Best Postgres hosting providers for AI agents in 2026

Picking a Postgres host for an AI agent isn't like picking one for a normal app. High-volume agents can write constantly. Checkpoints, memory, tool calls, and retrieved context all land on the same database that may have to handle traces, evals, and analytics later. Put both jobs on one primary at sufficient scale, and they start competing for CPU and disk.

At modest scale, keeping both workloads in Postgres is a valid place to start. When analytical scans begin to affect transaction latency, the fix is decoupling the two. Some providers do this with CDC into a separate analytical engine. Others use read replicas or serverless isolation. Either way, agent writes shouldn't have to wait on analytics once that contention becomes measurable.

We compare the major Postgres providers on transactional speed, retrieval quality, and how well each one keeps that separation when you need it.

TL;DR #

• Many AI agents can begin with Postgres for state, memory, checkpoints, tool activity, retrieval, and light reporting. Add a separate OLAP path for telemetry, evaluations, and analytics when scans create persistent CPU, I/O, cache, or p99 contention.
• We built ClickHouse Managed Postgres for production applications that need high-performance transactional Postgres and an integrated route to ClickHouse Cloud. It runs Postgres on compute-colocated local NVMe. You configure ClickPipes to replicate selected data into ClickHouse Cloud, where offloaded analytical queries run on separate compute.
• pg_clickhouse lets you query imported ClickHouse Cloud tables from Postgres. Supported work is pushed down to ClickHouse; unsupported or mixed work can still execute in Postgres.
• Choose a narrower alternative when scale-to-zero for smaller workloads, native cloud integration, time-series features at single-node write scale, or a required extension matters more than an integrated OLTP-to-OLAP path. Whatever you pick, test vector-index memory, p99 latency, connection behavior, write throughput, durability, and analytical isolation on your own workload.

Why production AI applications may need both OLTP and OLAP #

AI agents are often chatty database clients. A single run can read conversation history, retrieve context, update a checkpoint, record tool calls, and append new state. Those operations need Postgres transactions and predictable p99 latency.

The same run can produce traces, token usage, tool outcomes, evaluation results, latency distributions, errors, and product events. Teams scan and aggregate that data across thousands or millions of runs, and those queries are much heavier than the point reads and writes on the live agent path. ClickHouse's engineering guidance for managed Postgres in AI and real-time applications walks through how agent traffic, vector lookups, continuous telemetry, and analytical scans can put pressure on I/O, memory, WAL processing, and concurrency at once.

If you run both workloads on one Postgres primary, they can compete for CPU, I/O, and buffer cache. Read replicas can move reporting queries off the primary and provide useful compute isolation at moderate scale, but they're still row-oriented Postgres instances. They don't give you the execution model of a dedicated analytical engine.

That doesn't make a separate analytical engine mandatory on day one. Stateless agents, applications that store traces elsewhere, and modest Postgres-only systems are all valid. Isolate analytics when scans push transactions beyond their latency target, reporting misses its own SLA, long analytical transactions interfere with vacuum, or you have to overprovision Postgres mainly for aggregations.

At that breakpoint, an application needs high-performance OLTP, isolated OLAP, and a managed path connecting the two. That's the architecture we built ClickHouse Managed Postgres to support. Postgres stays the transactional system of record, ClickHouse Cloud handles the analytical workloads, and configured ClickPipes plus pg_clickhouse connect them without you having to build the replication system yourself.

Evaluation criteria for Postgres hosting providers for AI agents #

This comparison asks five questions about each provider. The benchmark numbers cited later apply only to the configurations tested, not to every plan or region a provider offers.

1. Can it keep agent state fast? Agent workloads mix append-only conversation and tool events with mutable checkpoints, summaries, and memory records. Look at storage architecture, write throughput, vacuum behavior, high availability, and transactional latency.
2. Can it retrieve the right context quickly? Check pgvector, alternative vector indexes, hybrid keyword and vector search, index memory, and disk access. Extension availability is only the starting point.
3. What happens when traffic spikes? Connection pooling, transaction multiplexing, cold starts, autoscaling, and storage latency all shape p99 latency under chatty agent traffic.
4. Where do analytical queries run? Database monitoring is not the same as analytics over traces, token spend, evaluations, latency distributions, and product behavior. Ask whether those scans run on the primary, on row-oriented replica compute, or on a dedicated analytical engine. Also check freshness and source-side replication overhead.
5. Can agents operate the platform safely? Check APIs, Infrastructure as Code, MCP support, agent-operable CLIs, branching, migrations, and how much infrastructure you must assemble to connect the transactional and analytical paths.

