AI Playground
Free, MIT-licensed desktop app for running text and image prompts across 11 AI providers side by side.
If you bounce between provider dashboards all day, AI Playground collapses them into one window and keeps your prompt history and spend data on your own disk. The free price is real — you pay providers directly and Ollama runs at $0.00 — but you must be comfortable managing per-provider API keys, and there is no team workspace or shared URL. For visual side-by-side comparison and permanent local run history it beats juggling browser playgrounds; if you need a programmatic endpoint rather than a testing UI, a gateway such as OpenRouter or LiteLLM is the better fit.
Verified 18h ago · liveness 67/100 · cite: rightaichoice.com/tools/ai-playground
- Developers comparing model answers, latency, and cost side by side
- Privacy-conscious users who want keys, prompts, and history in a local SQLite file
- Ollama users who want a cleaner front end and $0.00 cost tracking
- Prompt engineers tuning temperature, top-p, system prompts, and image parameters across providers
- Non-technical users unwilling to create accounts and manage API keys per provider
- Teams needing shared workspaces, roles, or a hosted URL to hand to colleagues
- Anyone expecting free hosted inference — you bring your own keys or run Ollama locally
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Skip AI Playground if you need a hosted, shared workspace your whole team can open in a browser, or a programmatic API endpoint — this is a local single-user desktop testing lab with no login, no roles, and no server.
The app is free, but every text and image run bills your own provider card at their standard rates, so a long comparison session across OpenAI or Anthropic adds up on your provider invoice, not in the app.
There is one price: $0 under the MIT License, with all 11 providers unlocked and no seat count, no usage cap, and no upgrade tier. Your real cost is whatever OpenAI, Anthropic, Gemini, Runware, and the rest bill your own keys — typically cents per comparison run on small models. That undercuts paid cloud aggregators and hosted playgrounds with per-seat fees, but it also means no support contract, no SLA, and no team billing to consolidate: each teammate pays providers separately.
In short
AI Playground — Free, MIT-licensed desktop app for running text and image prompts across 11 AI providers side by side. Best for Developers comparing model answers, latency, and cost side by side, Privacy-conscious users who want keys, prompts, and history in a local SQLite file, Ollama users who want a cleaner front end and $0.00 cost tracking. Free to use.
What's new in AI Playground
Checked todayAcross the latest 5 updates: 3 feature updates and 2 changelog entries.
v1.2.5 — Syntax-highlighted code blocks, collapsible thinking tags, edit & regenerate, conversation pinning and export
Adds syntax-highlighted code blocks with one-click copy, expandable thinking/reasoning sections, inline message editing and regenerate, a context-window usage bar, conversation pinning, in-conversation message search, and Markdown/JSON/plain-text conversation export.
v1.2.4 — Telemetry migrated to PostHog with crash-resilient background tracking
Replaces the manual offline telemetry queue with PostHog's native Node SDK, making background tracking crash-resilient and bypassing Electron network-stack restrictions; also adds strict web-crawler filtering on the backend.
v1.2.3 — Model cache versioning and improved AI/ML API provider parsing
Provider model caches now carry a version number and invalidate on app update, AI/ML model type detection was improved across 400+ models, and empty model lists are no longer cached after a failed fetch.
v1.2.0 — Three new providers: AI/ML, NVIDIA, and Hugging Face
Adds AI/ML (100+ models with image generation), NVIDIA (55 enterprise inference models), and Hugging Face (52 open models via router API), plus a redesigned provider-health grid, a Hugging Face routing-suffix toggle, a vision-only image-attach zone, and full 6-language internatio
v1.1.0 — Persistent multi-turn chat and sidebar conversation management
Chat history now persists across sessions via local SQLite for OpenAI, Claude, Gemini, and Grok, with a sidebar for renaming and jumping between threads, plus a unified 'base-nova' dark theme.
