TL;DR
An AI agent marketplace is a catalog where companies find, buy and deploy pre-built AI agents from a platform vendor and its partners. The enterprise options split into hyperscaler marketplaces from Microsoft, AWS and Google Cloud, and app-native stores from Salesforce, ServiceNow and Oracle. Buy from the marketplace tied to the systems your agent must act on, because billing, permissions and integration follow that choice. A listing is not a security approval, so check data handling, permissions, pricing and exit terms yourself. Budget for model usage, cloud compute and integration work on top of the listed price. Selling your own agent means joining the vendor’s partner program, packaging the agent to its rules and paying a listing fee.
Key Takeaways An agent marketplace is a procurement channel, so the contract, bill and admin controls follow the agent into your environment. Hyperscaler marketplaces give deployment choice, while app-native stores give faster setup inside one business system. Microsoft, AWS, Google Cloud, Salesforce, ServiceNow and Oracle all run agent catalogs with partner listings and admin approval. Marketplace vetting is a starting point, and your own security, data and legal review still decides the purchase. The listed price leaves out model usage, compute, integration and support, which often cost more than the license. Publishers pay a listing fee, such as 3% on AWS SaaS public offers and Microsoft’s 3% store service fee. Watch on YouTube
Google’s A2A Protocol: How AI Agents Collaborate Without Humans
Agents bought from different marketplaces still have to hand work to each other. Kanerika explains how the Agent2Agent protocol lets them do that, and why it now shapes which catalogs list which agents.
Where Should Your Next AI Agent Come From?
Should your next AI agent come from your cloud provider’s catalog, or from the store inside the CRM your sales team opens every morning? That question now reaches CIO desks almost every quarter, because the options keep multiplying. Microsoft, AWS, Google Cloud, Salesforce, ServiceNow and Oracle each run an agent marketplace of their own.
The catalogs look alike on the surface. Underneath, they differ in where the agent runs, who bills you and which data it can touch. An agent bought in the wrong place can still work, but it may sit outside your security review or your cloud commitment. Those differences decide whether the purchase saves months or starts a second integration project.
What Is an AI Agent Marketplace?
An AI agent marketplace is a curated catalog where organizations discover, evaluate, buy and deploy AI agents built by a platform vendor or its partners. Each listing also names the publisher, the agent’s permissions, its pricing and the systems it connects to. Purchase and activation then happen inside the account you already use for that platform.
That last point separates a marketplace from a page of links. Because you buy through the platform, the contract, the invoice and the admin controls travel with the agent. Still deciding whether to buy at all? Our AI agent builder comparison and the guide to custom AI agents cover the build side of that choice.
How Buying an Agent Through a Marketplace Works
The flow looks like ordinary software procurement, though with one extra approval layer. First someone finds an agent, the team reviews the listing, and procurement buys it through the platform account. Then an administrator decides who may use it.
Search. Browse by function, industry or compatibility, such as A2A support.Compare. Read the publisher details, permissions and pricing before reviews.Vet. Run your own security, data and legal checks, even for vetted listings.Purchase. Subscribe or accept a private offer once the checks pass.Approve and assign. An admin enables the agent for chosen users or groups.Monitor. Track usage, cost and behavior after go-live, since agents change over time.Google’s Gemini Enterprise shows the approval layer clearly. For example, its Agent Gallery has a Marketplace filter for third-party partner agents. When an employee wants one, they click Request access, and the request goes to an administrator for review.
Marketplace, Agent Store, Gallery and Directory Are Not the Same
Vendors use these words loosely, so pin them down before the shortlist meeting.
Marketplace. A commercial channel where you buy agents and other software under a contract, such as AWS Marketplace.Agent store. An in-app catalog where admins enable agents for users, such as Agent Store in Microsoft Copilot.Gallery or library. A set of templates, samples and approved agents, which may have no purchase flow at all.Directory. A discovery website that lists agents from many vendors, usually without billing or security review.Of these, only the first two carry a contract and admin controls. A directory entry is a lead to research, whereas a marketplace listing is something procurement can actually buy.
