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24 alternativesClip-as-a-service
CLIP-as-service provides scalable image and sentence embeddings, reasoning, and ranking with CLIP.
About Clip-as-a-service
CLIP-as-a-service is a client-and-server system for producing image and text embeddings with CLIP, intended to be deployed as a microservice inside neural search and cross-modal applications. The server can serve CLIP models through TensorRT, ONNX runtime or PyTorch without JIT, and multiple CLIP models can run on a single GPU with horizontal scale up and down and automatic load balancing. Clients connect over gRPC, HTTP or WebSocket with TLS and compression, and support non-blocking duplex streaming for large data and long-running tasks. The API also exposes a ranking endpoint that scores candidate captions against an image, which the repository demonstrates for object recognition, object counting, color recognition and spatial relation understanding.
Documented throughput is about 800 QPS with the default configuration (single replica, PyTorch without JIT) on a GeForce RTX 3090, so capacity depends on the hardware and model configuration used. The examples call a hosted inference address (for example a wolf.jina.ai endpoint) and pass an Authorization token, which means requests can leave your own environment when a remote endpoint is used; running the server yourself avoids that. Pricing is not stated in the source, and the repository is listed as not open source in the directory data.
Key features
- Embeds images and text with CLIP through a client and server
- Serves models via TensorRT, ONNX runtime or PyTorch without JIT
- Scales multiple CLIP models on one GPU with automatic load balancing
- Async client over gRPC, HTTP or WebSocket with TLS and compression
- Ranking endpoint scores candidate texts against an image
- Integrates with Jina and DocArray for neural search pipelines
Best for: Developers building neural search or cross-modal retrieval systems who need CLIP embeddings served as a scalable microservice.
Good for
- Non-blocking duplex streaming on requests and responses, aimed at large data and long-running tasks
- Consistent embedding API for both images and sentences across several network protocols
- Documented about 800 QPS with default configuration on a single GeForce RTX 3090
Watch out for
- No pricing information is provided in the source
- The example deployments depend on a hosted inference address and an access token
- Quoted throughput is tied to a specific default configuration and GPU
Clip-as-a-service at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Clip-as-a-service FAQ
Which network protocols can clients use?
The async client can switch between gRPC, HTTP and WebSocket, with TLS and compression supported.
What visual reasoning tasks does the ranking endpoint cover?
The repository lists four basic skills: object recognition, object counting, color recognition and spatial relation understanding.
What hardware and configuration does the 800 QPS figure refer to?
It is measured with the default configuration (single replica, PyTorch without JIT) on a GeForce RTX 3090.
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