What people actually say about AI Playground — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
97 mentions across 6 sources (Hacker News, Product Hunt, Bluesky, Stack Overflow, GitHub, Lemmy) · researched Jul 16, 2026.
Average across the 6 sources that answered — each source counts once, not each post.
- +Free and open-source with no usage limits or subscriptions.
- +Runs fully offline, ensuring data privacy and low latency.
- +Supports multiple LLMs (Llama 3, Mistral, Gemma) side-by-side.
- +Adjustable parameters like temperature and top-p for fine-grained control.
- +Works on consumer hardware via quantized models.
- −Frequent installation failures and hangs on Windows.
- −Blocked on Windows systems with admin rights.
- −Model download confirmation button often unresponsive.
- −Performance can degrade dramatically over time.
- −No cloud-hosted or managed version for quick testing.
- • No hidden costs, but requires significant RAM/disk space and manual model downloads
Viability Score
How well maintained and how widely used is AI Playground? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Side-by-side comparison of one prompt across up to 3 models in parallel columns (text and image)
- Live streaming with per-run token count, latency, and cost estimate
- Image generation via Runware, Cloudflare, or OpenAI with 0.5K-2K resolution and negative prompts
- Advanced image controls: steps, CFG scale, and seed
- 11 provider integrations: OpenAI, Gemini, Anthropic, OpenRouter, Runware, Cloudflare, xAI Grok, Ollama, AI/ML, NVIDIA, Hugging Face
- Local model inference through Ollama at $0.00 with no API key required
- Cumulative spend chart and provider-health panel with branded monograms
- Live pricing catalog fetched from OpenRouter and Runware, filterable by provider and sortable by cost
- Persistent multi-turn chat with sidebar management, conversation pinning, and in-conversation message search
- Edit-and-regenerate: edit a user message inline and re-send, or regenerate the last assistant response
- Collapsible thinking/reasoning tags rendered as an expandable 'Thinking...' section
- Syntax-highlighted code blocks with language labels and one-click copy
- Context window usage bar in the Chat settings panel
- Taggable prompt library with in-place editing and clipboard copy
- Raw JSON mode, system prompt support, and configurable temperature/top-p/frequency penalty/max tokens
About AI Playground
AI Playground is a free desktop app (Windows, macOS, Linux) built on Electron and Next.js that puts 11 AI providers — OpenAI, Gemini, Anthropic, OpenRouter, Runware, Cloudflare, xAI Grok, Ollama, AI/ML, NVIDIA, and Hugging Face — behind one local interface. You bring your own API keys, or point it at a local Ollama install and pay nothing at all; there is no account, email, or license key, and no vendor backend. The signature feature is side-by-side comparison: run the same prompt across up to three models at once, watch results stream into parallel columns, and add or drop model columns on the fly for both text and image generation. Every run shows token count, latency, and a cost estimate, with a cumulative spend chart and a provider-health grid behind it. Image generation runs through Runware, Cloudflare, or OpenAI with aspect ratio, 0.5K-2K resolution, negative prompts, and per-image cost shown per provider. v1.1.0 added persistent multi-turn chat with a sidebar of saved conversations, rename and pin controls, and message search. Around that sit the power-user pieces: a taggable prompt library, raw JSON mode, system prompt support, configurable temperature/top-p/frequency penalty/max tokens, advanced image controls (steps, CFG scale, seed), one-click connection tests per provider, and export of run history and conversations as CSV, Markdown, JSON, or plain text. Keys, prompts, history, and metrics live in a local SQLite file you can open yourself. The UI is translated into six languages, and it works offline except for the API call itself.
