---
title: RPC Framework VS REST | The Detailed Guide
description: RPC Framework VS REST to build fashion characterised by a set of building limitations and aiming for wide appropriation with numerous API customers
image: https://www.xenonstack.com/hubfs/rpc-framework-vs-rest.png
---

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# RPC Framework VS REST | The Detailed Guide

[Navdeep Singh Gill](https://www.xenonstack.com/insights/author/navdeep-singh-gill) | 05 October 2022

![RPC Framework VS REST](https://www.xenonstack.com/hs-fs/hubfs/rpc-framework-vs-rest.png?width=1280&height=720&name=rpc-framework-vs-rest.png)

## What is RPC?

A Remote Procedure Call is a software communication protocol that considers far off the execution of a limit in a substitute setting. It grows the possibility of adjacent framework calling yet puts it with respect to an HTTP API.

Initial XML-RPC was problematic because ensuring data types of XML payloads is tough. So, later an its API started using a more concrete JSON-RPC specification which is considered a simpler alternative to SOAP. gRPC is the most recent RPC variant created by Google in 2015. With pluggable help for load adjusting, following, wellbeing checking, and verification, gRPC is appropriate for interfacing microservices.

> Enables the server and client applications to communicate transparently and build connected systems. Click to explore about our, [gRPC Adoption and Working Architecture](https://www.xenonstack.com/insights/what-is-grpc)

## How does it works?

A client summons a far-off method, serializes the boundaries and extra data into a message, and sends the message to a server. On getting the message, the server deserializes its substance, executes the mentioned activity, and sends an outcome back to the client. The server stub and client stub deal with the serialization and deserialization of the boundaries.

### What the pros and cons of it?

The pros of RPC are:

- **Direct interaction:** It utilizes GET to bring data and POST for all the other things. The mechanics of the connection between a server and a client come down to calling an endpoint and getting a reaction.
- **Easy-to-add functions:** If we get another prerequisite for our API, we can undoubtedly add another endpoint executing this necessity. write another capacity and toss it behind an endpoint, and presently, a client can hit this endpoint to get the information meeting the set prerequisite.
- **High performance:** Lightweight payloads go simple on the arrange, giving tall execution, which is imperative for shared servers and parallel computations executing on workstation systems. It can optimize the arranged layer and make it exceptionally effective by sending tons of daily messages between diverse administrations.

The cons of RPC are:

- **Tight coupling to the basic framework:** An API's reflection level contributes to its reusability. The more tightly it is to the basic framework, the less reusable it'll be for other frameworks. It's tight coupling to the basic framework doesn't permit a reflection layer between the capacities within the framework and the external API. This raises security issues as it's straightforward to spill usage points of inteREST, almost the basic framework, into the API. It's tight coupling makes scalability requirements and freely coupled groups challenging to attain. So, the client either stresses approximately any possible side impacts of calling a specific endpoint or tries figuring out what endpoint to call since it doesn't get how the server is naming its functions.
- **Low discoverability:** In it, there's no way to introspect the API or send a request and begin understanding what work to call based on its demands.
- **Function explosion:** It's so simple to make current capacities. So, rather than altering the existing ones, we make unused ones finishing up with a colossal list of covering capacities that are difficult to get.

> An application programming interface API Testing attempts to connect an application to the web and different APIs. Click to explore about our, [API Testing Tools and Best Practises](https://www.xenonstack.com/insights/what-is-api-testing)

## RPC Use Cases

It design started being utilized around the 80s, but this doesn't naturally make it outdated. Huge companies like Google, Facebook (Apache Thrift), and Jerk (Twirp) utilize its high-performance variates inside to perform greatly high-performance, low-overhead informing. Their gigantic microservices frameworks require internal communication to be precise while organized in brief messages.

### Command API

It is a suitable choice for sending commands to an inaccessible framework. For occurrence, a Slack API is exceptionally command-focused: Connect a channel, take off a channel, and send a message. So, the architects of the Slack API modeled it in an RPC-like fashion, making it little, tight, and easy to utilize.

### Customer-specific APIs for internal microservices

Having coordinated integration between a single supplier and customer, we don't need to spend a part of our time transmitting a part of metadata over the wire, as a REST API does. With a tall message rate and message execution, gRPC and Twirp are solid cases for microservices. Utilizing HTTP 2 beneath the hood, gRPC can optimize the organized layer and make it exceptionally productive by sending daily messages between distinctive administrations. In any case, in case you're not pointing at tall arranged execution, but maybe at a steady API contact between groups distributing exceedingly particular microservices, REST will guarantee that.

