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# Weaviate vs Supermemory: Database, Engram, and Agent Memory

Compare Weaviate Database, Engram, and Supermemory by application needs, processing behavior, pricing inputs, deployment, and migration tests.

By Shardul Mane April 28, 2026 · 5 min read 

![Weaviate vs Supermemory: Database, Engram, and Agent Memory](https://supermemory.ai/_astro/cover.B2s31pg0_Z25dEsK.webp)

Compare Weaviate and Supermemory at the product boundary you actually need. Weaviate Database is a foundation for storing and retrieving data. Weaviate Engram is a separate memory offering. Supermemory provides memory APIs, ingestion, retrieval, and user context.

Keep Weaviate when its retrieval capabilities fit your application and you can maintain the remaining memory behavior. Evaluate Engram or Supermemory when you want a service to handle more of that behavior. This article is published by Supermemory and compares the documented capabilities of these three offerings.

## Weaviate Database, Engram, and Supermemory compared

| Choice            | What you are evaluating                                           | Application work to examine                                                                  |
| ----------------- | ----------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
| Weaviate Database | Data storage and retrieval infrastructure                         | Memory selection, lifecycle policy, application identity, and context assembly               |
| Weaviate Engram   | A memory service built within the Weaviate ecosystem              | Integration, scope mapping, processing readiness, and workflow-specific acceptance tests     |
| Supermemory       | Memory ingestion, retrieval, profiles, and supported integrations | The same application identity, permissions, source-of-truth, and evaluation responsibilities |

The question is not whether one product has more marketing features. It is which implementation passes your workload with acceptable cost and operating effort.

## What does Weaviate Database provide?

Weaviate documents [hybrid search](https://docs.weaviate.io/weaviate/search/hybrid), which combines keyword and vector retrieval, and [multi-tenancy](https://docs.weaviate.io/weaviate/manage-collections/multi-tenancy). These features support filtered retrieval for agent applications.

A team can store conversations and source documents, attach metadata, and build a memory application on that foundation. The remaining design work includes interpreting corrections, maintaining useful context, and ensuring every operation uses the right permissions.

For example, finding two similar statements does not settle whether one supersedes the other or applies only to a different project. That policy can be implemented above the database. The [memory versus vector database guide](https://supermemory.ai/blog/ai-memory-vs-vector-databases-complete-guide/) explains the boundary without assuming the database cannot support it.

## What does Engram add?

The [Engram documentation](https://docs.weaviate.io/engram) describes a memory pipeline that extracts information, transforms it using existing context, and commits the result. Processing is asynchronous, with run status available to track completion. Search supports vector, keyword, and hybrid approaches.

Its [concepts documentation](https://docs.weaviate.io/engram/concepts) distinguishes groups, topics, and scopes. Topics describe what to remember; scopes define the relevant visibility boundary. Configurable pipelines have plan-specific availability, so verify the plan you intend to test.

Test how those scopes and processing stages handle corrections, deletion and access changes in your application.

## Where does Supermemory fit?

Supermemory is an option when you want ingestion, retrieval, and reusable user context integrated into an existing application. Its [profiles](https://supermemory.ai/docs/concepts/user-profiles) complement targeted retrieval; its [connector documentation](https://supermemory.ai/docs/connectors/overview) identifies supported source-specific setup paths.

Check the source formats and integration you need. Do not infer a managed native connector from an example that manually imports content through an API. In particular, an application that reads Slack messages is not automatically evidence of a managed Slack connector.

Your application can coordinate a document index and a memory service, but that is an application architecture. Do not assume a documented bring-your-own-Weaviate storage adapter exists for Supermemory. If you maintain both stores, specify identifiers, update propagation, deletion, and which system answers each kind of question.

## How should you compare pricing?

Use current product-specific pricing and the same workload. Start with [Weaviate's pricing information](https://weaviate.io/pricing) and [Supermemory's rate card](https://supermemory.ai/pricing/), then confirm the selected service, plan, and metering units. A database starting price is not a quote for every related memory product.

Count ingestion and changes, memory searches, model-context tokens, storage, and any additional services. Add the engineering work that remains on each side. Estimate each additional component from the same workload.

An illustrative pilot could contain 10,000 source records, 1,000 changed records per month, and 100,000 retrieval requests. Those are workload inputs, not a cost estimate. Obtain a comparable bill for each implementation before claiming savings. The [operating-cost guide](https://supermemory.ai/blog/hidden-cost-building-llm-memory-in-house/) shows how retries and context size affect that calculation.