What You Will Find in These Catalogs
AWS groups its own catalog into four kinds of listing. They are pre-built agents, agent tools such as MCP servers and guardrails, agent development solutions and professional services. Other marketplaces mix the same ingredients, though in different proportions.
So a search for “agent” returns finished agents next to parts for building one. A pre-built agent does a whole job, while a tool or skill gives an agent you already run one more capability. Our explainer on AI agent skills covers that second category, while the overview of types of AI agents helps you sort the first.
Benefits of Buying From an AI Agent Marketplace
Buying through a marketplace changes the paperwork more than the technology. The agent may match one sold direct, yet the purchase lands inside contracts, bills and controls you already run.
One bill. Google Cloud and Salesforce put marketplace agents on the account you already pay, and AWS keeps the licenses in your console.Spend against existing commitments. Eligible Microsoft Marketplace purchases count toward an Azure Consumption Commitment.A screened starting point. Google lists pre-vetted partner agents, Salesforce vets partner agents for security, and Oracle validates each one against a checklist.Admin control from day one. In Microsoft 365 and Gemini Enterprise, an administrator approves an agent before employees can use it.Negotiated terms. Private offers let larger buyers agree custom prices and terms with the publisher.None of this proves the agent itself is any good. These benefits lower the cost of buying and governing an agent, so the vetting steps later in this guide still apply.
Three Kinds of AI Agent Marketplaces
Every catalog on a shortlist falls into one of three groups. Each group trades breadth for depth in a different way, and the right one depends on where your data already lives.
Hyperscaler marketplaces come from the cloud providers. Microsoft Marketplace, AWS Marketplace and Google Cloud Marketplace sell agents that run as SaaS, containers or APIs, and you pay through your existing cloud bill. That makes them a better fit for agents that have to work across many applications.
App-native agent stores live inside a business application. Salesforce AgentExchange, the ServiceNow Store and Oracle’s Fusion Applications AI Agent Marketplace sell agents that act on that application’s own data and permissions. Setup is faster, but the agent rarely leaves its home platform.
Open directories and independent stores such as aiagentstore.ai and aiagentsdirectory.com list agents from many vendors in one place. They help you scan the market quickly, especially early in a search. Contracts, billing and security review still happen with each vendor directly, so treat them as research tools rather than procurement channels.
Teams that would rather assemble agents from open frameworks can skip all three. Our roundups of agentic AI tools and open-source AI agents cover that route, along with the engineering effort it brings.
Kanerika Service
Agentic AI Services for Buying, Building and Connecting Agents
Kanerika helps enterprises decide which agents to buy from a marketplace, which to build, and how to connect both to their data and workflows.
Explore Agentic AI Services The Major Enterprise AI Agent Marketplaces Compared
This guide compares six enterprise marketplaces, three from the hyperscalers and three inside major business applications. The summaries below come from each vendor’s own documentation and announcements, checked in November 2026, so confirm the details again before you sign.
Microsoft Marketplace and Agent Store in Microsoft Copilot
There are two layers here. Microsoft Marketplace is the commercial catalog, which Microsoft describes as one place to find, try and buy cloud solutions and AI agents. Eligible purchases also count toward a Microsoft Azure Consumption Commitment, and private offers let you negotiate custom terms with the partner.
The second layer is Agent Store in Microsoft Copilot , where employees install agents inside Teams, Outlook, Word, Excel and PowerPoint. Before an agent reaches that store, an admin first reviews it in the Microsoft 365 admin center. The details pane shows the publisher, the agent’s capabilities and actions, a security and compliance view, and any certification or publisher attestation.
Admins then assign the agent to themselves, to specific groups or to the whole organization, and they can block or remove it later. This route suits companies already standardized on Microsoft 365 Copilot. It also fits teams that build their own agents in Copilot Studio , because both kinds of agent land in the same store.