> An architecture containing tools, libraries, and functionalities suitable to build and maintain massive web projects using a fast and efficient approach. Click to explore about our, [Python Flask Framework](https://www.xenonstack.com/blog/python-flask-framework)

## What is REST (Representational State Transfer)?

It may be a self-explanatory API building fashion characterized by a set of building limitations and aiming for wide appropriation with numerous API customers.

### How REST works?

It isn't as entirely characterized as Cleanser. Serene engineering ought to comply with six engineering limitations:

#### Uniform Interface

Allowing a uniform way of association with a given server notwithstanding gadget or application sort.

#### Stateless

The vital state to handle the task is contained inside the task itself and without the server putting away anything related to the session.

#### Client-server Architecture

We are permitting autonomous advancement of either side.

In reality, a few administrations are Serene, as it were, to a degree. They have RPC fashion at the center, break down bigger administrations into assets, and utilize the HTTP framework proficiently. But the key portion is utilizing hypermedia, aka HATEOAS, brief for Hypertext: The Motor of Application State. Essentially, it implies that with each reaction, its API gives metadata connecting to all the related data almost how to utilize the API. That's what empowers decoupling the client and the server. As a result, both API suppliers and API providers can advance freely without preventing their communication.

> The well known Java frameworks used for writing tests used for writing matcher objects. Click to explore about our, [AssertJ and HamCrest Assertion Frameworks](https://www.xenonstack.com/insights/what-are-assertion-frameworks)

## What are the pros and cons of REST?

The pros of REST are:

- **Decoupled client and server:** Decoupling the client and the server as much as possible, REST allows for a better abstraction than it. A system with abstraction levels can encapsulate its details to identify better and sustain its properties. This makes a REST API flexible enough to evolve while remaining a stable system.
- **Cache-friendly:** Reusing a part of HTTP devices, it is the, as it were, fashion that permits caching information on the HTTP level. Caching execution on any other API will require arranging an extra cache module.
- **Multiple formats support:** The capacity to bolster numerous groups for putting away and trading information is one of the reasons REST is right now a winning choice for building open APIs.

The cons of REST are:

- **No single REST structure:** There's no correct way to construct a REST API. How and which assets to show will depend on each situation. This makes it straightforward in hypothesis but troublesome in honing.
- **Big payloads:** It returns a part of wealthy metadata so that the client can get everything essential around the state of the application fair from its reactions. And this chattiness is no enormous bargain for an arranged pipe with parcels of transfer speed capacity. But that's not continuously the case.

> A Stateful application remembers specific details of a user like profile, preferences, and user actions. Click to explore about our, [Stateful and Stateless Applications](https://www.xenonstack.com/insights/stateful-and-stateless-applications)

## REST Use Cases

- **Management APIs:** APIs centered on overseeing objects in a framework and expecting numerous customers are the foremost common API sort. REST makes a difference such APIs have solid discoverability and excellent documentation, which fits this question well.
- **Simple resource-driven apps:** It may be a critical approach for interfacing resource-driven apps that don't require adaptability in questions.

## RPC VS REST

| **S.No** | **REST API** | **RPC API** |
| --- | --- | --- |
| **1** | It is resource-oriented. | It is action-oriented. |
| **2** | Supports HTTP methods GET, POST, PUT, PATCH, and DELETE. | It only supports GET and POST requests. |
| **3** | It provides flexibility for hardware architecture | no flexibility in it for hardware architecture |
| **4** | It supports hypermedia and hyperlinks | It does not supports hypermedia and hyperlinks |
| **5** | It allows to specify Content-types or accept headers | Requires payloads of a few data types as XML for XML-RPC. |

> A system testing of software or hardware conducted on an entire, integrated system to assess the system's compliance with its specified requirements. Click to explore about our, [System Testing Tools and its Best Practices](https://www.xenonstack.com/insights/what-is-system-testing)

## Conclusion

Each API venture has distinctive necessities and needs. Ordinarily, the engineering choice depends on

- The Language of programming used.
- The environment which is being developed.

