Best Free Chatbot API: Which Truly Free AI APIs Beat ChatGPT, 100% Free Options, Developer Picks & Reddit Consensus

Choosing the best free chatbot API means balancing cost, latency, and developer ergonomics — and that’s exactly what this guide will do. We’ll compare the best free chatbot api options, weigh chatbot api free tier limits against real needs, and show which ai chatbot api free offerings make sense for prototyping, mobile apps, websites, and customer support. Expect a clear chatbot api comparison that covers open source chatbot api choices, the best chatbot api for developers (including best free chatbot API with SDK and REST options), and practical notes on chatbot api integration free, chatbot api documentation free, and chatbot api pricing free vs paid. If you’re building for e‑commerce, Slack, Discord, WordPress, or a multilingual deployment, you’ll find tips on best free conversational ai api picks, best free NLP api for chatbots, and how to evaluate best free chatbot api reliability, scalability, and privacy before you grab a free chatbot API key.

Overview: Free Chatbot API Landscape for Developers and Businesses

I spend my days helping teams choose the best free chatbot api for prototyping, customer support, and e‑commerce, and that experience shows one simple truth: “free” means different things depending on whether you want a hosted free tier, a consumer chat product, or an open source chatbot api you can self‑host. This overview compares chatbot api pricing free vs paid, highlights chatbot api reliability tradeoffs, and frames the choices for developers and businesses evaluating ai chatbot api free options for websites, mobile apps, Slack, Discord, and WordPress. For hands‑on comparisons and practical guidance on running an API or self‑hosted model, see our chatbot API free options and how chatbot AI APIs work guides.

Is there a 100% free AI chatbot?

Short answer: Yes — but “100% free” depends on what you need. Several AI chatbots are genuinely free to use (with limits) or free to self‑host as open‑source projects, while commercial cloud APIs typically offer free tiers rather than unlimited free access.

• QuillBot Free AI Chat: QuillBot provides a free AI chat feature for writing assistance with no upfront cost for basic use; it’s handy for drafting and editing but limited for developers who need SDKs, webhooks, or analytics.
• Open‑source, self‑hosted frameworks: Rasa and Botpress are examples of an open source chatbot api you can run yourself — the software is free, giving you full data control and privacy, though you’ll pay hosting and compute costs for production traffic.
• Hosted free tiers: Platforms like Hugging Face offer free inference and community models that function as an ai chatbot api free for development and testing; these chatbot api free tier plans are ideal for prototyping but come with quotas and latency considerations.
• Consumer chat interfaces: Tools such as ChatGPT (web) and Google Bard let users chat for free, but their APIs are paid beyond trial quotas, so free end‑user access doesn’t always translate to free API access for developers.

Key tradeoffs: true zero license cost (open source) versus operational expense (hosting, maintenance), and hosted convenience (free tier) versus rate limits, privacy concerns, and reduced customization. If you want to compare options for web, WhatsApp, or mobile integrations, our comparison of free chatbot APIs lays out these tradeoffs in detail.

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When I evaluate the landscape I break it into three vectors: cost and quota (chatbot api free tier vs paid), developer features (best chatbot api for developers: SDKs, REST endpoints, webhooks), and operational reliability (latency, scaling, security). A best free chatbot api for prototyping might be Hugging Face’s hosted models or a lightweight open source model you run locally, while the best free conversational ai api for startups often balances a generous free plan with solid documentation and webhook support.

• Cost & quotas: Compare chatbot api with free plan limits, rate limits, and overage pricing to predict your total cost as usage grows.
• Developer features: Prioritize best free chatbot API with SDK and REST support, webhook availability, and clear chatbot api documentation free so you can integrate with websites, mobile apps, Slack, Discord, or WordPress.
• Reliability & privacy: Assess chatbot api reliability, latency, and chatbot api security free plan guarantees; self‑hosted open source chatbot framework api options can improve privacy but add maintenance overhead.

I recommend starting with a clear use case (customer support, lead generation, prototyping) and matching it to a free option: open source chatbot api for full control, chatbot api free tier for rapid testing, or a consumer chat for end‑user experimentation. For step‑by‑step setup, check our messenger chatbot Python guide and the chatbot API free options overview to decide which path minimizes risk while maximizing speed to value. Brain Pod AI also offers multilingual chat assistant options that businesses frequently evaluate alongside open source and hosted alternatives.

