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Why ekoDB / Platform
One platform, built as one system
A single Rust binary handles your data, search, real-time layer, and agents together, which makes the platform faster, simpler, and considerably cheaper to run than any stack you would assemble to match it.
What is ekoDB?
One governed platform - data, retrieval, real-time APIs, and a built-in agent runtime.
Run AI agents next to your data, governed by the same auth, encryption, and audit trail as every query, or run the platform as a fast managed database with built-in chat, RAG, and vector search.
EnterpriseAIstallswhendata,retrieval,andagentsliveinseparatesystemsthateachhavetobesecured,integrated,andoperated.ekoDBbringsthemintoonegovernedplatform:documentstorage,key-valueoperations,full-textsearch,vectorsearch,andabuilt-inagentruntime,soteamsgofrompilottoproductionwithoutassemblingastack.
The architecture
Four layers, one integrated system
Everything below runs inside the same process, rather than as separate services calling each other across a network.
Access layer
SDK / REST APITerminal clientAgent chatWeb dashboardAgent layeroptional
Builds backendsChat with your dataAutonomous opsGoals and tasksDatabase engine
Document storeKey-value and cacheFull-text searchVector search / RAGReal-time WebSocketsInfrastructure
Managed hostingMulti-region replicationSecurity by default
Fewer network hops
Removing network hops cuts end-to-end latency
Every call between separate systems adds latency. A single request against an assembled stack can cross the wire five times before the database has done any actual work.
Assembled stack
- App server to auth servicenetwork hop
- App server to Postgresnetwork hop
- App server to Redis cachenetwork hop
- App server to vector DBnetwork hop
- App server to search indexnetwork hop
ekoDB
- Request to ekoDBone hop
Auth, storage, cache, search, and vector retrieval all run in-process. There is no second round trip to make.
This is separate from raw database speed. It is an advantage that exists because the system is not spread across several hosts, and it would hold even if the engines matched. For how ekoDB's operation-level throughput compares with PostgreSQL, MongoDB, and Redis, see the Performance page.
The full cost of complexity
Every system you run is a cost you carry
Consolidating a stack is not just fewer logos on an architecture diagram. Each system you remove takes a whole category of ongoing cost and risk with it, for as long as the application runs.
Subscription fees
One bill, rather than a line item per vendor for the life of the system.
Specialist hiring
Fewer systems to find, train, or hire specialists for.
Dashboards and tooling
One place to monitor and operate, not one per vendor.
Independent scaling
No separate capacity planning for every system in the stack.
Security surface
Fewer systems to patch, harden, and audit.
Vendor onboarding
No procurement and security review for each new tool.
SLAs to enforce
One vendor relationship to manage instead of several.
Integrations to maintain
No glue code to build, break, and repair for the system's lifetime.
Data consistency
One source of truth, rather than syncing the same records across systems.
Upgrades and patching
One release cycle to track instead of coordinating several.
On-call burden
Fewer systems that can page someone at 3am.
Compliance overhead
One system to certify and audit, not five.
None of this appears on a pricing page. It shows up later, in headcount, in incident reports, and in the work your team did not have time to ship. Multiply any one of them across five or six systems and the gap compounds.
Frequently asked questions
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