Behind the Verdict
The thing AI Playground gets right is scope discipline. It is not trying to be an agent framework or a hosted aggregator; it is a lab bench. Eleven providers — OpenAI, Gemini, Anthropic, OpenRouter, Runware, Cloudflare, xAI Grok, Ollama, AI/ML, NVIDIA, Hugging Face — share one provider selector, one model selector, and one run-history table, so the comparison loop ('same prompt, three columns, which is cheaper and faster') takes seconds instead of three browser tabs and a spreadsheet. The dashboard's provider-health grid, one-click connection tests with latency readouts, and the cumulative spend chart are the kind of instrumentation most free tools skip entirely. Data handling is the second strength. Keys, prompts, chat history, and metrics live in a local SQLite database with no vendor backend, and the app explicitly documents that keys go only to the provider you call. For anyone testing prompts against sensitive inputs, that is a meaningful difference from a browser playground. The changelog shows the product is genuinely maintained: v1.2.0 shipped three providers at once (AI/ML, NVIDIA, Hugging Face), a routing-suffix toggle for Hugging Face, a vision-only image-attach zone, and full i18n across 487 keys per locale; v1.2.5 added syntax-highlighted code blocks, collapsible thinking/reasoning tags, edit-and-regenerate, and a context-window usage bar. The honest weaknesses are structural rather than bug-shaped. Comma, this is a single-developer project — the changelog fixes are real but release cadence and bus factor are not enterprise-grade. There is no browser version, so locked-down machines that forbid desktop installs are out. There is no shared workspace, role model, or hosted URL to hand a colleague; collaboration means exporting a conversation as Markdown. And the 'works offline' framing needs a caveat: the app runs offline, but text and image generation still require calls to external providers unless you are on Ollama. Image generation is narrower than text — Runware, Cloudflare, or OpenAI only, capped at 2K. Where it fits: individual developers, prompt engineers, and researchers who already hold a few keys, want spend visibility, and value a permanent searchable run history over a shared workspace. Where it does not: teams needing hosted access or role controls, non-technical users unwilling to create per-provider accounts, and production pipelines that need an API endpoint rather than a visual testing app.
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Real-world workflow fit
Concrete scenarios for the personas AI Playground actually fits — and what changes day-one when you adopt it.
After installing the app and pasting in an OpenAI, Gemini, and Anthropic key, they run the same support-reply prompt across all three columns, watch latency and token counters stream in, and check the per-run cost estimate next to each output.
Outcome: They pick the model with the best quality-to-cost ratio for that task, and the run sits in searchable local history so they can re-compare the same prompt after a model update.
They point AI Playground at a local Ollama install, no API key needed, and run three local models side by side on internal documents, confirming on the dashboard that spend stays at $0.00.
Outcome: They get model comparison and a stored prompt library without sending the source text to a hosted service.
They adjust temperature, top-p, frequency penalty, and system prompt in the Text Playground, re-run across models, use the context-window usage bar to watch token consumption, and save the winning prompt to the tagged library.
Outcome: They end up with a reusable, tagged prompt set and a Markdown export of the runs to share with the team.
Use Cases
- Compare response quality and cost of the same prompt across up to three models in parallel columns
- Iterate on prompt engineering privately, keeping every attempt and its token cost in local history
- Run models through Ollama for $0.00 when inputs cannot leave your machine
- Estimate per-image cost across Runware, Cloudflare, and OpenAI before committing to a provider
- Prototype an AI feature against several providers before choosing one for production
- Teach how LLMs respond differently, using the side-by-side view and live token counters
- Track cumulative spend across several API keys on one dashboard chart
- Search and reuse past conversations and tagged prompts instead of rewriting them
Models Under the Hood
as of 2026-09-09
Limitations
- Text and image generation still require external API calls to providers such as OpenAI, Gemini, Anthropic, or Ollama — the app runs offline, but the model does not, unless you are on local Ollama.
- Image generation is limited to Runware, Cloudflare, or OpenAI and capped at 2K resolution, while text runs are broader across all 11 providers.
- Model and feature availability fluctuate with each provider's API, and the live pricing catalog depends on OpenRouter and Runware, so it can go stale for providers those catalogs do not cover.
- There is no browser version, so environments that forbid desktop installs are excluded, and there is no shared workspace, role model, or hosted URL.
- Comparison is capped at three model columns at a time.
