Cloro vs PromptShark

Cloro and PromptShark 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.

Cloro

Cloro is a single API that sits in front of ChatGPT, Perplexity, Copilot, Gemini, AI Mode, and Google AI Overviews, returning structured JSON with the text, markdown, HTML, parsed sources, citations, search queries, and shopping cards that the provider UIs surface but their direct APIs omit. A single request, a single auth token, a single response schema across providers — so your team stops maintaining six integration layers and one provider's breaking change stops your entire pipeline. The free tier caps at 500 credits per month with one concurrent job, which is enough to validate a use case but not enough to run production monitoring at any real keyword volume. Teams tracking hundreds of queries across multiple providers will exhaust that ceiling quickly and step up to a paid tier. Self-hosting is not an option.

PromptShark

PromptShark is a local MITM proxy written in Go and C++ that sits between your agent and any OpenAI-compatible API endpoint. Every request and response pair is captured, logged, and surfaced in a real-time dashboard — no changes to your agent code, just a single base_url swap. The loop detector flags infinite tool-calling cycles automatically. The time-travel replay feature lets you re-run or edit any historical API step without firing a live request, which means no extra token spend during debugging. The self-hosted, MIT-licensed architecture means your prompts and API keys never leave your own infrastructure.

Attribute	Cloro	PromptShark
Pricing	Paid	Free
Free trial	No	No
Open source	No	Yes
Has API	Yes	Yes
Self-hosted option	No	Yes
Platforms	—	Cross-platform (Go binary + Docker)
Pros	Single API covers ChatGPT, Perplexity, Copilot, Gemini, AI Mode, and AI Overview under one auth token and one response schema, so you stop writing and maintaining separate integrations for each provider every time one changes its API surface. Returns parsed sources, citations, search queries, and shopping blocks — the structured data the provider UIs show but direct APIs omit — which means your SEO analysis reflects what users actually see rather than a stripped-down completion. Response format is selectable (markdown, text, or HTML) with shopping cards and source positions included in the same response, so downstream parsing stays consistent regardless of which engine answered the query. Credit-based pricing scales with volume and the per-credit rate drops at higher tiers, so teams with predictable query volume can forecast costs in a way token-based provider pricing makes impossible. Python and TypeScript SDKs ship with the API, so integration into an existing data pipeline or monitoring script is a client instantiation and a method call rather than a custom HTTP layer.	Zero-instrumentation integration via a single base_url swap, so you get full request visibility without touching your agent's codebase or adding an SDK dependency. Automatic infinite tool-calling loop detection, which means runaway agent cycles are flagged before they drain your token budget rather than after you read the invoice. Per-step token usage and USD cost tracking surfaced in real time, so you can attribute spend to specific agent decisions rather than receiving an opaque monthly total. Time-travel replay lets you re-run and edit any past API step without issuing a live request, so iterating on a prompt variant costs nothing in tokens during a debugging session. MIT-licensed and fully self-hostable via Docker, which means your prompts, responses, and API keys remain on your own infrastructure with no third-party data handling.
Cons	The free tier allows only one concurrent job, so any monitoring workflow that runs queries in parallel hits a queue immediately — teams doing batch keyword tracking across providers will exhaust both the concurrency limit and the 500-credit monthly cap within a single test run and must commit to a paid tier before real work begins. No self-hosted deployment option exists, which means every query routes through Cloro's infrastructure — teams operating under data residency requirements or internal security policies that prohibit third-party intermediaries handling query content cannot use this tool and will revert to building and maintaining direct provider integrations themselves. Grok is listed as unavailable in the provider matrix, so teams whose analysis specifically requires X's AI search responses get no coverage here and must build a separate integration or switch to a tool that includes it. The credit model creates a layer of cost uncertainty at scale: each provider and query type consumes credits at rates that may vary, and teams running high-frequency monitoring across multiple engines can burn through tiers faster than a simple per-query estimate suggests — budget modeling requires testing actual consumption against real workloads before committing to a tier.	The proxy is scoped to OpenAI-compatible API calls; agents using Anthropic, Google, or other non-OpenAI-format endpoints get no coverage, and teams with a mixed provider setup gain only partial visibility. The docs describe no multi-user access controls or role separation, so the tool cannot be safely exposed across a development team without additional network-layer restrictions — teams beyond a single developer typically add a separate access layer or switch to a purpose-built observability platform. There is no alerting or notification system described in the repository; when a loop fires at 2 AM, the dashboard captures it, but nobody is paged — teams with production monitoring requirements move to a dedicated tracing platform such as LangSmith or Langfuse that integrates with existing alerting pipelines. With only 3 commits and 1 star at the time of curation, the project is early-stage; the community reports no track record of sustained maintenance, which is a real risk if you build a debugging workflow around it and a breaking API change in the upstream OpenAI spec goes unpatched.

Bottom line

Cloro is paid while PromptShark is free; PromptShark is open source. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between Cloro and PromptShark?

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

Is Cloro better than PromptShark?

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.