After the day, trying a few small use situations with a particular style feels right to see if it fits your use case and solves your problems. If it does, try expanding and see if this matches more use situations.

- Discover more about [Digital Platform Strategy](https://www.xenonstack.com/insights/digital-platform)
- Explore here about [Continuous Intelligence](https://www.xenonstack.com/insights/continuous-intelligence)

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## Navdeep Singh Gill

Global CEO and Founder of XenonStack

Navdeep Singh Gill is serving as Chief Executive Officer and Product Architect at XenonStack. He holds expertise in building SaaS Platform for Decentralised Big Data management and Governance, AI Marketplace for Operationalising and Scaling. His incredible experience in AI Technologies and Big Data Engineering thrills him to write about different use cases and its approach to solutions.

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XenonStack Agentic Foundry powers enterprise agentic systems with unified infra, analytics, workflows, and security to drive automation and compliance.

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## AI Engineering

[Composite AI](https://www.xenonstack.com/artificial-intelligence/composite-ai/) [Decision AI](https://www.xenonstack.com/artificial-intelligence/decision-ai/) [AI Quality](https://www.xenonstack.com/artificial-intelligence/ai-quality/) [Generative AI](https://www.xenonstack.com/artificial-intelligence/generative-ai/) [Multimodal AI](https://www.xenonstack.com/artificial-intelligence/multimodal-ai/) [AI Assurance](https://www.xenonstack.com/artificial-intelligence/explainable-ai/) [MLOps](https://www.xenonstack.com/artificial-intelligence/mlops/) [Physical AI](https://www.xenonstack.com/artificial-intelligence/physical-ai/) [Augmented Engineering](https://www.xenonstack.com/artificial-intelligence/ai-augmented-software-development/) [Generative BI](https://www.xenonstack.com/artificial-intelligence/generative-bi/)

## Data Foundry

[Streaming Data Platform](https://www.xenonstack.com/dataops/streaming-data-platform/) [Data Lakehouse](https://www.xenonstack.com/dataops/delta-lake/) [Data Catalog](https://www.xenonstack.com/dataops/data-catalog/) [Data Observability](https://www.xenonstack.com/dataops/data-observability/) [Cloud Data Warehouse](https://www.xenonstack.com/dataops/cloud-data-warehouse/) [Data Engineering](https://www.xenonstack.com/dataops/data-engineering/) [MetaData Management](https://www.xenonstack.com/dataops/metadata-management/) [Data Quality](https://www.xenonstack.com/dataops/augmented-data-quality/) [Real Time Analytics](https://www.xenonstack.com/dataops/real-time-analytics/) [Data Modernization](https://www.xenonstack.com/dataops/data-modernization/)

## Platform Engineering

[Cloud Native](https://www.xenonstack.com/cloud-native/platform-engineering/) [Automation As Code](https://www.xenonstack.com/cloud-native/automation-as-code/) [Observability](https://www.xenonstack.com/cloud-native/observability/) [FinOps](https://www.xenonstack.com/cloud-native/finops/) [Application Modernization](https://www.xenonstack.com/cloud-native/application-modernization/) [DevSecOps](https://www.xenonstack.com/cloud-native/devsecops/) [Site Reliability Engineering](https://www.xenonstack.com/cloud-native/site-reliability-engineering/) [Progressive Delivery](https://www.xenonstack.com/cloud-native/progressive-delivery/) [GitOps](https://www.xenonstack.com/cloud-native/gitops/) [Compliance as code](https://www.xenonstack.com/cloud-native/compliance-as-code/) [Value Stream Management](https://www.xenonstack.com/cloud-native/value-stream-management/) [Policy as Code](https://www.xenonstack.com/cloud-native/policy-as-code/) [Telemetry Pipeline](https://www.xenonstack.com/cloud-native/telemetry-pipeline/)

## Agentic AI

[Agentic Analytics](https://www.xenonstack.com/agentic-ai/enterprise-systems/) [Agentic AI Systems](https://www.xenonstack.com/agentic-ai/agentic-ai-system/) [Process Intelligence](https://www.xenonstack.com/agentic-ai/business-process-operations/) [Developer Experience](https://www.xenonstack.com/agentic-ai/developer-experience-platform/) [Autonomous Operations](https://www.xenonstack.com/agentic-ai/autonomous-operations/) [AI Vision at EDGE](https://www.xenonstack.com/agentic-ai/edge-and-vision-ai/) [Compound AI System](https://www.xenonstack.com/agentic-ai/compound-ai-system/) [GUI Agents](https://www.xenonstack.com/agetic-ai/gui-agents/) [CMDB Management](https://www.xenonstack.com/agentic-ai/cmdb-management/) [ITSM](https://www.xenonstack.com/agentic-ai/itsm/) [Network Automation](https://www.xenonstack.com/agentic-ai/network-automation/)