Why pgvector support alone doesn’t determine retrieval performance #

pgvector support is table stakes. It tells you that a provider can store embeddings and run exact or approximate nearest-neighbor queries. But it doesn’t tell you whether an index will meet your recall, latency, ingestion, or cost requirements.

HNSW trades more memory and longer build times for a stronger speed-recall balance. IVFFlat builds faster and uses less memory, but needs training data and workload-specific tuning of lists and probes. Vector dimensions, filters, update frequency, index build time, available RAM, and storage latency all change the result.

Size retrieval against your own embedding model, dataset, filters, update rate, recall target, and latency objective.

Best Postgres hosts for AI agents at a glance #

1. Why ClickHouse Managed Postgres is the best overall host for AI agents #

We built ClickHouse Managed Postgres for production applications that need both data paths in one managed architecture. Transactions run on Postgres backed by compute-colocated local NVMe. Configured ClickPipes replicate selected application data into ClickHouse Cloud, where analytical scans run on separate OLAP compute. pg_clickhouse keeps supported analytical query access available through the Postgres interface.

ClickHouse packages the Postgres service, managed CDC, OLAP engine, and Postgres-facing query layer in the ClickHouse Cloud experience. You still select or create the destination ClickHouse Cloud service, configure a ClickPipe and its tables, and set up pg_clickhouse foreign tables. The integration is managed, not automatic.

Best for #

ClickHouse Managed Postgres is our default choice for sustained production agent backends where chatty transactional traffic and telemetry analytics are both core requirements, or where measured analytical contention shows that Postgres needs a dedicated OLAP path.

How it works #

ClickHouse Managed Postgres runs PostgreSQL on compute-colocated local NVMe and integrates it directly with ClickHouse Cloud. Postgres handles transactions, ClickHouse Cloud handles offloaded analytical scans, and configured ClickPipes plus pg_clickhouse connect the two.

Clients query imported foreign tables through pg_clickhouse. Only supported operations are pushed down. The current documentation reports full pushdown for 14 of 22 TPC-H queries; unsupported or mixed work may execute partly in Postgres or move rows there. Use EXPLAIN (VERBOSE) to check the plan for your own queries.

What stands out #

• Vector retrieval: Supports more than 90 extensions including pgvector, specifically pgvector (0.8.2), vchord, vchord_bm25, and pg_tokenizer. NVMe accelerates disk reads, but vector index memory and storage still need explicit sizing.
• Transactional state: Postgres runs on compute-colocated local NVMe, avoiding network-storage round trips for write-heavy event appends and mutable checkpoints.
• High availability: Three HA modes expose the latency and durability tradeoff. Zero standbys maximizes performance without a streaming standby. One standby is asynchronous and can lose recent acknowledged commits during failover. Two standbys use synchronous replication and add a cross-Availability-Zone acknowledgement to commits.
• Concurrency and latency: Bundled PgBouncer runs in transaction-pooling mode, letting many short-lived clients share a smaller pool of backend connections. Apps that need server-side prepared statements can connect directly.
• Operational integration: clickhousectl gives coding agents machine-readable workflows for provisioning and recovery operations. ClickHouse Agents adds read-only-by-default tools for querying Postgres, inspecting service metrics and slow-query patterns, and analyzing Postgres and ClickHouse Cloud together. Control-plane automation also includes OpenAPI and an alpha Terraform provider.
• OLAP and workload isolation: ClickPipes CDC replicates selected application data into ClickHouse Cloud. The default pull interval is 60 seconds; lower values are configurable, with values above 10 seconds recommended. End-to-end lag also depends on push time, backlog, and long-running transactions. Offloaded scans run outside the Postgres transactional compute, but CDC still uses source-side WAL, logical-decoding, snapshot, and network resources. Query Insights and Prometheus-compatible endpoints expose database metrics.