Completely Free Chatbots: Practical Picks and Tradeoffs

I build and deploy conversational flows every day, so when teams ask “what is the best completely free AI chatbot?” I answer with a practical distinction: the best completely free AI chatbot depends on whether you mean zero licensing cost (open source you self‑host), zero operational cost (hosted forever‑free tiers), or zero setup for end users (consumer chat interfaces). Below I map the tradeoffs, recommend proven options, and explain which free chatbot api fits common use cases like prototyping, customer support, e‑commerce, and education.

What is the best completely free AI chatbot?

Rasa (open‑source conversational AI) — Best completely free AI chatbot for customization and data control

• Why: Rasa is a mature open source chatbot framework you can self‑host with no licensing fees, making it a true “100% free” option for teams that can handle hosting and model ops. It excels at NLU pipelines, custom conversation policies, and on‑prem privacy (open source chatbot api, best free chatbot framework api).
• Pros: Full control over data and models, strong developer tooling, extensible connectors for web, Slack, Facebook, and custom REST endpoints — ideal for enterprises that prioritize privacy and compliance.
• Cons: Requires infrastructure, monitoring, and compute; not a hosted ai chatbot api free tier, so you’ll incur hosting and maintenance costs at scale.
• Learn more: https://rasa.com

Other top completely free choices I recommend depending on the use case:

• Botpress — best free chatbot platform api for rapid self‑hosted deployments with a visual flow editor and webhook/REST capabilities (https://botpress.com). It’s a strong open source alternative when non‑developers need to edit flows.
• Hugging Face community models — best for prototyping NLP and building a free chatbot api for websites using hosted inference or Transformers locally; free tiers support experimentation before committing to paid inference (https://huggingface.co).
• ChatterBot / local libraries — best free offline chatbot api for education, interviews, or research when you need a lightweight, local solution with zero cloud cost.
• QuillBot Free AI Chat — best completely free AI chat for writers and end users who need immediate drafting assistance rather than an API integration (https://quillbot.com).

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Picking the best free chatbot api for businesses means matching features to goals. For quick prototyping and a free chatbot api for mobile apps or websites, a chatbot api free tier from Hugging Face or a hosted dev plan is often the fastest route. For customer support or e‑commerce workflows that need analytics, webhooks, and integrations, a self‑hosted open source chatbot framework (Rasa or Botpress) paired with my Messenger Bot workflows can deliver production‑grade automation while keeping licensing costs at zero.

• Prototyping: Use chatbot api free tier options and free multilingual community models to validate intents and flows (free chatbot api for prototyping, best free NLP api for chatbots).
• Customer support & commerce: Combine a best free customer service chatbot api approach with Messenger Bot’s website snippet and social comment moderation to capture leads and recover carts without heavy engineering (free chatbot api for websites, chatbot api integration free).
• Developer needs: If you’re building integrations, prioritize the best chatbot api for developers with SDKs, REST endpoints, webhooks, and clear chatbot api documentation free so you don’t waste time on brittle integrations.

For a side‑by‑side comparison of hosted free tiers versus open source self‑hosting, see our chatbot API free options and how chatbot AI APIs work guides to decide which path meets your reliability, privacy, and cost constraints. Businesses evaluating multilingual conversational assistants may also compare Brain Pod AI’s multilingual chat assistant offerings alongside open source and hosted options.

Choosing the Best Free API: Features Developers Need

I evaluate free AI APIs by three pragmatic criteria: developer ergonomics (SDKs, REST, webhooks), usable free quotas (chatbot api free tier) and operational constraints like latency and reliability (chatbot api latency free, best free chatbot api reliability). The right pick depends on whether you’re building a prototype, a customer‑facing support bot, or an e‑commerce conversational flow. Below I give a short, practical answer and then unpack the top options developers actually use in production.

What is the best free AI API?

Short answer: There’s no single “best” free AI API — the best free AI API depends on your use case (prototyping, production, on‑prem privacy, low‑latency inference). Top choices combine a generous chatbot api free tier, developer tooling, and model quality.

