// Private-cloud AI memory framework
Cloud marketplace or private cloud. Same controlled memory plane.
GreatMemory packages retrieval, guardrails, audit, provider management, and a starter chat UI into one controlled workspace. Run it from a cloud marketplace listing or in your private cloud, with your data, database, network boundary, and LLM keys under your control. Multi-workspace support is on the roadmap for users who need access to multiple data estates.
Live answer over governed workspace knowledge.
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single workspace now
// Deployment posture
A simple choice: marketplace install or private-cloud service.
Cloud marketplace
Deploy the same workspace stack from AWS, Azure, or Google Cloud marketplace packaging. Entitlement happens at deploy time; the customer keeps the cloud account and data plane.
Private cloud
Use Helm, Terraform, or compose in your environment. We can prescribe the required services for your team to buy and install, or procure and install the supporting services as part of the engagement.
Managed maintenance
Private-cloud customers can add full maintenance: upgrades, database checks, observability setup, runbook support, and custom quotes based on the operating model.
// Measured, not claimed
The line stays flat while the corpus grows.
Both charts are drawn from real test runs. The reproducible command, raw output, and full methodology are linked directly under the data.
Resident memory across ingest
51.2 MB across 1,000 documents (31,000 chunks), real BGE-small embeddings, real SQLite.
Footprint at a glance
Idle, full-load peak, and the ceiling enforced on every push.
Reproduce the benchmark
From the greatmemory source tree:
cargo test -p gm-cli --release -- --ignored ram_regression --nocaptureRead exactly what is measured →
Evidence trail
- ✓The line chart is the verbatim output of
crates/gm-cli/tests/ram_regression.rsrun on 2026-06-12. - ✓The idle figure is
rss_bytesfromGET /v1/statsafter booting the published Docker image. - ✓The same test runs in CI and fails if peak RSS crosses 1 GB.
Show raw test output
[ram_regression] docs= 50 ingested= 2.6 MB rss= 535.9 MB peak= 535.9 MB [ram_regression] docs= 100 ingested= 5.1 MB rss= 530.5 MB peak= 535.9 MB [ram_regression] docs= 150 ingested= 7.7 MB rss= 529.3 MB peak= 535.9 MB [ram_regression] docs= 200 ingested= 10.2 MB rss= 535.8 MB peak= 535.9 MB [ram_regression] docs= 250 ingested= 12.8 MB rss= 512.6 MB peak= 535.9 MB [ram_regression] docs= 300 ingested= 15.4 MB rss= 578.1 MB peak= 578.1 MB [ram_regression] docs= 350 ingested= 17.9 MB rss= 545.5 MB peak= 578.1 MB [ram_regression] docs= 400 ingested= 20.5 MB rss= 547.7 MB peak= 578.1 MB [ram_regression] docs= 450 ingested= 23.1 MB rss= 520.9 MB peak= 578.1 MB [ram_regression] docs= 500 ingested= 25.6 MB rss= 545.8 MB peak= 578.1 MB [ram_regression] docs= 550 ingested= 28.2 MB rss= 550.6 MB peak= 578.1 MB [ram_regression] docs= 600 ingested= 30.7 MB rss= 543.4 MB peak= 578.1 MB [ram_regression] docs= 650 ingested= 33.3 MB rss= 556.5 MB peak= 578.1 MB [ram_regression] docs= 700 ingested= 35.9 MB rss= 543.7 MB peak= 578.1 MB [ram_regression] docs= 750 ingested= 38.4 MB rss= 530.4 MB peak= 578.1 MB [ram_regression] docs= 800 ingested= 41.0 MB rss= 526.9 MB peak= 578.1 MB [ram_regression] docs= 850 ingested= 43.6 MB rss= 494.9 MB peak= 578.1 MB [ram_regression] docs= 900 ingested= 46.1 MB rss= 560.0 MB peak= 578.1 MB [ram_regression] docs= 950 ingested= 48.7 MB rss= 588.1 MB peak= 588.1 MB [ram_regression] docs=1000 ingested= 51.2 MB rss= 541.3 MB peak= 588.1 MB [ram_regression] SUMMARY: ingested 51.2 MB across 1000 docs -> 31000 chunks; peak RSS 588.1 MB (limit 1024 MB) test ram_regression_50mb_ingest_stays_under_1gb ... ok test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out; finished in 2619.96s
// What ships
One workspace stack for search, chat, ingestion, and audit.
Hybrid retrieval
Vector similarity, BM25 full-text, extracted facts, recency, reranking, and graph expansion are fused into grounded search and chat responses.
Guardrails
A rule layer catches obvious PII patterns first; an optional customer-configured LLM layer can monitor, redact, or block before retrieval runs.
Audit and compliance trail
Security-relevant actions, chat outcomes, source IPs, user agents, redacted previews, content hashes, and exports are written into a tamper-evident audit chain.
Auth and roles
Local users, LDAP/AD login, admin/member roles, and API access sit in the control plane so every UI, custom app, and ETL caller shares the same policy path.
Provider management
Customers bring their own OpenAI, Anthropic, Google, Azure OpenAI, Bedrock, Vertex, or local provider keys; private and marketplace modes allow hosted LLM APIs.
Ingestion and ETL
REST sources, connector runs, delta ingest, purge, rollback, and third-party ETL tools all pass through the same auth, guardrail, and audit path.
Full-stack observability
App metrics, traces, host/container CPU and memory, and Postgres metrics flow through OpenTelemetry into bundled Grafana, cloud-native monitoring, or both.
MCP and API surfaces
Starter chat UI, customer-built UIs, REST integrations, streaming chat, ETL endpoints, and MCP tools run over the same workspace memory engine.
Pluggable storage
SQLite, Postgres, and MySQL backends support local evaluation, managed cloud databases, and enterprise database constraints.
Appliance-ready
For air-gapped appliance mode, local-only inference can hard-block external AI providers and keep telemetry inside the local Grafana stack.
The retrieval, temporal graph, trust, episodic, and reflection layers are grounded in current memory-systems research - see the research & roadmap →
// Engage
Tell us which operating model you want.
We will scope the install as a marketplace deployment or a private-cloud service. For private cloud, we can either specify the required services for your team to buy and install, or procure, install, and maintain them under a custom quote.
# same images, different packaging GM_DEPLOY_MODE=marketplace # cloud marketplace GM_DEPLOY_MODE=private # private cloud GM_DEPLOY_MODE=appliance # air-gapped local-only mode # the workspace control plane owns auth + LDAP/AD guardrails + BYO providers hash-chained audit ingest + purge + ETL Grafana + cloud-native telemetry