## AI Agents

[DataBricks AI Agents](https://www.xenonstack.com/ai-agents/databricks-ai-agents/) [SnowFlake AI Agents](https://www.xenonstack.com/ai-agents/snowflake-ai-agents/) [ServiceNow AI Agents](https://www.xenonstack.com/ai-agents/servicenow-ai-agents/) [AWS AI Agents](https://www.xenonstack.com/ai-agents/aws-ai-agents/) [Microsoft Azure AI Agents](https://www.xenonstack.com/ai-agents/microsoft-azure-ai-agents-actions/) [SalesForce AI Agents](https://www.xenonstack.com/ai-agents/salesforce-agents-actions/) [MySQL AI Agents](https://www.xenonstack.com/ai-agents/mysql-agents-actions/) [PostgreSQL AI Agents](https://www.xenonstack.com/ai-agents/postgresql-agents-actions/) [Datadog AI Agents](https://www.xenonstack.com/ai-agents/datadog-agents-actions/) [DynaTrace AI Agents](https://www.xenonstack.com/ai-agents/dynatrace-agents-actions/) [Splunk AI Agents](https://www.xenonstack.com/ai-agents/splunk-agents-actions/) [BigQuery AI Agents](https://www.xenonstack.com/ai-agents/bigquery-agents-actions/) [SAP AI Agents](https://www.xenonstack.com/ai-agents/sap-agents-actions/) [Infor AI Agents](https://www.xenonstack.com/ai-agents/infor-agents-actions/) [Oracle AI Agents](https://www.xenonstack.com/ai-agents/oracle-agents-actions/) [WorkDay AI Agents](https://www.xenonstack.com/ai-agents/workday-agents-actions/) [Jira AI Agents](https://www.xenonstack.com/ai-agents/jira-agents-actions/)

## Industry

[Aerospace and Aviation](https://www.xenonstack.com/industries/aerospace/) [Financial Services](https://www.xenonstack.com/industries/banking/) [Automotive And Industrial](https://www.xenonstack.com/industries/automotive/) [Consumer Tech](https://www.xenonstack.com/industries/consumer-technology/) [Technology, Media and Telco](https://www.xenonstack.com/industries/enterprise-technology/) [Digital Supply Chain](https://www.xenonstack.com/industries/digital-supply-chain/) [Hospitality and Tourism](https://www.xenonstack.com/industries/travel-hospitality/) [Discrete Manufacturing](https://www.xenonstack.com/industries/automotive/) [Education](https://www.xenonstack.com/industries/education/) [Media and Entertainment](https://www.xenonstack.com/industries/media-entertainment/) [Oil and Gas](https://www.xenonstack.com/industries/oil-and-gas/) [Energy and Utilities](https://www.xenonstack.com/industries/energy-and-utilities/)

## Enterprise Support

[AI Managed Services](https://www.xenonstack.com/managed-services/ai-managed-services/) [Kubernetes Managed Services](https://www.xenonstack.com/managed-services/kubernetes/) [SRE as a Service](https://www.xenonstack.com/managed-services/site-reliability-engineering/) [Data Managed Services](https://www.xenonstack.com/managed-services/big-data/) [Analytics Managed Services](https://www.xenonstack.com/managed-services/analytics-managed-services/) [Data Protection](https://www.xenonstack.com/readiness-assessment/data-protection/) [On-Premise AI](https://www.xenonstack.com/managed-services/on-premise-ai-cluster/)

## Solutions

[Private Cloud](https://www.xenonstack.com/solutions/private-cloud/) [Internal Developer Platform](https://www.xenonstack.com/solutions/internal-developer-platform/) [AI Inference](https://www.xenonstack.com/solutions/ai-inference/) [Open-Source Data Platform](https://www.xenonstack.com/solutions/open-source-data-platform/) [AI Trust Score](https://www.xenonstack.com/solutions/ai-trust-score/) [Autonomous SoC](https://www.xenonstack.com/solutions/autonomous-soc/) [Digital Twin](https://www.xenonstack.com/solutions/digital-twin/) [Readiness Assessment](https://www.xenonstack.com/readiness-assessment/) [Talk To Specialist](https://www.xenonstack.com/talk-to-specialist/)