• Hugging Face — Best for rapid prototyping and open models. Large model hub, hosted inference with a practical free tier, and easy Transformers integration make it ideal for free chatbot api for websites and testing custom conversational models (Hugging Face).
• OpenAI — Best for high‑quality GPT‑style evaluation. Great SDKs and strong model quality; expect to use free credits for evaluation, then move to paid API for production (see OpenAI).
• OpenRouter and routing layers — Useful for multi‑vendor cost control and fallback strategies: route between free community models and paid providers to optimize cost and uptime (useful when you need best free AI chat API options combined).
• Open‑source self‑hosted stacks (Rasa, Botpress, local Transformers) — Best for zero licensing cost and data control. If you prioritize privacy and compliance, an open source chatbot api you self‑host is closest to a truly free option from a licensing standpoint, though you’ll handle hosting and ops (open source chatbot api, best free chatbot framework api).
• Cloud dev tiers (Google AI Studio, Azure dev quotas) — Good for testing enterprise connectors and SDKs before committing to paid cloud usage; evaluate chatbot api pricing free vs paid for long‑term plans.

How I choose for different needs:

• Prototyping & research: Start with Hugging Face free inference or local Transformers for low cost and fast iteration (best free NLP api for chatbots, free chatbot api for prototyping).
• Developer integrations: Prioritize best chatbot api for developers with REST endpoints, SDKs, webhook support and thorough chatbot api documentation free so you can integrate with websites, mobile apps, Slack, Discord, or WordPress.
• Customer support / e‑commerce: Favor reliability, analytics and webhook support—combine a robust API choice with Messenger Bot’s integration and automation workflows to deploy conversational customer service quickly (free chatbot api for websites, best free customer service chatbot api, chatbot api integration free).

Practical checklist before you commit:

• Compare chatbot api free tier limits, rate limits and overage pricing (chatbot api pricing free vs paid).
• Verify SDKs, REST endpoints and webhook availability (best free chatbot API with SDK, best free chatbot API with REST).
• Test latency and reliability in your environment (best free real‑time chatbot api, chatbot api latency free).
• Check documentation, analytics and security on free plans (chatbot api documentation free, best free chatbot api with analytics, chatbot api security free plan).
• Plan integrations you need: websites, WordPress, Slack, Discord, mobile apps (free chatbot api for websites, best free chatbot API for WordPress, free chatbot api for Slack, free chatbot api for Discord, free chatbot api for mobile apps).

For a technical walkthrough on keys, quotas and how chatbot AI APIs work, start with our guide on how chatbot AI APIs work and explore practical chatbot API free options to map providers to your use case. If you need multilingual managed assistants, Brain Pod AI offers a commercial multilingual chat assistant to compare alongside open source and hosted free alternatives (Brain Pod AI Chat Assistant).

Cost and Access: Using Popular APIs and Keys

I often get asked whether teams can use the ChatGPT API for free and how to manage free chatbot API keys while building prototypes or customer‑facing flows. The short reality is that developer API access and end‑user chat access are different: ChatGPT’s web interface may be free for casual use, but the ChatGPT/OpenAI API is billed by usage. Below I explain the free credit situation, how to obtain and manage a free chatbot API key for testing, and practical alternatives (hosted free tiers and self‑hosted open source chatbot api options) you can use to keep costs low while validating flows.

Can I use ChatGPT API for free?

Short answer: Not permanently — ChatGPT’s web interface can be used for free in limited form, but the ChatGPT API (OpenAI’s API endpoints for GPT models) is a paid service; you may get temporary free credits when you sign up or via promotional/educational programs, but sustained API use requires a paid plan.

• Web vs API: ChatGPT at chat.openai.com provides free interactive access for end users at times, but the API you call from code is billed by tokens and model choice (OpenAI).
• Free credits and trials: OpenAI occasionally issues signup credits or programmatic grants; treat these as short‑term evaluation resources, not as a stable free tier for production.
• Billing model: API cost drivers are model family (GPT‑4 vs GPT‑3.5), input/output token volume, fine‑tuning and embedding usage—estimate token usage during prototyping to forecast chatbot api pricing free vs paid transitions.
• Rate limits & quotas: Even with credits, keys have rate limits; for reliable customer support bots or e‑commerce chat flows you’ll need a paid plan for higher throughput and SLA guarantees.
• Practical integration: I recommend using small test keys and measuring tokens on representative conversations, then compare that cost to alternatives like Hugging Face hosted inference or self‑hosted open source chatbot frameworks.