Where it falls short #

ClickHouse Managed Postgres uses continuously provisioned compute rather than an idle-suspension model. It doesn't bundle application services such as auth or object storage. The analytical path also needs a separately configured and billed ClickHouse Cloud service. It isn't the right fit when scale-to-zero economics for an intermittent workload or a bundled application backend is the primary requirement.

2. Neon Postgres for AI agents #

Best for #

Smaller-scale or latency-tolerant applications that need copy-on-write branching, API-driven provisioning, and serverless developer workflows rather than sustained production scale.

How it works #

Now a part of Databricks, Neon separates Postgres storage from compute. Copy-on-write branching creates isolated clones in seconds, and idle compute can suspend automatically. Compute nodes maintain a local page cache, while WAL durability uses a separate Safekeeper path.

What stands out #

• Vector retrieval: Supports pgvector for exact and approximate nearest-neighbor search. Index performance and memory requirements follow standard pgvector behavior.
• Transactional state: Branching simplifies state sandboxing during development. The local page cache affects operations that need database pages, while writes use Neon's separate WAL durability path.
• Concurrency and latency: Neon can suspend idle computes after five minutes by default and documents wake-up within a few hundred milliseconds. Exact first-query latency depends on your workload and cache state.
• Operational integration: APIs automate provisioning and branch creation, so you can spin up isolated environments quickly.
• OLAP and workload isolation: Read replicas can move moderate reporting away from the writer, but remain row-oriented Postgres. Larger telemetry analytics need an external analytical system.

Where it falls short #

Suspend economics don't apply cleanly to steady production agent traffic. Cold-start latency directly penalizes the first tool call. If the application can't accept that delay, disable scale-to-zero. Benchmark sustained throughput and p99 latency before using it beyond smaller-scale or latency-tolerant workloads.

3. Tiger Cloud Postgres for AI agents #

Best for #

Time-series-heavy agent data that benefits from hypertables, retention policies, continuous aggregates, and columnar storage, and that fits a single-node Postgres writer.

How it works #

Tiger Cloud combines TimescaleDB hypertables, continuous aggregates, a columnstore, pgvectorscale, and pg_textsearch. These features support time-series agent events and hybrid retrieval inside Postgres.

What stands out #

• Vector retrieval: Uses pgvectorscale with StreamingDiskANN and pg_textsearch with BM25 for hybrid retrieval.
• Transactional state: Hypertables partition time-series records by time, while retention policies and continuous aggregates reduce manual work for rollups and historical data.
• Concurrency and latency: Read replicas can be sized independently to isolate reporting query compute from the writer.
• Operational integration: Tiger MCP ships inside the Tiger CLI. OAuth covers the control plane, while SQL access also depends on stored database credentials and the selected Postgres role. Read-only mode must be enabled.
• OLAP and workload isolation: On-service Hypercore and continuous-aggregate work shares the Postgres resource envelope. Read replicas can isolate analytical query compute, but they aren't a dedicated scale-out OLAP engine. An external Iceberg connector is also available.

Where it falls short #

TimescaleDB 2.13 was the last release with multi-node support, so evaluate Tiger Cloud around a vertically scaled single writer rather than horizontal write scaling. On-primary queries and continuous-aggregate maintenance still share resources with agent transactions. pg_textsearch compacts segments synchronously, creating a concrete tradeoff for sustained write-heavy BM25 workloads.

4. Amazon RDS for PostgreSQL for AI agents #

Best for #

AWS-standardized teams wanting conventional managed Postgres with IAM, VPC, AWS operational controls, and Bedrock-adjacent architecture.

How it works #

Amazon RDS runs PostgreSQL on EBS-backed network storage, making provisioned IOPS and storage latency central to performance.

