Databricks is where your data, features, and models live, with an agent layer built around them. Databricks agents run on Agent Runtime, a Databricks Apps service that recovers interrupted runs and keeps their state in Lakebase, its managed Postgres. 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, AI Search (formerly Vector Search), and Model Serving — with Agent Bricks, its agent developer platform, built around it. Databricks is the data, feature, and model tier. Agents built there read your governed data and run on Agent Runtime, which recovers runs that a crash or restart interrupts. Session state and long-term memory live in Lakebase, its managed Postgres, and the agent reads its session at the start of every turn.
Databricks publishes a 99.9% control-plane SLA. Databricks publishes no six-nines figure for the tier agents run on. Databricks managed disaster recovery requires an access request, fails over in minutes, and does not replicate Model Serving endpoints.
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.
Akka Optimize puts each request on the best model for the task, including smaller models trained on your data.
| Dimension | With Databricks | With Akka |
|---|---|---|
| Category | A data intelligence platform — lakehouse, AI 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 | Agent Runtime on Databricks Apps recovers interrupted runs. Run state, sessions, and long-term memory live in Lakebase managed Postgres, and the agent re-reads its session 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 | Databricks reports that most teams run active-passive DR, with example targets of RTO under 1 hour and RPO under 15 minutes for critical workloads. Managed DR requires an access request and excludes Model Serving endpoints. | Active-active HA/DR that Akka owns and operates — sub-1-minute RTO, zero-byte RPO, 24/7. |
| Governance | Unity Catalog controls access and records lineage and audit. Unity Gateway policies (Beta) check each tool call before it runs and can block it or require approval. Databricks offers no pre-deployment classification against regulations. | Akka Verify applies any control to any AI system and guarantees its enforcement, with inline guardrails, HITL and HOTL control, pre-deployment classification, and hash-chained evidence. |
| Spec-driven delivery | Genie Code drafts a plan you approve before it builds data pipelines from a natural-language request. Agent Bricks generates evaluation criteria and synthetic test data for an agent you define. | Akka Specify builds agentic systems from your specifications and releases only what meets your definition of done. |
| Model optimization | Smart Routing (Beta) sends each coding-agent task to the lowest-cost model that can handle it. Foundation Model Fine-tuning has reached end of life, and Databricks points training and fine-tuning to AI Runtime serverless GPUs. | Akka Optimize puts each request on the best model for the task, including smaller models trained on your data. |
| Infrastructure cost | DBU consumption metering — per-token plus provisioned-throughput DBUs for serving — so the meter moves with load, and lakehouse, AI 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. |
Databricks enforces Unity Gateway policies on the model and tool calls that pass through its gateway. Databricks publishes AI security guidance and leaves EU AI Act risk assessment to the customer.
Security guidance, no classification
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. Unity Gateway service policies, in Beta, check an MCP tool call before it runs and allow it, block it, or require approval. Built-in policies cover PII, prompt injection, and unsafe content.
Databricks maps its AI security framework to MITRE ATLAS, OWASP, NIST, and the Cloud Security Alliance, and leaves EU AI Act high-risk assessment to the customer. Databricks keeps audit logs, usage tables, and inference tables of full request and response payloads.
Block & escalate
Risk teams govern every agentic system without waiting on its developers, and every control they apply is guaranteed to be enforced. Risk teams choose from 1,718 controls drawn from 241 regulations and frameworks. 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. The lakehouse, feature store, vector search, and model serving are its layer, and for storage, semantic retrieval, and hosted inference Databricks 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. The lakehouse stays the system of record, and the governed agentic system runs on Akka.
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.
