Best Video Intelligence APIs in 2026

from google.cloud import videointelligence

client = videointelligence.VideoIntelligenceServiceClient()
features = [
    videointelligence.Feature.LABEL_DETECTION,
    videointelligence.Feature.SHOT_CHANGE_DETECTION,
]
operation = client.annotate_video(
    request={"input_uri": "gs://my-bucket/video.mp4",
             "features": features}
)
result = operation.result(timeout=300)
for label in result.annotation_results[0].segment_label_annotations:
    print(f"{label.entity.description}: "
          f"{label.segments[0].confidence:.2f}")

from twelvelabs import TwelveLabs

client = TwelveLabs(api_key="YOUR_API_KEY")
index = client.index.create(
    name="my-videos",
    engines=[{"name": "marengo2.7", "options": ["visual", "conversation"]}]
)
task = client.task.create(index_id=index.id, url="https://example.com/video.mp4")
task.wait_for_done()
results = client.search.query(
    index_id=index.id,
    query_text="person explaining a diagram on a whiteboard",
    options=["visual", "conversation"]
)
for clip in results.data:
    print(f"[{clip.start:.1f}s - {clip.end:.1f}s] score: {clip.score:.2f}")

const accountId = "YOUR_ACCOUNT_ID";
const apiKey = "YOUR_API_KEY";
const videoUrl = "https://example.com/video.mp4";

const uploadRes = await fetch(
  'https://api.videoindexer.ai/${accountId}/Videos?' +
  'name=my-video&videoUrl=${encodeURIComponent(videoUrl)}&accessToken=${apiKey}',
  { method: "POST" }
);
const { id } = await uploadRes.json();
// Poll GET /Videos/{id}/Index until state === "Processed"
// Result includes faces, topics, brands, sentiment, OCR, transcript

const symbl = require("@symblai/symbl-js");

await symbl.init({ appId: "YOUR_APP_ID", appSecret: "YOUR_SECRET" });
const conversation = await symbl.async.addVideoUrl({
  url: "https://example.com/meeting.mp4",
  name: "Q4 Planning Meeting",
});
const topics = await symbl.getTopics(conversation.conversationId);
const actions = await symbl.getActionItems(conversation.conversationId);
actions.forEach((item) =>
  console.log('Action: ${item.text} (assignee: ${item.from?.name})')
);

import boto3

rekognition = boto3.client("rekognition")
response = rekognition.start_label_detection(
    Video={"S3Object": {
        "Bucket": "my-bucket",
        "Name": "video.mp4"
    }},
    MinConfidence=80,
    NotificationChannel={
        "SNSTopicArn": "arn:aws:sns:us-east-1:123456:video-done",
        "RoleArn": "arn:aws:iam::123456:role/rekognition-role"
    }
)
job_id = response["JobId"]
# Poll get_label_detection(JobId=job_id) or use SNS callback

import boto3

bda = boto3.client("bedrock-data-automation-runtime")

job = bda.invoke_data_automation_async(
    inputConfiguration={"s3Uri": "s3://my-videos/clip.mp4"},
    outputConfiguration={"s3Uri": "s3://my-videos/bda-output/"},
    dataAutomationConfiguration={
        "dataAutomationProjectArn": "arn:aws:bedrock:us-east-1:123456789012:data-automation-project/PROJECT_ID",
        "stage": "LIVE",
    },
    dataAutomationProfileArn="arn:aws:bedrock:us-east-1:123456789012:data-automation-profile/us.data-automation-v1",
)

status = bda.get_data_automation_status(invocationArn=job["invocationArn"])
print(status["status"])  # results are written as JSON under the output S3 prefix

from clarifai.client.user import User

client = User(user_id="YOUR_USER_ID", pat="YOUR_PAT")
app = client.app(app_id="my-video-app")
model = app.model(model_id="general-image-recognition")

# Analyze video by extracting frames
input_obj = app.inputs().upload_from_url(
    input_id="video-1",
    video_url="https://example.com/video.mp4"
)
prediction = model.predict_by_url(
    url="https://example.com/video.mp4",
    input_type="video",
    sample_ms=1000  # analyze 1 frame per second
)
for frame in prediction.outputs:
    for concept in frame.data.concepts:
        print(f"Frame {frame.id}: {concept.name} ({concept.value:.2f})")