Memory is what makes or breaks enterprise chat. Using your AI assistant will feel like a toy, not a production system, if it forgets who the user is, loses context between tickets, or asks the same clarifying questions every time. Naive methods that transmit the whole conversation history into every prompt, on the other hand, raise token prices and delay.
This is when dedicated memory frameworks come in.
In this post, I’ll show you a useful way to make decisions about corporate memory, compare Mem0 with Zep, and talk about when it makes sense to develop your own memory layer. I will talk about cost, architecture, security and compliance, and realistic timetables for rolling out an enterprise.
What “memory management” truly implies for business discussion
A memory system for a typical business chat assistant or agent needs to do more than just “store previous messages in a database.”
You normally have to:
1. Keep user-specific context across sessions, such as preferences, past decisions, account details, limitations, and escalation history. Kept for weeks or months, not just one chat.
2. Get the important things, not everything. Pull out the most important details from vast chat histories. Get the proper snippets at inference time to keep the model’s window in the right place and keep token costs down.
3. Link chat to business information like orders, tickets, CRM records, knowledge articles, IoT data, or transaction data. Often needs more than just ordinary embeddings; it needs graph-style relationships and temporal reasoning.
4. Follow the rules for security and compliance, such as data residency, encryption, PII masking, RBAC or ABAC access, and audit trails. Work with your current IAM and network limits.
5. Work on a large scale with tens of thousands or hundreds of thousands of users. Thousands of communications every month, with costs and performance that can be predicted.
Most teams end up in the same place: you need a separate “memory layer” between your chat front end and your LLM provider.
Option 1: Using Mem0
Mem0 is a controlled and open-source “universal memory layer” for LLM apps that takes important information from chats and other sources and stores it to use for long-term memory and customisation. You can get it as:
• You can host it yourself using the Apache 2.0 open source stack. (docs.mem0.ai) • Managed the Mem0 Platform, which charges based on usage and takes care of infrastructure and operations for you. (docs.mem0.ai)
Mem0 is used in a lot of agent frameworks and integrations, like Microsoft Autogen. It focuses on memory extraction, consolidation, and retrieval. (Microsoft GitHub)
Good things
• An OSS project that is grown up and busy: A large and expanding community, regular commits, and a lot of stars and forks show that people are using it. (GitHub) • Make the open source vs. platform story clear: The OSS version is free to use and is based on Apache 2.0. The platform includes tools for hosting, managing, and running a business. The documents clearly compare the costs of infrastructure between OSS and platform. (docs.mem0.ai)
• Made to save tokens and time: Mem0 only pulls out the important data instead of playing back the whole history, which can save a lot of tokens. (arXiv) • Works well for “personalized assistant” situations: Customer support bots, internal copilots, and multi-session assistants that consider user traits and preferences. (GitHub)
Weaknesses and trade-offs
• Not as set in their ways about graph and corporate data modeling: You can connect Mem0 to RAG or your own graph, but it is not a complete graph knowledge platform on its own. You will need to connect other systems if you want to run rich graph queries across business entities.
• You still own the infrastructure for OSS. When you host your own site, you pay for the vector database, LLM calls, and hosting. (docs.mem0.ai) • Choosing between a vendor platform and doing it yourself: If you choose the managed platform, you will have to make sure it fits with your data residency, DPA, and security needs.
Where Mem0 usually works best
• You want to get value quickly and don’t want to build a graph stack that is too complicated on the first day.
• Your main goals are to personalize several sessions, lower costs, and improve memory in chat.
• You can either host your own Apache 2.0 stack or sign up for a SaaS memory platform that is made just for you.
Option 2: Using Zep
Zep is a “context engineering platform” that builds on a temporal knowledge graph by adding agent memory, Graph RAG, and context assembly. (getzep.com)
Graphiti is a Python framework that lets you design temporally aware knowledge graphs that show how entities, events, and relationships change over time. (help.getzep.com)
Zep began with an open-source community edition of Apache 2.0 and now mostly works on a managed enterprise platform. The OSS repo is still available, but it is not being actively updated anymore. (Reddit)
Good points
• Good at reasoning with graphs and time
Zep models user memories and business data as a temporal knowledge graph and has published results suggesting that it is more accurate and has less latency than baseline methods on long-term memory benchmarks. (arXiv)
• Features and certifications that are good for businesses
The business platform has SOC 2 Type II, DPAs for EU customers, HIPAA BAA on enterprise plans, and a number of deployment choices, such as managed, BYOK, and others. (getzep.com)
• Good connections to other parts of the ecosystem
It’s easy to add agent memory to existing stacks thanks to integrations with LangChain and other tools. (LangChain Docs)
• Integrations with cloud providers
For instance, Zep can use Amazon Neptune and OpenSearch as storage for graph and text search to help it remember enterprise data for a long time. (Amazon Web Services, Inc.)
Weaknesses and trade-offs
• The open-source tale is now “static.”
The open-source community edition is Apache 2.0, however it is no longer being worked on. That means that your team may have to do more work to keep Zep running if you host it yourself for a long time.
• More complicated than you might need for simple assistance. The complete graph centric context engineering stack can be too much if all you need are memories of user preferences and short conversations.
• Focus on business: It’s evident that the documentation and support are better for the SaaS product. If your business simply hosts itself, think about this carefully.
Where Zep typically works best
• You care about having strong relationships and being able to reason about time across chat and corporate data.
• You want a managed, enterprise-level platform with SLAs and certifications. • You plan to make several agents that all use the same knowledge graph.
Option 3: Making your own memory layer
The third choice is to make your own memory system, which you can do by combining:
• An embedding vector database.
• A relational or document database for facts that are organized.
• Application logic for getting, summarizing, scoring, and getting back. • A layer of rules for handling PII, tenancy, and RBAC.