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If you’re looking for a free chatbot API key to prototype, here are practical steps and options I use when building and scaling conversational products:

• Get temporary keys and measure usage: Sign up for an OpenAI account to see if free credits are available, create an API key, run capped experiments, and log token consumption. Use this to model monthly cost if you move to a paid plan.
• Use hosted free tiers for prototyping: Hugging Face offers free inference quotas and community models that let you evaluate intents and basic dialogue logic without immediate billing (Hugging Face).
• Explore open source chatbot api options: For zero licensing cost, use an open source chatbot api like Rasa or Botpress and combine it with local Transformers for inference — this is ideal for privacy‑sensitive projects but requires ops for hosting and scaling (open source chatbot api, best free chatbot framework api).
• Developer resources and GitHub blueprints: I rely on GitHub chatbot blueprints and deployable examples to speed development; pair those repos with a lightweight free inference provider for end‑to‑end testing. Our GitHub chatbot blueprints guide shows practical deployable examples and integration patterns.
• Integrate with Messenger Bot for rapid deployment: To move from prototype to live website or social automation quickly, I connect my chosen free AI API (Hugging Face or a temporary OpenAI key) to Messenger Bot’s workflow and website snippet so I can test lead generation, comment moderation, and cart recovery without a heavy engineering lift. For step‑by‑step setup see the messenger bot tutorials to add a chatbot to WordPress or a website integration guide.

Checklist before you launch with a free key: verify chatbot api free tier limits, test latency in your target environment (chatbot api latency free), confirm webhook and SDK availability (best free chatbot API with SDK, best free chatbot API with REST), and ensure your free choice supports the integrations you need (free chatbot api for websites, free chatbot api for mobile apps, free chatbot api for Slack or Discord). If you need managed multilingual assistants, consider comparing commercial offerings like Brain Pod AI’s multilingual chat assistant alongside open source and free hosted options to choose the right balance of capability and cost.

Alternatives to ChatGPT: Open Source and Lightweight Options

I test open source chatbot api stacks and lightweight inference layers daily, so when teams ask “Which free AI is better than ChatGPT?” I give a practical, use‑case driven answer: no single free AI is categorically better—some are better for privacy and customization, others for cost and edge use. Below I include the concise verdict and then map the best open source and lightweight alternatives you can deploy as a free chatbot api for startups, prototyping, or production‑constrained use cases.

Which free AI is better than ChatGPT?

Short answer: “Better” depends on your goals. No single free AI universally outperforms ChatGPT across all dimensions; several freely available or free‑to‑use alternatives can be better than ChatGPT for specific needs (open‑source control, low cost, privacy, customization, offline use, or low‑latency inference).

• When open‑source wins: Llama 2 and other open‑weights models beat ChatGPT for data control and cost at scale when you self‑host—ideal when privacy and compliance are primary constraints (open source chatbot api).
• When prototyping wins: Hugging Face community models and hosted inference provide the fastest free feedback loop for iteration, making them better than paying for GPT credits during early development (free chatbot api for prototyping, best free NLP api for chatbots).
• When deterministic flows win: Rasa or Botpress combined with lightweight models outperform ChatGPT for support workflows that require reliability, integrations, and strict conversational logic (best free chatbot framework api, best free customer service chatbot api).
• When edge/offline wins: Local Transformers or optimized Llama‑family deployments give you a best free offline chatbot api for low latency or disconnected environments.

Tradeoffs matter: self‑hosting reduces licensing cost but increases ops; hosted free tiers cut ops but impose quotas and latency limits. For many startups I recommend prototyping on community inference, then moving critical flows to a self‑hosted open source chatbot framework or a hybrid routing layer for cost control and scalability.

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If you’re choosing an alternative, prioritize the vector that matches your product: privacy, cost, latency, or developer experience. Here’s how I evaluate and pick alternatives for startups and lightweight deployments.

