Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) vs LinkedIn Cognitive Memory Agent
Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) vs LinkedIn Cognitive Memory Agent: side-by-side comparison of two platform-provider memory systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) · LinkedIn Cognitive Memory Agent
Cost & capability
| Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | LinkedIn Cognitive Memory Agent | |
|---|---|---|
| Capability band | frontier | competent |
| Capability composite | 87 | 72 |
| Cost tier | — | searched not found |
| $/Mtok input | — | searched not found |
| $/Mtok output | — | searched not found |
| Use cases | Long Running Session, Multi Agent Coordination, Scoped Agentic | Multi Agent Coordination, Long Running Session, Scoped Agentic |
Where they differ (17)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | LinkedIn Cognitive Memory Agent | |
|---|---|---|
| Capability band | frontier | competent |
| Capability composite | 87 | 72 |
| Use cases | Long Running Session, Multi Agent Coordination, Scoped Agentic | Multi Agent Coordination, Long Running Session, Scoped Agentic |
| Type | Long-running agent memory with continuous event-streaming consolidation | Episodic + semantic + procedural (shared) |
| Created | 2025-07 (Vertex Memory Bank Preview); 2026-04-22 (GA + rebrand to Gemini Enterprise Agent Platform) | 2026-04 (engineering blog published April 2026; Hiring Assistant shipped 2025) |
| Pricing | Pay-per-use (Vertex/Gemini Enterprise consumption-based) | searched not found |
| Funding | Google/Alphabet public (GOOGL); ~$2T+ market cap | parent is public |
| Backend storage | custom (Google-managed; Spanner-class backend) | searched not found |
| Deployment | Managed cloud (GCP); on-prem option via open-source Gemma 4 | Managed-only (internal LinkedIn production infrastructure) |
| API surface | REST, gRPC, SDK: Python, Java, Node.js, Go | searched not found |
| Embedding | multiple supported (Gemini text-embedding-004 + customer-supplied) | searched not found |
| Multi-tenancy | hard-isolation (per-tenant + per-project) | searched not found |
| MCP | via official adapter (ADK + managed MCP servers for Maps/BigQuery/Compute Engine/Kubernetes; Apigee bridge) | searched not found |
| A2A | supported — A2A v1.2 production at 150 organizations; built into ADK/LangGraph/CrewAI/LlamaIndex | searched not found |
| OpenTelemetry | first-class — Cloud Trace / OTel | searched not found |
| Optimised for | long-running enterprise agents with continuous event-streaming consolidation; days-long state persistence; deep GCP IAM/CMEK/A2A integration | episodic+semantic+procedural memory at LinkedIn scale |
| Anti-fit | not for non-GCP stacks; not for OSS/self-host requirements; not for users requiring full memory transparency (managed service) | not deployable - internal LinkedIn infrastructure only |
At a glance
| Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | LinkedIn Cognitive Memory Agent | |
|---|---|---|
| Section | Platform-provider memory | Platform-provider memory |
| Tier | T1 | T1 |
| Type | Long-running agent memory with continuous event-streaming consolidation | Episodic + semantic + procedural (shared) |
| Created | 2025-07 (Vertex Memory Bank Preview); 2026-04-22 (GA + rebrand to Gemini Enterprise Agent Platform) | 2026-04 (engineering blog published April 2026; Hiring Assistant shipped 2025) |
| Pricing | Pay-per-use (Vertex/Gemini Enterprise consumption-based) | searched not found |
| Funding | Google/Alphabet public (GOOGL); ~$2T+ market cap | parent is public |
| Backend storage | custom (Google-managed; Spanner-class backend) | searched not found |
| Deployment | Managed cloud (GCP); on-prem option via open-source Gemma 4 | Managed-only (internal LinkedIn production infrastructure) |
| API surface | REST, gRPC, SDK: Python, Java, Node.js, Go | searched not found |
| Embedding | multiple supported (Gemini text-embedding-004 + customer-supplied) | searched not found |
| Multi-tenancy | hard-isolation (per-tenant + per-project) | searched not found |
| MCP | via official adapter (ADK + managed MCP servers for Maps/BigQuery/Compute Engine/Kubernetes; Apigee bridge) | searched not found |
| A2A | supported — A2A v1.2 production at 150 organizations; built into ADK/LangGraph/CrewAI/LlamaIndex | searched not found |
| OpenTelemetry | first-class — Cloud Trace / OTel | searched not found |
| Optimised for | long-running enterprise agents with continuous event-streaming consolidation; days-long state persistence; deep GCP IAM/CMEK/A2A integration | episodic+semantic+procedural memory at LinkedIn scale |
| Anti-fit | not for non-GCP stacks; not for OSS/self-host requirements; not for users requiring full memory transparency (managed service) | not deployable - internal LinkedIn infrastructure only |
Taxonomy
| Axis | Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | LinkedIn Cognitive Memory Agent |
|---|---|---|
| storage | vector | vector |
| retrieval | similarity | similarity |
| persistence | long-term | long-term |
| update | consolidation | extraction |
| unit | fact | episode |
| governance | user-controllable | opaque |
| conflict | llm | none |
Pros & cons
Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank)
Pros: GA at Next 2026 with hardened operational story (event-streaming consolidation; long-running agents over days; A2A v1.2 in production); deep ecosystem integrations (Box/Workday/Salesforce/ServiceNow); first-class OTel + Cloud Trace observability.
Cons: GCP-only; Memory Profiles feature reported in TheNextWeb but not surfaced in official release notes (treated here as unconfirmed); pricing tied to Vertex consumption; smaller mind-share than AWS / Anthropic memory in OSS communities.
LinkedIn Cognitive Memory Agent
Pros: First professional-network memory with a structured domain corpus (job history, connections, posts) — most other products see only freeform chat.
Cons: Closed ecosystem with no developer access; memory depth limited by what users actually post.