What stands out #

• Vector retrieval: Supports pgvector, including HNSW indexing. In an AWS benchmark, instance type and pgvector version affected build and query times for the tested workloads.
• Transactional state: RDS handles standard Postgres transactions. Frequent checkpoint updates create dead row versions and may require autovacuum tuning; lower fillfactor can help HOT updates where appropriate.
• Concurrency and latency: Relies on network-attached storage, meaning per-I/O behavior heavily influences latency under chatty traffic. RDS Proxy multiplexes transactions to handle connection volume.
• Operational integration: Supported by the established AWS API and Terraform ecosystem.
• OLAP and workload isolation: CloudWatch Database Insights covers database operability. Read replicas provide row-oriented query isolation, while eligible RDS zero-ETL integrations replicate PostgreSQL data to Redshift for dedicated analytics.

Where it falls short #

Network-attached storage paths introduce latency that compounds across chatty agent steps. RDS Proxy can pin sessions when applications use SQL PREPARE, temporary objects, or documented session state. Pinning reduces backend connection reuse rather than removing every proxy benefit. Redshift adds a separate service with version, region, and configuration requirements.

5. Amazon Aurora PostgreSQL for AI agent backends #

Best for #

AWS-standardized teams that need Aurora's distributed storage, Serverless v2 autoscaling, and eligible Optimized Reads for OLTP, with read replicas or a separate AWS service for heavier analytics.

How it works #

Aurora is a PostgreSQL-compatible service built on a distributed, log-structured storage layer. Aurora Serverless v2 manages burst scaling. Optimized Reads can use local NVMe on supported instance families and engine versions. Tiered caching depends on Aurora I/O-Optimized; Aurora Standard uses local storage for temporary objects.

What stands out #

• Vector retrieval: Supports pgvector, while eligible Optimized Reads configurations cache data locally for generative AI workloads.
• Transactional state: Aurora's managed durability layer handles append-only events and mutable summaries.
• Concurrency and latency: Distributed storage behaves differently than conventional block storage. Eligible Serverless v2 configurations can set MinCapacity=0 and auto-pause when no activity blocks it.
• Operational integration: Shares the AWS API and Infrastructure-as-Code control plane.
• OLAP and workload isolation: Read replicas provide row-oriented query isolation. Eligible Aurora PostgreSQL zero-ETL integrations replicate data to Redshift or SageMaker Lakehouse. Redshift is the dedicated warehouse option, not a technical requirement for every analytical query.

Where it falls short #

Aurora remains row-oriented. Auto-pause requires supported versions and regions, db.serverless, MinCapacity=0, and no blocking activity. An attached RDS Proxy prevents associated instances from pausing, while logical replication, zero-ETL, and global-database topology can prevent particular writers or promotion tiers from pausing. Aurora Serverless v2 typically resumes in approximately 15 seconds, but resume can take 30 seconds or longer after more than 24 hours paused. That delay can break the latency budget of a user-facing first tool call.

6. Google Cloud SQL for PostgreSQL for AI agents #

Best for #

Teams standardized on Google Cloud that want conventional managed Postgres rather than an AI-tuned Postgres-compatible engine.

How it works #

Cloud SQL is Google Cloud's conventional managed PostgreSQL service, while AlloyDB is the Postgres-compatible alternative with ScaNN vector and columnar acceleration. Cloud SQL Enterprise Plus adds Managed Connection Pooling and read pools.

What stands out #

• Vector retrieval: Supports pgvector using HNSW configurations and includes google_ml_integration for Vertex AI embeddings.
• Transactional state: Standard Postgres writes and updates handle agent event logs and checkpoints.
• Concurrency and latency: Depends on the selected storage path, edition, caching, and connection limits you configure for the instance.
• Operational integration: Accessible through the Google Cloud API and Terraform providers. Google provides a Database Insights MCP server for metrics and a separate Cloud SQL remote MCP server that can create, manage, and query resources.
• OLAP and workload isolation: Enterprise Plus read pools offload read traffic and moderate analytics from the primary. BigQuery federation queries Cloud SQL directly, so it doesn't isolate the source workload. Datastream can stream changes into a separate analytical system.

Where it falls short #

Cloud SQL lacks the ScaNN index and in-engine columnar acceleration found in AlloyDB, though it still supports HNSW, pgvector optimizations, and edition-specific caching. Read pools provide row-oriented compute isolation. If you need a dedicated columnar path, you still have to assemble and operate a separate analytical destination.

