AgentMeter vs OfoxAI

AgentMeter and OfoxAI are both inference engines & infra tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

AgentMeter

AgentMeter runs locally — no cloud sync, no account creation, no vendor dashboard to log into — and parses the tool calls, token counts, and caching splits that CLI agents like Claude Code, Gemini CLI, Codex CLI, and Copilot CLI generate. It surfaces the three-tier cost structure that prompt caching creates (input, cached-input, and output tokens each priced differently), which the raw API bill flattens into noise. The value-multiplier calculation compares API spend against estimated developer time saved, giving you a number to put in front of a manager. The wall appears when you need alerting, real-time budget enforcement, or integration with a team billing system — none of that is here.

OfoxAI

OfoxAI is an API gateway that routes requests to 100+ models from providers including OpenAI, Anthropic, Google, DeepSeek, Qwen, and Mistral through one OpenAI-compatible base URL. The integration is a one-line SDK change: swap the base_url, keep your existing OpenAI client code. The vendor states ~300ms latency for standard requests and ~210ms for edge-routed workloads, with 99.9% uptime claimed and spending caps available for cost-controlled deployments. Where this breaks is anywhere you need vendor-specific features that fall outside the OpenAI chat completions schema — those edge cases require wrapping the gateway or hitting the provider directly.

Attribute	AgentMeter	OfoxAI
Pricing	Free	Paid
Free trial	No	No
Open source	Yes	No
Has API	No	Yes
Self-hosted option	Yes	No
Platforms	macOS, Linux, Windows (Python)	Web, API
Pros	Runs entirely on-device with no account, no cloud sync, and no vendor access to your session data, so usage patterns and project names never leave your machine. Breaks prompt-caching costs into the three actual billing tiers (input, cached-input, output), so you can see whether your caching strategy is paying off instead of inferring it from a flattened total. Per-session and per-project cost aggregation across Claude Code, Gemini CLI, Codex CLI, and Copilot CLI, which means you get a unified spend view instead of hunting across four separate dashboards. Value-multiplier calculation compares API spend against estimated developer time saved, so you have a concrete number when someone asks whether the agent usage is worth the invoice. Open-source under Apache-2.0, so you can audit exactly what it reads and how costs are calculated — no black-box pricing assumptions you have to take on faith.	OpenAI SDK compatibility out of the box — changing base_url is the entire migration, so teams avoid rewriting existing client code when adding a new provider. 100+ models across eight-plus providers behind a single API key, which means you stop managing separate credentials, billing accounts, and rate-limit budgets for each vendor. Spending caps at the deployment level, so a runaway loop or a traffic spike does not turn into an uncapped invoice at the end of the month. Edge routing targeting ~210ms latency, so Asia-Pacific and European workloads avoid the round-trip penalty of hitting US-based provider endpoints directly. Zero-markup billing on provider rates, which means cost modeling stays predictable — you are not absorbing a percentage fee on top of already-variable token costs.
Cons	There are no budget caps or threshold alerts. A session can exhaust your API credits before AgentMeter reports on it — the tool tells you what happened after the fact, not while it is happening. Teams that need spend enforcement have to wire up separate controls at the API key or infrastructure level. No shared or multi-user view exists. If two developers are both running Claude Code on the same project, their session data stays on their own machines. Teams that need consolidated spend reporting across contributors cannot get it here and will move to a vendor-native dashboard or a shared cost-tracking layer instead. Support is limited to CLI agents (Claude Code, Gemini CLI, Codex CLI, Copilot CLI). If your stack includes API-direct integrations, LangChain pipelines, or custom agent frameworks, AgentMeter produces nothing — you are back to reading raw API logs.	Provider-specific API features that fall outside the OpenAI chat completions schema — Anthropic's extended thinking, OpenAI Assistants thread management, provider-native streaming controls — either get flattened by the gateway or require you to bypass it entirely and call the provider directly, at which point you are maintaining two integration paths. No self-hosted option exists, which means teams with hard data-residency rules, air-gapped environments, or enterprise policies that prohibit third-party request intermediaries cannot use this at all — those teams route to a self-hostable gateway like LiteLLM or a direct provider integration instead. The no-logging guarantee is a vendor-stated claim with no independently auditable artifact described on the page; teams in regulated industries who treat that guarantee as a compliance control will hit a wall when their security review asks for a signed DPA or audit log.

Bottom line

AgentMeter is free while OfoxAI is paid; AgentMeter is open source; only OfoxAI exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between AgentMeter and OfoxAI?

AgentMeter is Free and open source, while OfoxAI is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is AgentMeter better than OfoxAI?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.