Why teams think about this
• You want to oversee all the data, logic, and deployment.
• You already have a lot of experience with ML and platform engineering in-house. • You want memory to work well with current data platforms and internal standards.
Pros
• Full control over data and architecture
You can change how memories are taken out, combined, versioned, and erased. You can use the backups, observability, and disaster recovery plans you already have.
• Works with the internal tech stack
Instead of getting a new vendor, you can build on top of certified databases, message buses, and security measures.
• No surprises with features
Your roadmap is for you alone. No need to rely on decisions made by other products.
Drawbacks
• A lot of money is spent on engineering
To get Mem0 or Zep to work like they do, you need to add: Entity and fact extraction, deduplication, and scoring. Reasoning about time and across sessions. Isolation between tenants, retention procedures, and legal holds.Tools for debugging memory for administrators and observers.
• Cost of ongoing maintenance
You will always own schema migrations, scaling, performance optimization, and security patches.
• It’s harder to compare to the best of the best: Vendors are continually putting out studies and benchmarks on the cost and performance of long-term memory. It is not easy to do this internally.
“Build your own” only makes sense for most businesses if they are making a strategic internal platform and not just one chatbot.
Comparing costs
Things to think about when it comes to cost: not just the price of the license and SaaS When you compare Mem0, Zep, or a custom build, think about the cost in three ways:
1. Cost of the platform and infrastructure
a. Mem0 OSS
i. License: Apache 2.0, no cost.
ii. You pay for hosting, vector DB, and LLM calls. (docs.mem0.ai)
b. Mem0 Platform
i. This is a usage-based SaaS pricing model that includes infrastructure in the platform. (docs.mem0.ai)
c. Zep SaaS
i. There is a free tier, a credit-based Flex plan, and corporate options. Managed deployment, BYOK, and choices for businesses. (getzep.com) d. Build your own
i. You pay for your own computing, storage, networking, monitoring, and backup.
ii. If you use business vector DBs or graph databases, you may need to get more licenses.
2. Cost of tokens and computing at inference
a. Better memory systems send only the necessary context instead of whole chat logs, which lowers the number of tokens needed for each request. Both Mem0 and Zep stress latency and token savings in their message and research findings.
b. Custom systems can do the same thing, but only if you spend money on strong logic for summarizing and retrieving information.
3. Cost of engineering and running the business
a. Putting together an off-the-shelf memory platform can take weeks of work. b. Building a strong custom layer can take anything from a few months to a few years, including continuous support.
A solid rule of thumb is to use vendor platforms when you want to get the most value for your time and trustworthiness.
• Use self-hosted OSS if you want to save money on SaaS and have control, but are okay with owning the infrastructure.
• Only build your own when memory is a key feature of your internal platform.
Timelines: How long does each path normally take?
The actual timescales will depend on your company, but this is a reasonable plan for rolling out an enterprise chat.
Using Mem0 or Zep
• Week 1 to 2: Proof of concept
o Connect the chat app to Mem0 or Zep memory APIs so that it can be used as a single assistant.
o Keep user profiles and basic preferences between sessions.
o Check the effect on latency and token reduction.
• Week 3 to 6: Try it out in one area
o Include more structured entities and business information.
o Set up basic guardrails and monitoring.
o Check with internal users or just one business unit.
• From the third to the sixth month, production will grow.
o Make security, SSO, and RBAC stronger.
o Set rules for how long data should be kept and when it should be deleted. o Spread memory across several helpers and business lines.
Making your own memory layer
• Month 1 to 2: Alpha and architecture
o Choose databases, establish schemas, and set up memory abstractions. o Set up basic extraction and retrieval for one case.
• From months 3 to 5: Beta and integrations
o Include temporal, deduplication, and summarization elements.
o Work with IAM, observability, and at least one LLM app.
• Month 6 and after: getting stronger and changing the time
o Make sure that performance, cost, and accuracy are as good as they can be. o Make tools for administrators, audit views, and governance workflows. o Think of the memory layer as a product with its own backlog.
A vendor-backed memory framework is usually the best way to get results this quarter for your business.
A brief list of things to think about before choosing
You can use these questions to get your internal architectural conversation going:
1. Is long-term memory a strategic platform capability or just a feature?
a. If your assistants have this functionality, choose Mem0 or Zep.
b. If you’re making a “agent platform” for the full company, a custom build might be worth it.
2. Do you need to be able to reason at the graph level and in other domains right now?
a. A lot of graph and temporal reasoning across chat and business data points to Zep or a custom graph solution.
b. If not, it could be easier to use Mem0’s simplified mental model.
3. What are the limits on your compliance and deployment?
a. If you can’t utilize SaaS at all, Mem0 OSS is better than Zep OSS, which is basically not being updated.
b. If DPAs, SOC 2, and BYOK are allowed with SaaS, then both Mem0 Platform and Zep Enterprise are possible choices.
4. What skills do you have inside your company?
a. A bespoke memory layer may be handled by strong ML and distributed systems teams.
b. Don’t underestimate how much work your team must do if they are already busy.
5. What is your budget for tokens and latency?
a. Validate your choice with real workloads and compare token savings and latency side by side.
Final thoughts
Memory is no longer just a “nice to have” feature for business discussion. It is the most important part of an assistant’s experience that is reliable, personable, and affordable.
• Mem0 is a wonderful solution if you want an open-source memory layer that is versatile, actively maintained, and has an optional managed platform that focuses on personalization, cost reduction, and ease of use.
• Zep is the best choice when you need a complex temporal knowledge graph and a managed enterprise platform that makes context engineering a top priority. • Building your own is powerful but costly, so only enterprises that really want to own memory as a platform capability should do it.
Author: Milankumar Rana