AWS Marketplace AI Agents and Tools
AWS lists thousands of agents, tools and services from its partners. Pricing ranges from pay-as-you-go plans to contract subscriptions and negotiated custom terms. You can launch SaaS, container, AMI and API-based agent products, and the licenses sit in your AWS console with the rest of your spend.
The distinctive piece is Amazon Bedrock AgentCore. Select marketplace agents run on AgentCore Runtime, while AgentCore Gateway lets your own agents call API-based agents and tools bought there. AgentCore is a runtime rather than a second store, so ask which charges come from the agent vendor and which come from AWS infrastructure. Our note on AI agent infrastructure explains what that runtime layer does.
AWS also quotes a Forrester study it commissioned (May 2025), which reported 60% procurement time savings and 30% faster deployment for a composite customer. Still, treat those as vendor-sponsored figures, useful as a direction rather than a forecast for your own team.
Google Cloud Marketplace and Gemini Enterprise
Google also pairs a commercial marketplace with an in-app gallery. According to Google Cloud’s October 2025 announcement , Google Cloud Marketplace connects buyers with thousands of pre-vetted partner agents validated to work with Gemini Enterprise. You then buy them with your existing Google Cloud account and see one consolidated bill.
Employees spot an agent in the Gemini Enterprise Agent Gallery and ask IT to procure it. Once the purchase clears, an administrator registers the agent in Gemini Enterprise and controls access through IAM and Private Marketplace collections. The marketplace documentation also lets you filter agents by compatibility, for example A2A and Gemini Enterprise.
That A2A filter matters more than it looks. Agent2Agent is the protocol that lets agents from different vendors pass work to each other. Our comparison of MCP vs A2A explains where each protocol fits. Google ties partner onboarding directly to that standard, as the section on listing your own agent shows.
Salesforce AgentExchange
AgentExchange is Salesforce’s marketplace for apps, agents and Slack solutions. It also brings the old AgentExchange, Slack Marketplace and the Agentforce ecosystem into one hub. Admins can also plug third-party agents, sub-agents and MCP servers straight into Agentforce Builder.
Because Salesforce owns the whole stack, the buying side moves fast. Salesforce describes private offers, unified billing on your Salesforce account and automatic provisioning once an offer is accepted. Partner agents are vetted for security, and admins set data boundaries and API scopes so each agent sees only what it should.
The trade-off, however, is dependence on the Salesforce data model and licenses. AgentExchange is the natural first stop when the agent’s work starts and ends in CRM, service or Slack. Yet it is a weaker fit for an agent that has to reach deep into an ERP or a data warehouse outside Salesforce.
ServiceNow Store
ServiceNow treats its existing Store as the agent marketplace. In a January 2026 announcement , the company said a redesigned Build Program strengthens the ServiceNow Store as a global marketplace for partner-built AI agents. Partners build, test, certify and distribute those agents on the ServiceNow AI Platform.
For buyers, the appeal is that agents install into workflows you already run for IT service, HR and customer operations. The catch, though, is licensing. Check which agents your current ServiceNow subscription already covers, and also which need extra products or a partner contract.
Oracle Fusion Applications AI Agent Marketplace
Oracle launched its Fusion Applications AI Agent Marketplace in October 2025 as part of Oracle AI Agent Studio. It sits inside Fusion Applications, so finance, HR and supply chain teams install partner agent templates next to Oracle’s own pre-built agents. Deployment is one click, and each template can also be adjusted with documents, tools, prompts or APIs.
Oracle says every marketplace agent passes a validation checklist that applies the same security standards as Oracle-built agents. It also provides direct support for partner agents, covering tool usage, debugging, integration and runtime issues. That single support line makes incident ownership simpler for Fusion customers.
All Six Marketplaces Side by Side
Once you know each store, the table below shows how the six compare.
