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Enterprise storage backends Enterprise

MatrixDB and MatrixKV behind the context platform.

TemporalStore serves LLM context everywhere. When a deployment needs more than one node's worth of hot state, or a handful of records that can never be wrong, two enterprise backends sit underneath: MatrixDB for high-throughput hot context state and profile KV, and MatrixKV for the small, strongly consistent records the context path must trust. They are storage backends for the context platform — not general-purpose databases to shop for up front.

You should not have to pick a storage engine to use MatrixArk. You integrate with one context surface — ingest events, compile context, get a token-budgeted context pack — and TemporalStore routes state to the right backend underneath. Most context tolerates eventual consistency: memory timelines, hot session state, cached summaries. A small slice cannot: permissions, approvals, leases, ownership. The two enterprise backends exist so both kinds of state get the guarantees they need without changing the API.

The split, in one sentence each

  • TemporalStore — the serving core: time-aware memory, extraction, freshness, replay, and prompt-time context packs. Open source.
  • MatrixDB Enterprise — high-throughput, eventually-consistent hot context state and profile KV read on nearly every request.
  • MatrixKV Enterprise — strongly consistent, transactional records the context path must trust before and after it acts.

Where each backend sits

One serving surface on top; enterprise backends underneath, each for the state it is built for.

Context platform
TemporalStoretime-aware memory, extraction, freshness, replay, context packs
routes state to the right backend ↓
Enterprise storage backends

Application teams call one context API. TemporalStore stays the single serving surface; the enterprise backends handle placement, scale, durability, and consistency underneath.

MatrixDB: hot context state at scale

The high-throughput backend for the state the context path reads on almost every request.

MatrixDB is the enterprise backend for hot, high-volume context state: active session summaries, profile KV, cached retrieval candidates, and cache-policy metadata for prefix reuse. It offers Redis-compatible access for teams that already think in strings, hashes, and TTL state, but adds what a context platform needs at scale — persistence, scans and exports for repair and evals, tenant-aware placement and quotas, and cheaper storage for colder KV. It is tuned for throughput and eventual consistency, not for records that must have exactly one committed answer.

  • Hot session summaries, profile KV, and ranked retrieval candidates read on the serving path.
  • Cache-eligibility keys, reusable prompt-section ids, and invalidation hints for prefix reuse.
  • Scans, exports, and nearline query for repair, analytics, and eval joins.
  • Tenant-aware routing, quotas, and noisy-neighbor control across the context platform.

MatrixKV: records that cannot be wrong

The strongly consistent boundary the context path consults before and after it acts.

MatrixKV is deliberately small and separate from MatrixDB. It is not high-volume memory; it is the transactional boundary that keeps a few correctness-critical records trustworthy. Get these wrong and the model reasons over unauthorized, duplicated, or superseded context — a stale permission leaks data, a duplicate ownership record double-commits an agent action, a missed lease lets a paused worker keep writing.

  • Before the prompt — permissions, source versions, and tenant policy checked before context enters the model.
  • Before the action — idempotency keys, leases, approvals, and ownership committed before an agent mutates product state.
  • After the action — committed outcome ids and timestamps TemporalStore references later for replay, audit, and stale-action blocking.
Committed metadata the context path references
# Before the prompt: is this source visible to this user, at this version?
GET  perm:company_a:doc_913:user_918   -> "read"
GET  source:doc_913:visible_version     -> 7

# Before the action: commit ownership + idempotency in one transaction.
TXN
  SETNX  lease:gpu_batch:owner   worker_44   EX 30
  SETNX  action:appr_913:commit  1
COMMIT

# After the action: record the committed outcome for replay + stale-blocking.
SET  outcome:appr_913  '{"status":"approved","ts":"2026-06-14T16:20:00Z"}'

When to use which backend

TemporalStore serves context everywhere. Reach for a specific backend only for the state that demands it.

BackendConsistencyUse it when
Local disk Single node Dev, edge, and self-hosted context footprints where one node owns its storage. Open source.
MatrixRaft Replicated Replicated high availability for context state without a shared-storage tier. Open source.
MatrixObject Enterprise Durable shared Disaggregated, concurrent, elastic capacity for durable context data at five-nines.
MatrixDB Enterprise Eventual High-throughput hot context state, profile KV, and cache metadata read on nearly every request.
MatrixKV Enterprise Strong / transactional Small correctness-critical records — permissions, approvals, leases, ownership, timestamps — the context path must trust.

Explore the context platform

Serving core TemporalStore Time-aware memory, extraction, freshness, replay, and prompt-time context packs. Enterprise MatrixDB Eventually-consistent hot context state, profile KV, and cache metadata at scale. Enterprise MatrixKV Strongly consistent permissions, approvals, leases, ownership, and timestamps. Enterprise MatrixObject Disaggregated shared-object storage for durable context data at scale. Writing Blogs Why LLM context needs real infrastructure and how the pieces fit together. Open source temporalstore.ai The open-source engine behind the enterprise MatrixArk context platform.