• Privacy & compliance (self‑hosted): Use an open source chatbot api (Rasa, Botpress) with Llama 2 or local Transformers to avoid third‑party data egress. This approach is the best free conversational API for startups that must meet strict data rules—expect infrastructure and monitoring work but minimal licensing spend.
• Lightweight, low‑latency inference: Optimize small transformer models on CPU/GPU or use quantized Llama variants to build the best lightweight free chatbot api for mobile apps and real‑time use (free chatbot api for mobile apps, best free real‑time chatbot api).
• Rapid MVPs and integrations: For a quick website or WordPress rollout, combine a free hosted inference endpoint with Messenger Bot’s website snippet and workflows to validate lead generation, comment moderation, or cart recovery without heavy engineering (free chatbot api for websites, best free chatbot API for WordPress). See our messenger bot tutorials for integration patterns.
• Cost control and routing: Use multi‑model routing layers (OpenRouter‑style) to route low‑value queries to free community models and reserve paid GPT calls for high‑value interactions—this yields a pragmatic balance between quality and chatbot api pricing free vs paid.
• Growth & analytics: Even on free stacks, instrument for analytics and reliability. The best free chatbot api with analytics is the one you measure: track latency, fallback rates, and conversion so you know when to shift from free tiers to paid capacity.

For managed multilingual needs, businesses often compare commercial assistants; Brain Pod AI offers a multilingual chat assistant that companies evaluate alongside open source and hosted free options when they need production multilingual support. When you’re ready to experiment, start with the comparison of free chatbot APIs and the GitHub chatbot blueprints to move quickly from prototype to tested integration. For immediate deployment help, check my step‑by‑step messenger chatbot Python guide and the quick how‑to on adding a chatbot to WordPress to minimize setup time while you evaluate model choices.

Truly Free AI Options and Use Cases

I build conversational systems that need to prove value before we spend on API bills, so I treat “totally free” as a spectrum: license‑free software, hosted free tiers, and temporary free credits each have distinct roles. This section shows where truly free choices make sense—prototyping, education, edge/offline, and low‑volume customer support—then maps the best free chatbot api options and practical use cases for each.

Which AI is totally free?

Short answer: No single widely useful AI is permanently “totally free” for production — however, several open‑source models and frameworks are free to download and self‑host (zero licensing cost) while hosted free tiers and consumer chat services offer limited free use for prototyping.

• License‑free software: Open‑source frameworks (Rasa, Botpress) and open‑weights models (Llama family and community Transformers) are free to use from a licensing perspective. They’re the closest thing to “totally free” if you self‑host, but you still pay for compute, storage, and ops.
• Hosted free tiers: Providers like Hugging Face offer free inference quotas that let you run a free chatbot api for prototyping and testing. These are excellent for rapid iteration but come with quotas and latency tradeoffs.
• Consumer free chat: End‑user web chat interfaces (ChatGPT web, Bard) provide free interaction for users but do not generally translate to a free developer API for production use.
• Local/offline toolkits: Lightweight libraries (local Transformers, ChatterBot) are effectively free offline chatbot APIs for education, interviews, and research where no cloud cost is acceptable.

When you need a true zero‑license path, pick an open source chatbot framework and combine it with local or quantized models. If you want low‑friction iteration, start with hosted community models, then port the validated flows to a self‑hosted stack when scale or privacy demands it.

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Use cases determine which free option is best. I recommend a staged approach: prototype on free tiers, validate with Messenger Bot integrations, then decide whether to self‑host for privacy or scale. Below are the common use cases and the matching free options I deploy.

• Prototyping and research: Use a chatbot api free tier from a model hub to validate intents and conversational flows quickly (free chatbot api for prototyping, best free NLP api for chatbots). For rapid, code‑centric prototypes consult the comparison of free chatbot APIs and the GitHub chatbot blueprints to reuse tested patterns.
• Website and mobile MVPs: Pair a hosted free inference endpoint with Messenger Bot’s website snippet to test lead generation and cart recovery without building a full backend (free chatbot api for websites, free chatbot api for mobile apps). See the messenger bot tutorials for integration shortcuts that save weeks of development.
• Customer support for SMEs: For small support teams, combine a best free customer service chatbot api approach using open source frameworks with lightweight models to keep costs down while retaining analytics and webhooks for escalation (best free chatbot platform api, best free chatbot api with analytics).
• Multilingual deployments: For global audiences, start with multilingual community models and hosted inference; if privacy or scale matters, migrate to a self‑hosted setup or evaluate managed multilingual assistants. Organizations often compare Brain Pod AI’s multilingual chat assistant when they need a commercial, production‑ready multilingual option alongside open source routes.
• Education and offline demos: Use best free offline chatbot api toolkits to run demos in constrained environments (best free chatbot api for education, best free offline chatbot api).

