$ cat services.json

gRPC Service Design

Design efficient Protocol Buffer schemas and service definitions.

Deliverables:

Proto file design
Schema versioning strategy
Backward compatibility planning
Code generation setup
Documentation

gRPC Implementation

Build production-grade gRPC services in multiple languages.

Deliverables:

Python gRPC services
Node.js gRPC services
Java/Go implementations
Streaming patterns
Error handling

gRPC Infrastructure

Deploy and operate gRPC services at scale.

Deliverables:

Load balancing setup
Service mesh integration
Monitoring and tracing
Rate limiting
Circuit breakers

$ cat README.md

When to Choose gRPC

gRPC excels in these scenarios:

Microservices Communication: Internal service-to-service calls
Real-time Streaming: Live data feeds, chat, notifications
Mobile Backends: Efficient binary protocol saves bandwidth
Polyglot Systems: Services in different languages need to communicate
High-Throughput APIs: When REST/JSON becomes a bottleneck

gRPC vs REST: When to Use Which

Aspect	gRPC	REST
Performance	Binary, 10x faster	JSON, human-readable
Typing	Strong (Proto)	Weak (OpenAPI optional)
Streaming	Native support	Workarounds needed
Browser	Needs gRPC-Web	Native support
Tooling	Code generation	Manual or codegen
Learning Curve	Steeper	Gentler

My recommendation: Use gRPC for internal microservices, REST for public APIs. I can help you design hybrid architectures that leverage both.

Python gRPC Implementation

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import grpc
from concurrent import futures
from ai.inference.v1 import inference_pb2, inference_pb2_grpc

class InferenceServicer(inference_pb2_grpc.InferenceServiceServicer):
    def __init__(self, model_registry):
        self.models = model_registry

    async def Predict(self, request, context):
        model = self.models.get(request.model_id)
        if not model:
            context.abort(grpc.StatusCode.NOT_FOUND, "Model not found")
        
        scores = await model.predict(request.features)
        return inference_pb2.PredictResponse(
            prediction_id=str(uuid.uuid4()),
            scores=scores,
            label=model.decode_label(scores),
            confidence=max(scores),
        )

def serve():
    server = grpc.aio.server(futures.ThreadPoolExecutor(max_workers=10))
    inference_pb2_grpc.add_InferenceServiceServicer_to_server(
        InferenceServicer(model_registry), server
    )
    server.add_insecure_port('[::]:50051')
    server.start()
    server.wait_for_termination()

$ ls -la projects/

@ Flowrite

Challenge:

Solution:

Result:

@ Anaqua

Challenge:

Solution:

Result:

⚡ gRPC

$ cat services.json

gRPC Service Design

gRPC Implementation

gRPC Infrastructure

$ cat README.md

When to Choose gRPC

gRPC vs REST: When to Use Which

Python gRPC Implementation

$ ls -la projects/

Ready to Get Started?