Marketplace Type Where agents run How you buy Best fit Microsoft Marketplace + Agent Store Hyperscaler + in-app store Azure, SaaS, Microsoft 365 apps Card, invoice, MACC, private offers Microsoft 365 and Azure shops AWS Marketplace Hyperscaler SaaS, container, AMI, API, AgentCore Pay-as-you-go, contracts, custom terms AWS-centered enterprises Google Cloud Marketplace Hyperscaler + in-app gallery Partner services registered in Gemini Enterprise Subscription, usage, outcome-based, private offers Google Cloud and Gemini Enterprise users Salesforce AgentExchange App-native Agentforce, Salesforce, Slack Private offers, unified Salesforce billing CRM, service and Slack workflows ServiceNow Store App-native ServiceNow AI Platform Subscription and partner terms IT, HR and service operations Oracle Fusion AI Agent Marketplace App-native Oracle Fusion Applications Templates installed in Fusion Finance, HR and supply chain on Fusion
Read the table as a map of fit rather than a ranking. Each vendor is strongest where its own platform already holds your data and your users.
Common Enterprise Use Cases by Marketplace
Each catalog leans toward the work its home platform already runs. The examples below come from the vendors’ own listing pages as well as their launch announcements.
Marketplace Typical agent jobs Microsoft Agent Store Employee assistants that work inside Teams, Outlook, Word, Excel and PowerPoint AWS Marketplace Pre-built agents for code review, sales proposals, customer support and data analysis Salesforce AgentExchange Partner agents and MCP servers that extend Agentforce in CRM, service and Slack ServiceNow Store Partner-built agents for IT service, HR and customer operations workflows Oracle Fusion AI Agent Marketplace Partner templates for sales order entry, HR records, purchase order price history and invoice collection
The pattern is easy to spot. App-native stores sell agents for one business process, while AWS sells general agents plus the tools to build more. So start from the process you want to change, and the right store usually follows.
Hyperscaler or App-Native: Which AI Agent Marketplace Fits Your Stack?
Start with the systems the agent has to read and change. If an agent updates opportunities and logs calls, the Salesforce store will connect faster than any cloud catalog. When it joins ERP, warehouse and ticketing data, a hyperscaler catalog gives you more room, because the agent can run where that data sits.
Data location comes next, since it can rule options out. Some agents run inside the SaaS application, some run as a vendor-hosted service, and some run in your own cloud account. Rule out any arrangement that breaks a residency or regulatory limit before you compare features, because no discount fixes a compliance gap later.
One Marketplace or Several?
Large companies often end up buying from more than one catalog, because their business systems come from several vendors. That is fine, so long as every purchase lands in one internal agent inventory with one approval process. Without that inventory, two departments can buy overlapping agents through two stores and nobody sees the combined spend.
Consolidating on a single preferred cloud marketplace still makes sense for agents that are not tied to one application. In practice, it keeps spend against one commitment and gives security one place to look. Our guide to enterprise AI agents covers the operating model that inventory belongs to.
AI Assessment
Check Your Readiness Before You Shortlist Agents
Kanerika’s AI Maturity Assessment scores your data foundations, generative AI and AI agent readiness, then returns recommendations you can take into a marketplace shortlist.
Take the AI Assessment → A Weighted Scorecard for Shortlisting
Score each qualifying marketplace from 1 to 5 on the criteria below, then multiply by the weight. Security and compliance minimums sit outside the score as pass or fail gates, so a low price can never offset a failed check.
Criterion Suggested weight What a 5 looks like Fit with existing systems 25% Native connectors to the systems the agent must change Security and data control 25% Scoped permissions, clear data location, admin block and remove Total cost 20% Counts toward an existing commitment, predictable usage pricing Procurement and support terms 15% Private offers, named support owner, written SLA Interoperability and exit 15% Open protocols, exportable logs and configuration
Pick the marketplace first, then pilot two or three agents inside it before a wider rollout. If the shortlist keeps pointing at custom work instead, our list of AI agent development companies is the better next read.