- Vision input appears only for providers that accept images.
as of 2026-09-14
Verification history
We have re-verified AI Playground 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published AI Playground tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free (MIT License)
$0
Ideal for
Individual developers, prompt engineers, and hobbyists who already hold provider API keys or run local Ollama models and want comparison and spend tracking without a subscription.
What this tier adds
Starting tier and only tier: $0, all 11 provider integrations unlocked, unlimited runs billed by your own keys, no account or license key required.
Where the pricing makes sense
The company stage and team size where AI Playground's pricing actually pencils out — and where peers do it cheaper.
There is one price: $0 under the MIT License, with all 11 providers unlocked and no seat count, no usage cap, and no upgrade tier. Your real cost is whatever OpenAI, Anthropic, Gemini, Runware, and the rest bill your own keys — typically cents per comparison run on small models. That undercuts paid cloud aggregators and hosted playgrounds with per-seat fees, but it also means no support contract, no SLA, and no team billing to consolidate: each teammate pays providers separately.
Setup time & first value
How long it actually takes to get something useful out of AI Playground — broken out by persona, not the marketing-page minute.
Download-to-first-prompt is roughly 10-15 minutes: grab the installer for your OS, unzip and run with no account, email, or license key, then paste keys and hit Test on each provider you use. Ollama needs no key, so a fully local setup is closer to 5 minutes if Ollama is already installed. Teams standardizing on multiple providers should budget 20-30 minutes to collect and verify every key.
Switching to or from AI Playground
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From OpenAI / Anthropic / Gemini browser playgrounds: paste your existing keys into the API Keys page and hit Test, then re-run your saved prompts in the Compare view.
- →From Ollama CLI: keep your local models as-is, add Ollama as a provider (no key required), and move recurring prompts into the tagged prompt library.
- →From OpenRouter or a cloud aggregator: add your OpenRouter key to reach its model catalog inside the app while keeping other providers side by side.
- →From a spreadsheet of manual model tests: import prompt text into the library and let run history, token counts, and cost estimates replace the manual logging.
- ↗To OpenRouter or LiteLLM: export conversations and run history as JSON or CSV, then rebuild the same prompts against a programmatic gateway endpoint.
- ↗To a hosted team playground: export conversations as Markdown and re-upload, since AI Playground has no shared workspace or login to transfer.
- ↗To a production API integration: use the exported JSON run history to document which provider and parameters you settled on before wiring the app's logic into code.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “AI Playground”, and we withheld 6: 6 could not be judged, because “AI Playground” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about AI Playground.
Official links
Tools that pair well with AI Playground
Common stack mates teams adopt alongside AI Playground, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Ai Playground vs Bito
Pick Bito if your team uses AI coding agents (Cursor, Claude Code, Codex) and struggles with cross-repo dependencies, architectural planning, or onboarding. Choose AI Playground if you're a solo developer or researcher who wants a free, private, multi-model comparison lab—just bring your own API keys. They serve completely different needs: Bito is an enterprise context layer; AI Playground is a local testing sandbox.
Ai Playground vs Poolside Ai
If you're a developer or researcher comparing models without spending a dime, AI Playground is the obvious choice—it's free, local, and supports side-by-side testing. But if you're an enterprise handling sensitive code in finance, healthcare, or defense and need auditable AI agents with on-prem deployment, Poolside AI's Laguna models and governance features are unmatched. There's no overlap: AI Playground is for exploration, Poolside is for production in high-stakes environments.
Ai Playground vs Cognition Ai
If you manage a large production codebase and need an autonomous engineer that plans, codes, tests, and ships — with a multimillion-dollar productivity guarantee — Cognition AI's Devin is unmatched. If you're a developer or researcher comparing LLM outputs across providers in a privacy-first local app, AI Playground is the free, powerful choice. These tools serve fundamentally different needs; the right pick depends on whether you're shipping software or evaluating models.
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Frequently Asked Questions
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