Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) vs Google Gemini Memory
Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) vs Google Gemini Memory: 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) · Google Gemini Memory
Cost & capability
| Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | Google Gemini Memory | |
|---|---|---|
| Capability band | frontier | frontier |
| Capability composite | 87 | 88 |
| Cost tier | — | free |
| $/Mtok input | — | 0 |
| $/Mtok output | — | 0 |
| Use cases | Long Running Session, Multi Agent Coordination, Scoped Agentic | Memory Augmented Chat, Long Running Session |
Where they differ (14)
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) | Google Gemini Memory | |
|---|---|---|
| Capability composite | 87 | 88 |
| Use cases | Long Running Session, Multi Agent Coordination, Scoped Agentic | Memory Augmented Chat, Long Running Session |
| Type | Long-running agent memory with continuous event-streaming consolidation | Personal Context + Personal Intelligence |
| Created | 2025-07 (Vertex Memory Bank Preview); 2026-04-22 (GA + rebrand to Gemini Enterprise Agent Platform) | 2024-11-20 (initial rollout to Gemini Advanced subscribers) |
| Pricing | Pay-per-use (Vertex/Gemini Enterprise consumption-based) | Free + paid |
| Funding | Google/Alphabet public (GOOGL); ~$2T+ market cap | Google/Alphabet public (GOOGL); no separate Gemini funding |
| Backend storage | custom (Google-managed; Spanner-class backend) | custom (Google-managed) |
| Deployment | Managed cloud (GCP); on-prem option via open-source Gemma 4 | Managed-only |
| Multi-tenancy | hard-isolation (per-tenant + per-project) | hard-isolation |
| MCP | via official adapter (ADK + managed MCP servers for Maps/BigQuery/Compute Engine/Kubernetes; Apigee bridge) | via official adapter — Gemini supports MCP via ADK / extensions |
| A2A | supported — A2A v1.2 production at 150 organizations; built into ADK/LangGraph/CrewAI/LlamaIndex | supported — Google originated A2A |
| OpenTelemetry | first-class — Cloud Trace / OTel | no — consumer product |
| Optimised for | long-running enterprise agents with continuous event-streaming consolidation; days-long state persistence; deep GCP IAM/CMEK/A2A integration | Google ecosystem personal context (Gmail, Drive, Calendar) |
| Anti-fit | not for non-GCP stacks; not for OSS/self-host requirements; not for users requiring full memory transparency (managed service) | not for non-Google ecosystems |
At a glance
| Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | Google Gemini Memory | |
|---|---|---|
| Section | Platform-provider memory | Platform-provider memory |
| Tier | T1 | T1 |
| Type | Long-running agent memory with continuous event-streaming consolidation | Personal Context + Personal Intelligence |
| Created | 2025-07 (Vertex Memory Bank Preview); 2026-04-22 (GA + rebrand to Gemini Enterprise Agent Platform) | 2024-11-20 (initial rollout to Gemini Advanced subscribers) |
| Pricing | Pay-per-use (Vertex/Gemini Enterprise consumption-based) | Free + paid |
| Funding | Google/Alphabet public (GOOGL); ~$2T+ market cap | Google/Alphabet public (GOOGL); no separate Gemini funding |
| Backend storage | custom (Google-managed; Spanner-class backend) | custom (Google-managed) |
| Deployment | Managed cloud (GCP); on-prem option via open-source Gemma 4 | Managed-only |
| API surface | REST, gRPC, SDK: Python, Java, Node.js, Go | — |
| Embedding | multiple supported (Gemini text-embedding-004 + customer-supplied) | — |
| Multi-tenancy | hard-isolation (per-tenant + per-project) | hard-isolation |
| MCP | via official adapter (ADK + managed MCP servers for Maps/BigQuery/Compute Engine/Kubernetes; Apigee bridge) | via official adapter — Gemini supports MCP via ADK / extensions |
| A2A | supported — A2A v1.2 production at 150 organizations; built into ADK/LangGraph/CrewAI/LlamaIndex | supported — Google originated A2A |
| OpenTelemetry | first-class — Cloud Trace / OTel | no — consumer product |
| Optimised for | long-running enterprise agents with continuous event-streaming consolidation; days-long state persistence; deep GCP IAM/CMEK/A2A integration | Google ecosystem personal context (Gmail, Drive, Calendar) |
| Anti-fit | not for non-GCP stacks; not for OSS/self-host requirements; not for users requiring full memory transparency (managed service) | not for non-Google ecosystems |
Taxonomy
| Axis | Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | Google Gemini Memory |
|---|---|---|
| storage | vector | kv |
| retrieval | similarity | injection |
| persistence | long-term | long-term |
| update | consolidation | extraction |
| unit | fact | fact |
| governance | user-controllable | user-controllable |
| conflict | llm | llm-arbitrate |
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.
Google Gemini Memory
Pros: Tightest integration of any memory product with personal data sources (Gmail, Drive, Calendar) where users have meaningful history.
Cons: Cross-product data sharing is a trust liability; persistence model has been less clearly communicated than ChatGPT's.