How to Vet an AI Agent Before You Buy It
Marketplace screening tells you the publisher is real and the package installs. But it does not tell you the agent is safe for your data, your regulators or your contracts. Those checks stay with your own security, legal and architecture teams, so run them on every agent regardless of the store.
Security and Publisher Verification
First, confirm who built the agent, which audits they hold and how they handle vulnerabilities. Then list every permission it requests, every system it can write to and how it authenticates. For example, an agent that only needs read access to one mailbox should not ask for tenant-wide scopes.
Microsoft’s admin pane is a good model of what to look for, with its separate views for data and tools, security and compliance, and certification. Our guide to agentic AI security goes deeper on threats such as prompt injection and over-broad tool access.
Data Residency and Compliance
Ask where prompts, retrieved documents, outputs and logs are processed and stored. Get retention, deletion, subprocessors and any regional limits in writing as well. Also request evidence for GDPR and your industry rules rather than relying on a badge on the listing.
Integration and Protocol Support
A listing that says “supports MCP” or “A2A compatible” only tells you what the publisher intends. Test the agent against your real systems, your identity provider and your permission model during the pilot. Connector names often match even though field mappings, rate limits and error handling differ.
Score the pilot against a fixed test set so that the result is comparable across vendors. Our framework for AI agent evaluation covers accuracy, tool use and failure handling in detail.
Support, SLAs and Version Changes
Find out who answers the phone when the agent fails, the marketplace or the publisher. Uptime targets, response times and incident reporting also belong in the contract. Ask how much notice you get before a model or prompt change alters the agent’s behavior, because a silent update can break a tested workflow. Our guide to AI agent observability shows how to catch that drift in production.
Exit Terms and Lock-In
Before you sign, check whether configurations, business rules and logs can be exported. Also ask what happens if the publisher withdraws the listing or stops support. An agent that only runs inside one SaaS platform is fine, provided you priced the cost of replacing it.
Checklist
Enterprise Agentic AI Checklist
A practical checklist for evaluating agentic AI before rollout, from use-case fit and data access to security, oversight and operations.
Get the Checklist → The checklist below turns those checks into questions procurement can send with the RFP.
Area Question for the publisher Evidence to request Security Which permissions and systems does the agent need? Permission list, audit reports Data residency Where is our data processed, stored and logged? Hosting and data processing terms Pricing What drives the monthly bill at our volume? Rate card, sample usage estimate Reliability What happens during downtime or a bad output? SLA, incident process Integration Does it work with our identity and core systems? API docs, pilot test results Change control How are model and prompt updates announced? Release notes, version policy Exit Can we leave without losing data or rules? Export format, termination terms
Keep the answers on file next to the agent’s inventory entry. Auditors will ask for them, and so will the next team that wants to reuse the same agent.
AI Agent Marketplace Pricing and the Real Cost of Ownership
The price on a marketplace listing is only the vendor’s share. Model usage, compute, integration and support sit on top of it, and for usage-heavy agents those lines can outgrow the license within a year. So build the full picture before you compare two listings on price.
Pricing Models You Will Meet
Subscription. A fixed monthly or annual fee, whether or not the agent is busy.Per user or per agent. Charges tied to assigned seats, so cost rises with headcount.Usage-based. Charges per run, request or token, which rise with adoption.Outcome-based. Charges per result, such as a ticket resolved or a report generated, which Google Cloud Marketplace supports for partners.Private offers. Negotiated prices and terms for larger or multi-year deals, available on all three hyperscalers and on AgentExchange.Usage-based and outcome-based plans look cheap in a pilot, then grow with adoption. That is why you should ask each publisher for a sample bill at your expected volume, not just the rate card.
Cloud Commitments and Consolidated Billing
Buying through a hyperscaler can put agent spend against money you have already committed. Microsoft lets eligible marketplace purchases count toward an Azure Consumption Commitment, and Google bills marketplace agents on your existing Google Cloud account. Eligibility is set per offer, though, so confirm it for each listing rather than assuming it.