## Can you compare self-hosting and enterprise controls directly?

Only after identifying the product and deployment. Self-hosting Weaviate Database does not establish an identical self-hosted Engram offering. Likewise, a local Supermemory option should not be assumed to have every managed or enterprise capability.

Record the proposed deployment, data locations, model calls, access controls, support, retention, and export behavior. Confirm contractual or compliance requirements for that specific arrangement. A logo or a generic security claim does not answer which obligations apply to your implementation.

## Run a migration-sized experiment first

Select one real workflow and a small source collection. Preserve original source IDs, timestamps, and permissions. If you compare Weaviate-based retrieval, Engram, and Supermemory, keep the answering model and questions consistent while recording each system's retrieval configuration.

Include a returning user, a corrected fact, a deleted document, a revoked permission, and an unavailable dependency. Measure when accepted content becomes searchable. Separate retrieval time from full answer time, and record costs for the same successful tasks.

If a migration is justified, test both ingestion and export before planning the cutover. Keep an application-level switch or another rollback path while validating the new flow. Use the sample migration to estimate the full cutover and validation work.

## Which option should you choose?

Choose Weaviate Database when the retrieval foundation meets your needs and you want to own the memory policy above it. Evaluate Engram when its memory workflow fits your Weaviate-based stack. Evaluate Supermemory when its ingestion, profiles, and retrieval remove useful work from your application.

Use the [memory API shortlist](https://supermemory.ai/blog/best-memory-apis-stateful-ai-agents/) to compare the broader category and [MemoryBench](https://supermemory.ai/blog/build-agent-memory-eval-harness/) to organize a reproducible test. Use the results to choose the implementation that meets your quality and operating requirements.

To compare a managed memory workflow with your Weaviate setup, [try Supermemory on the same test cases](https://console.supermemory.ai/). Measure the returned evidence and the application code you still need before deciding whether to add or replace a component.

## Other posts.

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6. [How Chatarmin Ditched RAG and Went Memory-Only with Supermemory Chatarmin replaced a heavy RAG pipeline with Supermemory's memory layer — cutting average AI response time from 40s to 12s and token usage by 40–50%. Case StudyJun 10, 2026 ](https://supermemory.ai/blog/chatarmin-memory-only)
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17. [Building code-chunk: AST Aware Code Chunking At Supermemory, we're building context engineering infrastructure for AI. A huge part of that is dealing with code: ingesting repos, understanding structure, and making it searchable. The problem is that most code chunking solutions are terrible. We built code-chunk to fix this. EngineeringDec 29, 2025 ](https://supermemory.ai/blog/building-code-chunk-ast-aware-code-chunking)
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19. [Mem0 vs Supermemory: Why Scira Switched Scira AI moved its production memory layer from Mem0 to Supermemory. This is what failed, what improved, and how the team evaluated the two systems. Case StudyOct 2, 2025 ](https://supermemory.ai/blog/why-scira-ai-switched)
20. [Never Record Again: How Montra Uses Supermemory to Rethink Video Creation Campbell Baron, the founder of Montra, has been making videos since he was twelve. By thirteen, he was already doing brand work. Today, he’s betting on a very different future for creators: a world where recording is the exception, and most videos are generated from scratch. Case StudyAug 21, 2025 ](https://supermemory.ai/blog/never-record-again-how-montra-uses-supermemory-to-rethink-video-creation)
21. [Unified Memory That Works Where You Work: Your Second Brain With Supermemory Hi everyone, I’m Dhravya, the founder of Supermemory. I want to start with a little story behind why this product means so much to me. You can also skip straight to what it is and how it works below. EngineeringJul 25, 2025 ](https://supermemory.ai/blog/unified-memory-that-works-where-you-work-your-second-brain-with-supermemory)
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26. [The UX and technicalities of awesome MCPs Last month, we launched the Supermemory MCP, mostly to test our own infrastructure and get some initial traction. It blew up. To my absolute surprise, the initial launch itself got half a million impressions (!!!). Then, we launched and got #2 on ProductHunt too. EngineeringJun 8, 2025 ](https://supermemory.ai/blog/the-ux-and-technicalities-of-awesome-mcps)
27. [Architecting a memory engine inspired by the human brain Language is at the heart of intelligence, but what truly powers meaningful interaction is memory — the ability to accumulate, recall, and contextualize information over time. Large Language Models (LLMs) have mastered language, but memory remains their Achilles’ heel. EngineeringJun 5, 2025 ](https://supermemory.ai/blog/memory-engine)

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