Wireflow gives developers a visual canvas and a REST API that handles inference across 157+ AI models, so you can generate images, video, and audio with a single HTTP call instead of managing GPU infrastructure yourself. Whether you are building a SaaS product, a marketing pipeline, or a creative tool, understanding how AI inference APIs work is the fastest path to shipping AI features without maintaining your own hardware.
What Is an AI Inference API?
An inference API accepts input data (a text prompt, an image, audio) and returns the output of a trained AI model. The API provider runs the model on its own GPUs, and you interact with it through standard HTTP requests. For a hands-on look, check out the AI inference API feature page, which walks through how this pattern works for every supported model. The concept matters because inference is the expensive, latency-sensitive part of any AI application, and offloading it to a managed API removes the hardest operational burden.
Unlike training, which happens once (or periodically), inference runs every time a user triggers a generation. That means cost and speed scale linearly with usage. A good inference API provides clear rate limits, transparent pricing, and async execution patterns so you can build production pipelines without surprises. In practice, most teams interact with inference APIs through simple HTTP clients (curl, fetch, axios, requests) rather than vendor-specific SDKs, which keeps the integration portable across providers.
Anatomy of an Inference Request
Every inference API follows the same basic pattern: authenticate, send a payload, and poll or wait for the result. Below is a concrete example using a REST API to execute a workflow that generates an image from a text prompt. API keys are created at Settings > API Keys and begin with sk-. For full details, see the API overview docs. The authentication model uses Bearer tokens, which is the same pattern used by OpenAI, Replicate, and most other inference providers, so your existing HTTP client code transfers directly.
Step 1: Execute the workflow
curl -X POST https://www.wireflow.ai/api/v1/workflows/YOUR_WORKFLOW_ID/execute \
-H "Authorization: Bearer sk-your-api-key" \
-H "Content-Type: application/json" \
-d '{
"nodes": [...],
"edges": [...],
"triggerData": { "prompt": "A sunset over a mountain lake" }
}'
This returns a 201 status with an executionId. The model has started running, but it is not finished yet. You need to poll for the result.
Step 2: Poll until complete
curl https://www.wireflow.ai/api/v1/workflows/executions/EXECUTION_ID/poll \
-H "Authorization: Bearer sk-your-api-key"
The response moves through RUNNING to COMPLETED (with node outputs) or FAILED (with an error). Recommended polling strategy: start at 1 second, multiply by 1.5 on each retry, cap at 10 seconds. Adding an Idempotency-Key header on the execute call prevents duplicate runs within a 24-hour replay window.
Handling Errors and Rate Limits
Production inference calls fail. Accounts run out of credits, rate limits kick in, models time out. A well-written client handles every case gracefully. The API returns standard HTTP codes: 401 for bad or expired keys, 402 when credits are insufficient (the response body includes requiredCredits, availableCredits, and a per-node breakdown), 429 when you hit your tier's request ceiling, and 500 for server errors. The error shape is consistent JSON ({"error":"message"}), which makes it simple to parse in any language.
Rate limits vary by plan: Free gets 10 requests per minute and 50 daily executions, Starter gets 20/200, Pro gets 60/1,000, and Enterprise gets 200/unlimited. Execution-per-minute stays at 10 across all tiers, so even Enterprise users cannot burst more than 10 concurrent model runs per minute. Every response includes X-RateLimit-Remaining and X-RateLimit-Reset headers, plus a Retry-After value on 429 responses. You can inspect limits per request using X-Request-Id for debugging with support.
Here is a practical JavaScript snippet for handling rate limits in a production client:
async function executeWithRetry(workflowId, body, apiKey, maxRetries = 3) {
for (let attempt = 0; attempt < maxRetries; attempt++) {
const res = await fetch(
`https://www.wireflow.ai/api/v1/workflows/${workflowId}/execute`,
{
method: 'POST',
headers: {
'Authorization': `Bearer ${apiKey}`,
'Content-Type': 'application/json'
},
body: JSON.stringify(body)
}
);
if (res.status === 429) {
const wait = parseInt(res.headers.get('Retry-After') || '5', 10);
await new Promise(r => setTimeout(r, wait * 1000));
continue;
}
return res.json();
}
throw new Error('Max retries exceeded');
}
This pattern keeps your application resilient without manual intervention, and the same approach applies to any API-driven generation platform.
When launching dev tools or AI products, having a clear go-to-market strategy helps you reach early adopters who will stress-test your API integration before the wider audience arrives.