Consolidated billing also helps finance teams, because one invoice per platform puts agent spend next to the compute it consumes. That view is far easier to govern than a dozen card payments to separate agent vendors.
Costs That Never Appear on the Listing
A three-year view of total cost needs six lines.
Agent license or subscription. Model and cloud usage, including tokens, compute and storage. Integration and configuration work, especially identity and data access. Vendor support and premium SLAs. Monitoring, evaluation and human review time. Exit costs, such as data export and a replacement project. Our breakdown of AI agent development cost uses the same lines for custom agents. It makes a fair baseline when you compare buying from a marketplace against building your own.
How to List and Sell Your Own AI Agent on a Marketplace
Selling an agent through a marketplace puts it in front of buyers who can purchase on an existing cloud or SaaS contract. That shortens sales cycles, but each store has its own partner program, packaging rules and fee. Treat a listing as a product launch, because buyers will vet it the way the section above describes.
Step 1: Pick the Marketplace Your Buyers Already Use
Start by listing where your target customers already buy software. An agent that acts on CRM records belongs on AgentExchange, while a cross-system analytics agent fits a hyperscaler catalog better. Then decide early whether you want a public listing, private offers only, or both.
Step 2: Join the Partner Program
Every store starts with seller registration and a commercial agreement. For example, Microsoft publishers work through Partner Center, where agent offers are grouped as Azure agents or Microsoft 365 agents. Salesforce asks you to become an ISV partner instead. ServiceNow’s redesigned Build Program also has Registered, Select, Premier and Elite tiers plus a new Access Tier for entry-stage builders.
Step 3: Package the Agent and Its Evidence
Package the agent in a format the store accepts, such as SaaS, container, API or a platform-native package. Google keeps this step lighter than most for agents sold as a service. Onboarding starts with a link to your Agent Card, which is a JSON file based on the A2A protocol. The marketplace then reads the agent’s metadata, capabilities and endpoints from it.
Alongside the package, also prepare the evidence buyers will ask for. That means a permission list, data flow description, security reports, support terms and a clear statement of what the agent will not do.
Step 4: Set Pricing and Know the Fee
Choose subscription, usage-based or private-offer pricing, then account for the marketplace’s cut. Published fees and revenue shares for five stores are below.
Marketplace Publisher route Published fee AWS Marketplace AWS Marketplace seller registration Public listings: 3% for SaaS, 20% for AMI, container and ML. Private deals: 3%, 2% or 1.5% by contract value, and 1.5% on renewals Microsoft Marketplace Partner Center 3% standard store service fee on marketplace transactions, with a 50% discount available on private renewals Google Cloud Marketplace Partner onboarding with an A2A Agent Card Standard revenue share once the product passes Google’s business case review Salesforce AgentExchange Salesforce ISV partner program Typically 15% of app revenue, or a flat annual fee for apps outside that model ServiceNow Store ServiceNow Build Program Single annual membership fee, plus partner terms
The AWS figures come from its seller listing fee guide , effective January 2024. Google’s marketplace partner requirements describe its standard revenue share, and Salesforce’s AgentExchange contract guide covers the 15% royalty. Microsoft’s fee is set out in its commercial marketplace transaction guide .
Kanerika Service
Product Engineering for Agents You Plan to Sell
Kanerika’s product engineering teams help software companies build, package and harden AI agents so they are ready for marketplace review and enterprise buyers.
Explore Product Engineering Step 5: Publish, Then Keep the Listing Honest
Next, submit the description, categories, screenshots or demo and any technical validation the store requires. But approval is where the real work starts. You still owe customers updates, vulnerability fixes, accurate listing details and notice before behavior changes.
That ongoing work is a lifecycle problem more than a sales one. Our guide to AI agent lifecycle management covers versioning, retirement and the monitoring a published agent needs.
Common Mistakes When Buying From Agent Marketplaces
The same five errors show up again and again in agent purchases.
