Use case

LLM API for Chatbots and Conversational AI

Power your chatbot with the right model for every conversation, streamed responses for instant feel, and failover that keeps the chat flowing.

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You only pay credits per request. No monthly subscription. Paid credits never expire.

Replace multiple AI subscriptions with one wallet that includes routing, failover, and optimization.

First success in 60 seconds
Step 01Sign up in 10 secondsGet 40 free creditsStep 02Open your dashboardCreate API keyStep 03Send first requestRun a sample
Why teams start here first
No monthly subscription
Pay-as-you-go credits
Start with trial credits, then buy only what you consume.
Failover safety
Production-ready routing
Auto fallback across providers when latency, quality, or reliability changes.
Data control
Your policy, your choice
BYOK and zero-retention mode keep training and storage scope explicit.
Single API experience
One key, multi-provider access
Use Chat/Compare/Blend/Judge/Failover from one dashboard.
Common problem
Chatbot users expect instant, high-quality responses, but a single LLM cannot excel at every conversation type: some need deep reasoning, others need speed, and others need creative flair.
Common problem
Provider outages during live conversations destroy user trust and are difficult to recover from because users are mid-session and expect continuity.
Common problem
High-volume chatbots accumulate large token bills quickly, and using a frontier model for every message wastes budget on simple queries that a cheaper model handles equally well.

How LLMWise helps

✓Auto mode routes each message to the optimal model: GPT-5.2 for complex reasoning, Claude Sonnet 4.5 for nuanced conversation, Gemini 3 Flash for fast simple responses, all transparent to the user.
✓Streaming Server-Sent Events deliver token-by-token output for a responsive chat experience with visible time-to-first-token under 300 milliseconds on most models.
✓Mesh failover ensures the chatbot stays responsive even during provider outages by automatically routing to a fallback model mid-conversation with no user-visible interruption.
✓Credit-based cost controls let you set per-user or per-session budgets, preventing chatbot sessions from generating unbounded costs.
Evidence snapshot

LLM API for Chatbots and Conversational AI implementation evidence

Use-case readiness across problem fit, expected outcomes, and integration workload.

Problems mapped
3
pain points addressed
Benefits
4
outcome claims surfaced
Integration steps
4
path to first deployment
Decision FAQs
5
adoption blockers handled

Integration path

  1. Connect your chatbot backend to the LLMWise streaming endpoint. Use the LLMWise SDK or call POST /api/v1/chat directly with stream=true; the API streams via SSE with structured delta chunks.
  2. Set the model to Auto for intelligent per-message routing, or choose a specific model for your chatbot's personality. Configure system prompts using the standard role/content message format.
  3. Enable Mesh mode on your chatbot endpoint to add failover. Define a fallback chain such as GPT-5.2 to Claude Sonnet 4.5 to Gemini 3 Flash, so conversations never stall.
  4. Monitor conversation metrics in the LLMWise dashboard: latency, token usage, cost per session, and model distribution. Use Optimization policies to refine routing as conversation patterns evolve.
Example API call
POST /api/v1/chat
{
  "model": "auto",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "..."}
  ],
  "stream": true
}
Example workflow

A user opens your chatbot and types a simple greeting. Auto mode routes it to Gemini 3 Flash, which responds in under 200 milliseconds with a friendly welcome. The user then asks a complex product comparison question. Auto mode detects the reasoning complexity and routes to Claude Sonnet 4.5, which streams a detailed comparison token by token via SSE. Mid-conversation, Claude's provider experiences a rate limit spike. Mesh failover detects the 429 response within one second, opens the circuit breaker, and reroutes the request to GPT-5.2 — the user sees a brief pause but receives a complete, high-quality answer. The full conversation history is preserved across the model switch, so the next message continues naturally.

Why LLMWise for this use case

Chatbots live and die by responsiveness and uptime. LLMWise gives you both without building your own orchestration layer: streaming SSE delivers the instant-typing feel users expect, Auto mode matches each message to the ideal model so you are not overpaying for simple replies or under-serving complex questions, and Mesh failover ensures conversations never stall due to a provider outage. The result is a chatbot that feels fast, handles everything from small talk to deep reasoning, and stays online 24/7 — all through a single API endpoint.

Common questions

Does LLMWise support streaming for chatbots?
Yes. All LLM endpoints stream via Server-Sent Events. You get real-time delta chunks plus a final done payload that includes credits charged and the resolved model. The docs include Next.js and React streaming examples.
Can I maintain conversation context across model switches?
Yes. LLMWise passes the full conversation history you send in each request. If failover switches the model mid-conversation, the new model receives the same message history and continues seamlessly. The conversation context is always under your control.
How do I prevent a single chatbot user from consuming too many credits?
Implement per-user credit budgets in your application layer using the LLMWise Credits API. Check the user's remaining balance before each message and return a friendly limit message when credits run out. The 402 status code makes this easy to handle programmatically.
What is the best LLM API for building chatbots?
The best chatbot API depends on your priorities. If you need multi-model routing so each conversation turn uses the optimal model, streaming SSE for real-time token delivery, and automatic failover to keep chats flowing during provider outages, LLMWise is purpose-built for that stack. Unlike single-provider APIs, LLMWise gives you access to nine models through one integration, so your chatbot can use a fast cheap model for greetings and a powerful reasoning model for complex questions — all transparently within the same conversation.
How do I add AI chat to my existing application?
Connect your backend to the LLMWise streaming endpoint using the LLMWise SDK (or direct HTTP). Send messages as role/content entries with the conversation history, and LLMWise streams back responses via SSE. Add Mesh routing for failover and Auto mode for intelligent model selection, and you have production-grade AI chat quickly without building your own orchestration layer.

One wallet, enterprise AI controls built in

You only pay credits per request. No monthly subscription. Paid credits never expire.

Replace multiple AI subscriptions with one wallet that includes routing, failover, and optimization.

Chat, Compare, Blend, Judge, MeshPolicy routing + replay labFailover without extra subscriptions
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