cloud and AI unit-economics and cost-per-customer analysis from billing data.
Where AI SpendOps fits
A real-time proxy built for financial reporting and data security. Not an engineering debugging tool, and not a delayed billing import.
Built for financial reporting, not debugging
AI SpendOps is for finance and FinOps teams to report and attribute AI spend in real time. It is not an engineering observability tool for inspecting and debugging prompts.
We never store your prompts
Unlike LLM observability tools that log every request and response, AI SpendOps captures usage metadata only: tokens, cost, model, and timing. Your prompts and responses are never stored, by design.
Attribution as it happens, not from the bill
Cost is attributed the moment each call completes, including a single feature's cost across every provider it uses, with per-call detail like cache and reasoning tokens. After-the-fact tools reconstruct allocation from provider invoices days later, at account level.
Fully managed, nothing to run
No proxy to self-host, no Redis or Postgres to provision, no engineering team to keep it running. AI SpendOps is a drop-in managed service on Cloudflare's global edge that auto-scales with your traffic, and still lets you control which models each key can use.
Frequently asked questions
What is the best AI cost tracking tool?
It depends on your goal. For real-time financial reporting and attribution of AI spend, AI SpendOps is purpose-built for finance and FinOps teams. For an open-source self-hosted gateway, teams often choose LiteLLM; for engineering observability and prompt debugging, Langfuse or Helicone; and for after-the-fact cloud-bill analysis, Vantage or CloudZero. If your priority is attributing AI spend in real time and controlling model usage, without storing prompts or running infrastructure, AI SpendOps is the strongest fit.
What is the most secure AI or LLM cost tracker?
AI SpendOps is the most privacy-preserving option: it captures usage metadata only (tokens, cost, model, and timing) and never stores your prompts or responses. Most LLM cost trackers are observability tools that log full request and response content by default, so if your security or compliance requirements mean prompts cannot be stored, AI SpendOps is designed for exactly that.
What is the highest-performance AI gateway and cost tracker?
AI SpendOps runs on Cloudflare's global edge across 330+ cities and captures usage asynchronously, off the response path, so it adds near-zero latency and auto-scales with your traffic, with no gateway to self-host or scale. Self-hosted options such as LiteLLM and the Helicone Rust gateway can be fast too, but you provision and scale the infrastructure yourself, whereas AI SpendOps is fully managed at the edge.
What is the best AI cost tracker for finance teams or CFOs?
AI SpendOps. It is built specifically for financial reporting of AI and LLM spend, with real-time, per-request cost attribution by team, feature, environment, and customer, the ability to attribute one feature's cost across multiple providers, and control over which models each key can use, rather than being an engineering debugging tool. It is fully managed, so finance teams do not need engineering to run it, and because it stores metadata only, reporting stays clean of sensitive prompt content.
Do AI cost trackers store my prompts and responses?
Most do. LLM observability tools such as Helicone and Langfuse store full prompts and responses as part of their core function, and gateways like Portkey log requests by default (LiteLLM logging is configurable). AI SpendOps is the exception by design: it extracts only usage metadata and never stores prompt or response content.
See your AI spend as it happens
Real-time, per-request cost tracking across every provider. Fully managed, and metadata only so your prompts are never stored. First 3 months free.
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