Treating store approval as company approval. Marketplace vetting covers the store’s standards, and your own data and legal review still applies.Comparing features without comparing deployment. Two agents with the same skills can carry very different infrastructure costs and support duties.Ignoring metered pricing. Per-run and token charges grow with adoption and arrive on separate bills.Skipping the integration test. A named connector does not prove the agent works with your fields, roles and rate limits.Forgetting the exit. Unsupported agents, auto-renewals and locked data make replacement slow and expensive.Most of these trace back to treating an agent like a static app rather than a system that acts. Our list of agentic AI risks explains why agents need closer oversight than ordinary SaaS.
Talk to Kanerika
Get a Second Opinion Before You Buy
Talk to Kanerika’s agentic AI team about your marketplace shortlist, the vetting questions to send publishers and the integration work to expect.
Book a Consultation → Where AI Agent Marketplaces Are Heading
Interoperability is already shaping the catalogs more than any other trend. Google onboards partner agents through A2A Agent Cards, Salesforce lists MCP servers next to agents, and AWS sells MCP servers as agent tools. As a result, expect more listings that work across vendors, and more need to test whether they really do.
Agent buying is also folding into normal software procurement. Private offers, consolidated billing and admin controls are now standard on the hyperscalers, so agent purchases can follow the usual software approval path. As agents start calling each other across stores, AI agent orchestration and multi-agent systems will matter as much as the choice of store.
Verified listings will also matter more. Google has added a “Google Cloud Ready – Gemini Enterprise” designation for agents that meet its quality bar. Oracle also validates every marketplace agent against its own security checklist. Even so, post-purchase monitoring stays with the buyer, because an agent’s behavior can drift after the review is done.
How Kanerika Helps Enterprises Source, Integrate and Govern Marketplace Agents
Kanerika works on both sides of the marketplace. On the buying side, our agentic AI services help enterprises choose which agents to buy or build, then connect them to their data. On the publishing side, Karl, our data insights agent, and the Azure to Fabric Migration Accelerator are native Fabric workloads, reachable from any Fabric workspace.
Case Study
Up to 30% Faster Inventory Reconciliation with Karl
A UK building products manufacturer used Karl, Kanerika’s data insights agent, to read Microsoft Navision ERP data in plain language and cut time-to-insight by over 50%.
Read the Case Study → In practice, a typical marketplace engagement runs in five stages.
Assess. Map the agent jobs worth automating, together with the systems each one touches, often starting with an AI strategy review.Shortlist. Score marketplaces and candidate agents with a weighted scorecard and pass or fail gates.Pilot. Test two or three agents against real data, permissions and a fixed evaluation set.Integrate. Connect the chosen agents to identity, data platforms and existing workflows, or build a custom agent where no listing fits.Govern. Add the agents to an inventory with owners, usage limits, logs and review dates under AI governance controls.The pilot stage usually settles the buy-or-build question. When no listing passes the evaluation set, the integrate stage becomes a custom build instead.
Results From Kanerika Agent Deployments
The strongest results come when agents fit the client’s own systems. A UK building products manufacturer running Microsoft Navision used Karl to read its ERP data in plain language. After rollout, weekly inventory reconciliation got 20 to 30% faster, and time-to-insight dropped by over 50%.
A global expert network took a different route with a context-aware AI agent that matched survey requests to experts. As a result, mismatch tickets fell by 80% and mapping accuracy rose by 40%. A payment technology provider used conversational AI for brand compliance and cut approval turnaround by 60%.
Our teams watch for the same pitfalls on every engagement. They include agents with broader permissions than the job needs, usage pricing nobody modeled, and pilots scored on demos rather than real data. Kanerika holds ISO 27001 and SOC 2 Type II certifications, so security reviews start from a known baseline.
Case Study
80% Fewer Mismatch Tickets with a Context-Aware AI Agent
A global expert network used a context-aware AI agent to match survey requests to experts, raising mapping accuracy by 40% and saving 22% of team bandwidth.
