Databricks is where your data, features, and models live, with an agent layer built around them. That is the tier agents read from. It is not the runtime an agentic system runs on. Databricks agents run on stateless serving endpoints and read your Lakehouse; Akka is the operated, governed, always-on runtime that holds agent state, keeps the system available, and enforces policy on every action.
Databricks is a lakehouse — Delta Lake storage, Unity Catalog, Vector Search, and Model Serving — with Agent Bricks and the Mosaic AI Agent Framework built around it. That is the data, feature, and model tier. Agents defined there read your governed data and run on stateless serving endpoints, with session and long-term memory externalized to Lakebase, its managed Postgres, and re-read at the start of every turn.
Databricks publishes a 99.9% control-plane SLA. Model Serving rides the Databricks SLA, and there is no published six-nines number for the agent-serving tier. Disaster recovery is customer-architected, and Model Serving endpoints are not replicated in Databricks managed DR.
Akka is the runtime agents run on: durable in-memory agent state, orchestration, streaming, APIs, and runtime governance on one platform. Agent working memory is held in durable sharded in-memory storage — 4 ms reads, sub-10 ms writes, replayable from the event journal — so it survives failover without a per-turn database round trip.
Akka keeps the system running around the clock with active-active HA/DR and automatic failover, and stands behind a contractual 99.9999% availability SLA across the entire platform, backed by indemnities.
| Dimension | With Databricks | With Akka |
|---|---|---|
| Category | A data intelligence platform — lakehouse, Vector Search, and Model Serving — with an agent layer built around it. It is the tier agents read from. | An agentic runtime — durable agents, memory, streaming, APIs, and governance on one operated platform. It is the tier agents run on. |
| Availability | 99.9% control-plane SLA — about 8.7 hours a year — with no published six-nines number for the agent-serving tier. | 99.9999% across the entire platform — about 31 seconds a year — active-active, sub-1-minute RTO, zero-byte RPO. |
| Agent state | Stateless serving endpoints; session and long-term memory externalized to Lakebase managed Postgres and re-read from a central database each turn. | Durable state held in memory, sharded and replayable from the event journal — 4 ms reads, sub-10 ms writes, surviving failover. |
| HA / DR | Customer-architected, typically active-passive — RTO under 1 hour and RPO under 15 minutes for critical workloads; serving endpoints excluded from managed DR. | Active-active HA/DR that Akka owns and operates — sub-1-minute RTO, zero-byte RPO, 24/7. |
| Governance | Data governance — Unity Catalog access control, lineage, and audit — plus AI-governance frameworks and guidance. It reads and records; it does not enforce on the running agent. | Runtime enforcement on every action — inline guardrails, HITL and HOTL control, pre-deployment classification, and hash-chained evidence, authored by risk teams with no developer code. |
| Infrastructure cost | DBU consumption metering — per-token plus provisioned-throughput DBUs for serving — so the meter moves with load, and lakehouse, Vector Search, and serving each consume separately. | One shared-compute runtime carries the same agentic volume on up to 90% less infrastructure, at a fixed annual fee. Akka Optimize keeps improving the models on your own production traffic, so cost per task keeps falling as the system runs. |
It comes down to whether governance acts on the running agent or beside it. Databricks governs the data and access, and adds AI-governance frameworks and guidance on top. With Akka, risk and compliance write the policies independently of the agents, and the runtime enforces them on every action — with no developer code to customize.
Govern the data and access
Databricks governance is data governance. Unity Catalog enforces access control, captures column-level lineage, logs activity for audit, and governs agents, tools, and models as catalog assets with on-behalf-of controls. Databricks also publishes AI-governance frameworks and responsible-AI guidance, and states it is committed to EU AI Act compliance.
These govern the data the agent reads and record what happened. They do not block a prohibited action before it executes, halt a running process, or classify a system against regulatory obligations before deployment.
Block & escalate
With Akka, your risk and compliance teams write the policies themselves, in a separate versioned lifecycle that is independent of the agents — there is no developer code to customize. The runtime enforces every policy inline at the agent boundary, so human-in-the-loop approvals, human-on-the-loop halt switches, sanitizers, guardrails, evaluations, testing gates, and simulations all run before an action executes and block or escalate anything that does not pass, with pre-deployment classification against the regulations that apply.
Every call is traced automatically into petabyte-scale, tamper-evident storage, and that same trace data feeds continuous evaluations that keep running in production, right alongside your agents.
Reach for Databricks when the work is data and analytics — the lakehouse, feature store, vector search, and model serving. Those are its layer, and for storage, semantic retrieval, and hosted inference it is the tier to build on. If your agents mainly need governed access to that data and a place to serve models, Databricks provides it.
Reach for Akka when you are running an agentic system in production — durable agent state, orchestration, streaming, APIs, and governance operated under one SLA, with active-active failover and inline policy enforcement. Akka runs above the data tier, not in place of it: the lakehouse stays the system of record, and the governed agentic system runs on Akka.
A sample of agentic and real-time systems on Akka Platform:
Databricks is where your data and models live. Akka is the operated, governed, always-on runtime that agents run on above it — durable state, a six-nines SLA, and inline enforcement on every action. Tell us what you are building, and we will show you how it runs in production.