## Company

[About Us](https://www.xenonstack.com/about-us/) [Leadership Team](https://www.xenonstack.com/about-us/leadership-team/) [TAO of XenonStack](https://www.xenonstack.com/about-us/tao-of-xenonstack/) [How We Grow](https://www.xenonstack.com/about-us/how-we-grow/) [How We Work](https://www.xenonstack.com/about-us/how-we-work/) [Careers](https://www.xenonstack.com/careers/) [XA - QSIR](https://www.xenonstack.com/xenonstack-academy/) [Contact Us](https://www.xenonstack.com/contact-us/) [Book Demo](https://demo.xenonstack.com/)

## Resources

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✕

## Agent SRE for Reliability and Observability Solutions

 AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability

- ![Performance Icon](https://www.xenonstack.com/hubfs/performance.svg)Proactive detection of performance and availability issues
- ![Root Cause Icon](https://www.xenonstack.com/hubfs/root-cause.svg)Root-cause analysis across microservices and environments
- ![Remediation Icon](https://www.xenonstack.com/hubfs/remediation.svg)Automated remediation playbooks to reduce MTTR

[Explore Agent SRE](https://agentsre.ai/)

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## Physical Surveillance with Vision AI Agent Technology

 AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly

- ![Motion Icon](https://www.xenonstack.com/hubfs/motion.svg)Real-time detection of suspicious motion or intrusion
- ![Video Search Icon](https://www.xenonstack.com/hubfs/video-search.svg)Natural language video search and instant playback
- ![Summary Icon](https://www.xenonstack.com/hubfs/summary.svg)Smart summaries for audits, investigations, and compliance

[See Vision AI in Action](https://www.xenonstack.ai/)

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## Agentic Data Intelligence Across Your Full Data Stack

 Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics

- ![Connects warehouses](https://www.xenonstack.com/hubfs/data-icon.svg)Connects to warehouses, lakes, and streaming sources
- ![Question Answering](https://www.xenonstack.com/hubfs/answers.svg)Question-answering in natural language
- ![Continuous monitoring](https://www.xenonstack.com/hubfs/monitoring-2.svg)Continuous monitoring for anomalies and KPI deviations

[See in Action](https://elixirdata.co/)

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✕

## Intelligent Diagnostic for Self-Healing System Automation

 Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment

- ![Diagnostics Icon](https://www.xenonstack.com/hubfs/diagnostic.svg)Automated diagnostics for recurring errors
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[See in Action](https://agentanalyst.ai/)

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✕

## Agentic GRC - Monitoring Risk and Compliance Controls

 AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready

- ![Controls Icon](https://www.xenonstack.com/hubfs/controls.svg)Continuous control checks across infrastructure and SaaS
- ![Audit Icon](https://www.xenonstack.com/hubfs/audit.svg)Automated evidence collection for audits
- ![Risk Icon](https://www.xenonstack.com/hubfs/risk.svg)Risk scoring and prioritized remediation recommendations

[Explore Agent GRC](https://agentgrc.ai/)

![monitoring-risk-and-compliance](https://www.xenonstack.com/hubfs/enhanced-security-measures.svg)

✕

## Agentic Finance and Procurement Intelligent Agents

 Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter

- ![Visibility Icon](https://www.xenonstack.com/hubfs/visibility.svg)Real-time visibility into spend and commitments
- ![Anomaly Icon](https://www.xenonstack.com/hubfs/anomaly.svg)Anomaly detection on invoices and vendor performance
- ![Workflow Icon](https://www.xenonstack.com/hubfs/workflow.svg)Intelligent workflows for approvals and sourcing decisions

[Optimize Finance & Procurement](https://www.xenonify.ai/)

![agentic-finance-and-procurement](https://www.xenonstack.com/hubfs/responsible-ai-aviators-banner-illustration.svg)

```json
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  },
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    },
    "name" : "Xenonstack Inc"
  }
}
```

```json
{
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    "@type" : "PostalAddress",
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}
```