Read the Case Study → Wrapping Up
An AI agent marketplace is a buying decision first and a technology decision second. Hyperscaler catalogs give you deployment choice and one cloud bill, while app-native stores give you speed inside the system the agent serves. Either way, the listing is only where your review begins.
If you also plan to sell an agent, budget for the partner program, the fee and the upkeep a live listing demands. For buyers, though, the order stays the same. Shortlist the marketplaces that match your stack, run the vetting checklist on every agent and price the full cost of ownership before you sign.
Frequently Asked Questions
What is an AI agent marketplace?
An AI agent marketplace is a curated catalog where organizations find, compare, buy and deploy pre-built AI agents from a platform vendor and its partners. Purchases run through your existing platform account, so the contract, billing and admin controls follow the agent. Microsoft, AWS, Google Cloud, Salesforce, ServiceNow and Oracle all run one.
Is there a marketplace for AI agents?
Yes, several. The hyperscalers run Microsoft Marketplace, AWS Marketplace and Google Cloud Marketplace, each with an AI agents category. Business software vendors run app-native stores such as Salesforce AgentExchange, the ServiceNow Store and Oracle’s Fusion Applications AI Agent Marketplace. Open directories also list agents, but they usually do not handle billing or security review.
Which AI agent marketplace is best for enterprises?
The best choice is the marketplace tied to the systems your agent must read and change. A CRM agent fits Salesforce AgentExchange, while a cross-system analytics agent fits a hyperscaler catalog. Score the shortlist on system fit, security, total cost, support terms and exit options, and treat compliance as a pass or fail gate.
Can enterprises buy ready-made AI agents without building them?
Yes. Pre-built agents can be bought and enabled through marketplaces, often with an admin approving which users get access. Most still need configuration, such as connecting data sources, setting permissions and adjusting prompts. Agents for a niche process or unusual data may still require a custom build or a partner-built extension.
What is the difference between Agent Store in Microsoft Copilot and Microsoft Marketplace?
Microsoft Marketplace is the commercial catalog where companies find, try and buy cloud solutions and AI agents. Agent Store in Microsoft Copilot is the in-app hub where employees install agents inside Teams, Outlook, Word, Excel and PowerPoint. Admins approve and assign agents in the Microsoft 365 admin center before they appear in Agent Store.
Is Amazon Bedrock AgentCore an AI agent marketplace?
No. AgentCore is AWS’s runtime and connection layer for running agents. AWS Marketplace is the catalog where you buy third-party agents and tools. The two connect, since select marketplace agents run on AgentCore Runtime and AgentCore Gateway lets your agents call API-based tools bought on the marketplace.
Are AI agents from marketplaces secure?
Marketplace vetting checks the publisher and the package against the store’s standards, which is a useful start. It does not confirm the agent suits your data, regulators or contracts. Review its permissions, data location, retention, audit reports and support terms yourself, then pilot it on real systems before a wider rollout.
How much does it cost to buy an AI agent from a marketplace?
Listing prices follow subscription, per-user, usage-based, outcome-based or private-offer models. The listed price is only part of the cost. Add model usage, cloud compute, integration work, support, monitoring and eventual exit costs. Ask each publisher for a sample bill at your expected volume before comparing two agents on price.
How do I sell my AI agent on a marketplace?
Pick the store your buyers already use, join its partner program and package the agent in a supported format. Prepare security and data evidence, set pricing and submit the listing for review. Microsoft uses Partner Center, Salesforce uses its ISV program, and Google onboards agents through an A2A Agent Card.
What fees do AI agent marketplaces charge sellers?
Published fees vary by store. AWS Marketplace charges 3% on public SaaS offers, 20% on AMI, container and machine learning products, and 1.5% to 3% on private offers. Microsoft charges a 3% standard store service fee on transact offers. Check current partner terms for Google, Salesforce and ServiceNow before pricing.