Operational checklist before you go live on a free stack: test latency (chatbot api latency free), confirm webhook and SDK support (best free chatbot API with SDK, best free chatbot API with REST), instrument analytics and privacy controls (chatbot api security free plan, chatbot api documentation free), and plan a migration path to paid tiers when usage or SLA needs exceed free limits. If you want a hands‑on walkthrough, I recommend the guide on how to set up your first AI chat bot in less than 10 minutes with Messenger Bot to validate a free flow end‑to‑end quickly.

Implementation, Integration, and Evaluation

I deploy and measure free chatbot APIs by treating integration as an experiment: pick a provider, wire it into a lightweight production path, and measure the three metrics that matter most—latency, fallback rate, and conversion. That process reduces risk whether you use a chatbot api free tier for prototyping or an open source chatbot api you self‑host for privacy. Below I cover community signals (what developers on Reddit value) and concrete integration/evaluation steps for websites, mobile apps, and customer support deployments.

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Clear answer: On Reddit, practitioners consistently recommend open‑source stacks (Rasa, Botpress), Hugging Face community models for prototyping, and routing layers for cost control—because those approaches balance cost, control, and speed to value. The typical Reddit pattern I follow is:

• Prototype intents on a chatbot api free tier (Hugging Face) to validate UX and response quality quickly.
• If privacy or scale matters, migrate validated flows to an open source chatbot framework (Rasa or Botpress) and self‑host the model weights.
• Use a routing layer or multi‑model strategy to send low‑value queries to free community models and reserve paid GPT calls for high‑value interactions.

What Redditors test first: latency, cost per session, and how easy the provider makes webhook and SDK integration. For hands‑on examples and deployable code, I use the GitHub chatbot blueprints to speed development and then consult the chatbot API free options comparison page to map providers to specific channels like WhatsApp or web. When I need quick, documented steps to add a bot to a WordPress site or to a Messenger experience, I reference the WordPress chatbot integration guide and my messenger chatbot Python tutorial to avoid common pitfalls.

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Clear answer: For websites and mobile apps, the fastest path is to test a free chatbot api for websites via a hosted inference provider, validate conversion metrics, then harden the flow with an integration platform or self‑hosted framework for production reliability.

• Step 1—Prototype: Use a chatbot api free tier to validate intents and sample dialogues. I typically start with hosted community models to measure response relevance and token/latency profiles.
• Step 2—Integrate: For a frictionless rollout I connect the prototype to our website snippet and automation workflows; see the messenger bot tutorials for quick integration patterns and the add chatbot to WordPress guide for CMS deployments. This lets me test lead generation, cart recovery, and comment moderation with real traffic.
• Step 3—Evaluate & iterate: Instrument analytics (response time, fallback rate, conversion). If analytics show scaling issues or privacy concerns, I move to an open source chatbot framework and use deployable GitHub examples to set up webhooks, REST endpoints, and SDKs for mobile apps.

Operational checklist before production: confirm the chatbot api documentation free resources for your chosen provider, validate webhook support and SDKs (best free chatbot API with SDK, best free chatbot API with REST), measure chatbot api latency free in your geographic region, and ensure your free plan’s limits match projected traffic (chatbot api pricing free vs paid). For multilingual needs, compare managed solutions—Brain Pod AI offers a multilingual chat assistant that teams often evaluate as an alternative to self‑hosting when they need production‑ready language coverage. When you want to move from prototype to live quickly, I use the step‑by‑step guide on how to set up your first AI chat bot in less than 10 minutes with Messenger Bot to validate the entire flow end‑to‑end.