Anthropic Claude Memory vs Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank)
Anthropic Claude Memory vs Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank): side-by-side comparison of two platform-provider memory systems — architecture, taxonomy, license, pricing, MCP/A2A support, and direct edges.
Anthropic Claude Memory · Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank)
Cost & capability
| Anthropic Claude Memory | Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | |
|---|---|---|
| Capability band | frontier | frontier |
| Capability composite | 90 | 87 |
| Cost tier | free | — |
| $/Mtok input | 0 | — |
| $/Mtok output | 0 | — |
| Use cases | Long Running Session, Memory Augmented Chat, Analytical Summarization | Long Running Session, Multi Agent Coordination, Scoped Agentic |
Where they differ (15)
Rows where both sides have data and the values disagree — the shortlist of dimensions that actually distinguish these two systems.
| Anthropic Claude Memory | Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | |
|---|---|---|
| Capability composite | 90 | 87 |
| Use cases | Long Running Session, Memory Augmented Chat, Analytical Summarization | Long Running Session, Multi Agent Coordination, Scoped Agentic |
| Type | File-backed + tool-driven + Auto Dream | Long-running agent memory with continuous event-streaming consolidation |
| Created | 2024-09 (Enterprise/Team rollout); 2026-03-02 (all users incl. free) | 2025-07 (Vertex Memory Bank Preview); 2026-04-22 (GA + rebrand to Gemini Enterprise Agent Platform) |
| Pricing | Free + paid | Pay-per-use (Vertex/Gemini Enterprise consumption-based) |
| Funding | Anthropic total $12.4B+ raised; $40B valuation (Series E+ 2025) | Google/Alphabet public (GOOGL); ~$2T+ market cap |
| Backend storage | custom (Anthropic-managed) | custom (Google-managed; Spanner-class backend) |
| Deployment | Managed-only | Managed cloud (GCP); on-prem option via open-source Gemma 4 |
| API surface | REST (Anthropic API), SDK: Python, TS | REST, gRPC, SDK: Python, Java, Node.js, Go |
| Multi-tenancy | hard-isolation (workspace) | hard-isolation (per-tenant + per-project) |
| MCP | native (first-party) — Claude apps consume MCP | via official adapter (ADK + managed MCP servers for Maps/BigQuery/Compute Engine/Kubernetes; Apigee bridge) |
| A2A | not supported | supported — A2A v1.2 production at 150 organizations; built into ADK/LangGraph/CrewAI/LlamaIndex |
| OpenTelemetry | no — consumer product | first-class — Cloud Trace / OTel |
| Optimised for | user-friendly persistent memory + Auto Dream consolidation | long-running enterprise agents with continuous event-streaming consolidation; days-long state persistence; deep GCP IAM/CMEK/A2A integration |
| Anti-fit | not for fine-grained programmatic memory control - opaque consumer feature | not for non-GCP stacks; not for OSS/self-host requirements; not for users requiring full memory transparency (managed service) |
At a glance
| Anthropic Claude Memory | Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) | |
|---|---|---|
| Section | Platform-provider memory | Platform-provider memory |
| Tier | T1 | T1 |
| Type | File-backed + tool-driven + Auto Dream | Long-running agent memory with continuous event-streaming consolidation |
| Created | 2024-09 (Enterprise/Team rollout); 2026-03-02 (all users incl. free) | 2025-07 (Vertex Memory Bank Preview); 2026-04-22 (GA + rebrand to Gemini Enterprise Agent Platform) |
| Pricing | Free + paid | Pay-per-use (Vertex/Gemini Enterprise consumption-based) |
| Funding | Anthropic total $12.4B+ raised; $40B valuation (Series E+ 2025) | Google/Alphabet public (GOOGL); ~$2T+ market cap |
| Backend storage | custom (Anthropic-managed) | custom (Google-managed; Spanner-class backend) |
| Deployment | Managed-only | Managed cloud (GCP); on-prem option via open-source Gemma 4 |
| API surface | REST (Anthropic API), SDK: Python, TS | REST, gRPC, SDK: Python, Java, Node.js, Go |
| Embedding | — | multiple supported (Gemini text-embedding-004 + customer-supplied) |
| Multi-tenancy | hard-isolation (workspace) | hard-isolation (per-tenant + per-project) |
| MCP | native (first-party) — Claude apps consume MCP | via official adapter (ADK + managed MCP servers for Maps/BigQuery/Compute Engine/Kubernetes; Apigee bridge) |
| A2A | not supported | supported — A2A v1.2 production at 150 organizations; built into ADK/LangGraph/CrewAI/LlamaIndex |
| OpenTelemetry | no — consumer product | first-class — Cloud Trace / OTel |
| Optimised for | user-friendly persistent memory + Auto Dream consolidation | long-running enterprise agents with continuous event-streaming consolidation; days-long state persistence; deep GCP IAM/CMEK/A2A integration |
| Anti-fit | not for fine-grained programmatic memory control - opaque consumer feature | not for non-GCP stacks; not for OSS/self-host requirements; not for users requiring full memory transparency (managed service) |
Taxonomy
| Axis | Anthropic Claude Memory | Gemini Enterprise Agent Platform Memory Bank (rebrand of Vertex AI Memory Bank) |
|---|---|---|
| storage | file | vector |
| retrieval | extraction-pull | similarity |
| persistence | long-term | long-term |
| update | consolidation | consolidation |
| unit | fact | fact |
| governance | user-controllable | user-controllable |
| conflict | manual | llm |
Pros & cons
Anthropic Claude Memory
Pros: Three-tier model (memory tool API + consumer Memory + Auto Dream) covers developer, user, and system layers; document-as-memory unit aligns with how humans organize information.
Cons: Three-tier model means developers and users see different abstractions; Auto Dream consolidation is not user-controllable.
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.