Webhooks: Event-Driven Inference Without Polling
For applications where polling adds unnecessary latency or complexity, webhook-triggered execution removes the need for API key management entirely. You publish a workflow and receive a webhookId. Any external service can then fire a POST request to trigger a run, no API key required. For detailed webhook configuration, see the webhooks documentation.
curl -X POST https://www.wireflow.ai/api/v1/workflow/WEBHOOK_ID/trigger \
-H "Content-Type: application/json" \
-d '{"prompt": "Generate a product mockup on white background"}'
This returns 202 with an executionId. The CORS policy is open (*), which makes webhooks useful for browser-based tools and no-code integrations. Note the path uses singular /workflow/ (not /workflows/).
Choosing the Right Model for Your Inference Workload
The model you call through the API determines cost, speed, and output quality. The node registry includes 157 node types across categories: image generation (Flux 2, Nano Banana Pro, Imagen 4, Seedream v4), video generation (Kling Video 2.5), talking head (VEED Fabric), editing (Flux 2 Edit, Seedream v4 Edit), and utilities like prompt concatenation. Each model node maps to one API call under the hood. You can browse the full catalog at the models page or review the node reference.
When picking a model, consider three factors: latency (lightweight models like Nano Banana Lite return results in 2-5 seconds; heavy video models may take 30-90 seconds), output resolution (some models default to 1K, others support up to 4K), and cost per generation (which the API surfaces in the 402 error breakdown). A common pattern is to run a cheap model during development and switch to a higher-fidelity option in production by changing a single nodeType value in the workflow definition.
For most use cases, start with a lightweight model like generate:nano_banana_lite for fast iteration, then switch to generate:flux_2_pro when you need higher fidelity. The visual node editor lets you swap models without changing your API integration code, because the workflow definition (nodes + edges) is what you send to the execute endpoint.
Automating Inference with Claude and Agent Patterns
The official Claude Skill lets Claude Code and Claude Desktop drive workflows programmatically through the same REST endpoints covered above. Instead of hardcoding API calls, you describe what you want in natural language and the agent builds, executes, and retrieves results automatically. This pattern works well for creative workflow automation where the exact prompt or model choice depends on context. You can read more about the skill at the Claude Skill guide.
Published workflows become callable app endpoints that accept structured inputs and return outputs. This makes it straightforward to embed inference into SaaS applications or expose generation capabilities to end users behind your own product. The workflow graph itself (nodes, edges, parameters) is the contract between your code and the inference layer, and it limits complexity to 100 nodes and 500 edges per workflow.
Try it yourself: Build this workflow in Wireflow. The nodes are pre-configured with the exact setup discussed above, so you can execute an inference call and see results immediately.
Frequently Asked Questions
What is an AI inference API?
An AI inference API is an HTTP endpoint that accepts input data and returns the output of a trained AI model. You send a request with your prompt or file, and the provider runs the model on its GPUs, returning the generated result.
How is inference different from training?
Training updates a model's weights using large datasets and massive compute. Inference uses the already-trained model to produce outputs from new inputs. Inference runs on every user request, so its cost and latency are what matter in production.
Do I need a GPU to use an inference API?
No. The entire point of an inference API is that the provider manages the GPU infrastructure. You make standard HTTP calls from any environment that supports it.
What happens when I hit a rate limit?
The API returns a 429 status code with a Retry-After header telling you how many seconds to wait. Your client should implement exponential backoff and check X-RateLimit-Remaining to avoid hitting the ceiling in the first place.
Can I trigger inference without an API key?
Yes, through webhooks. Published workflows expose a webhook URL that accepts POST requests without authentication. This is useful for integrations where sharing an API key is impractical.
How do I handle long-running inference jobs?
Use the async execute-then-poll pattern. Call the execute endpoint, receive an executionId, then poll until the status changes to COMPLETED or FAILED. Exponential backoff (start 1s, multiply 1.5x, cap 10s) keeps polling efficient.
What models can I call through an inference API?
Wireflow supports 157+ node types including image generators (Flux 2, Nano Banana, Imagen 4), video generators (Kling Video 2.5), editing models, and utility nodes. The full list is at /docs/nodes.
How much does inference cost?
Cost depends on your plan tier and which models you call. The API returns a 402 error with a credit breakdown per node when your balance is insufficient, so you always know the exact cost before retrying.