7. AlloyDB for PostgreSQL for AI agents #

Best for #

Google Cloud teams that want ScaNN vector retrieval and in-engine columnar acceleration without adding a separate analytical engine.

How it works #

AlloyDB is a PostgreSQL-compatible engine featuring in-engine columnar acceleration and advanced Google search technologies, distinguishing it from standard Cloud SQL.

What stands out #

• Vector retrieval: Supports pgvector-compatible vector operations, hybrid text and vector search, and Google's ScaNN index. ScaNN uses a tree-based quantization index with a smaller memory footprint. Adaptive filtering for filtered vector search is a separate Preview feature.
• Transactional state: Processes transactional agent checkpoints through a PostgreSQL-compatible interface and managed durability layer.
• Concurrency and latency: Designed to accelerate complex analytical queries concurrently with your transactional traffic.
• Operational integration: Managed through the GCP control plane, automation tools, and a first-party remote MCP server.
• OLAP and workload isolation: The columnar engine can run on the primary or on a read pool. A read pool isolates analytical compute from the primary, while sharing the cluster's distributed storage and replication path.

Where it falls short #

Frequent updates invalidate affected columnar content until it is refreshed, which can reduce acceleration for write-heavy telemetry. AlloyDB keeps analytics inside the AlloyDB estate, so if you need to combine that data with application events, logs, and evaluation data from elsewhere, you're working within GCP's ecosystem rather than a separate dedicated OLAP engine.

8. Azure Database for PostgreSQL for AI agents #

Best for #

Organizations standardized on Azure that need managed PostgreSQL for agent transactions and light operational reporting, with Fabric as a separate analytics path.

How it works #

Azure Database for PostgreSQL Flexible Server is the standard managed service. Elastic Clusters add Citus-based horizontal sharding when you need to scale out.

What stands out #

• Vector retrieval: Supports pgvector for embedding storage and nearest-neighbor execution.
• Transactional state: Processes agent events and memory checkpoints with standard Azure operational durability.
• Concurrency and latency: Uses a built-in PgBouncer implementation with a default max_client_conn of 5,000 and a configurable range of 1 to 50,000. Usable concurrency depends on compute and the single-threaded pooler.
• Operational integration: Integrated tightly with Azure APIs, Terraform, and Bicep.
• OLAP and workload isolation: Query Store and wait sampling feed Query Performance Insight, but are disabled by default. Fabric Mirroring copies selected eligible tables into OneLake in near-real-time batches and exposes a Fabric SQL analytics endpoint.

Where it falls short #

Flexible Server remains row-oriented. Fabric Mirroring has table and scope limits and no fixed end-to-end latency SLA. The built-in PgBouncer is single-threaded. Elastic Clusters can scale out to 20 nodes through self-service tooling, but currently can't scale in and don't support TimescaleDB. This limits operational flexibility for time-series telemetry.

9. Crunchy Bridge Postgres for AI agents #

Best for #

Teams that require a PostgreSQL extension or setting that ClickHouse Managed Postgres doesn't provide.

How it works #

Crunchy Bridge runs managed Postgres with 90 extensions plus supported procedural languages. Crunchy Data Warehouse provides a separate AWS-only analytical target that combines Postgres, a vectorized analytical engine, and Iceberg support.

What stands out #

• Vector retrieval: pgvector is available alongside standard relational querying for hybrid search designs.
• Extension access: The catalog includes Citus, TimescaleDB, PostGIS, foreign data wrappers, procedural languages, and other options that can justify choosing it for a specific requirement.
• Transactional state: Exposes table-level autovacuum controls and a documented subset of server settings for workload-specific tuning.
• Concurrency and latency: Standard AWS plans use gp3 network-attached storage, so don't treat Crunchy Bridge as a local-NVMe service.
• Operational integration: A Terraform provider handles infrastructure-as-code deployments.
• OLAP and workload isolation: Crunchy Data Warehouse isolates analytical telemetry queries from the transactional agent path. It supports heap and Iceberg tables, object storage, and local NVMe caching in the warehouse service.

Where it falls short #

Generic PostgreSQL behavior and pgvector aren't reasons to choose Crunchy Bridge over ClickHouse Managed Postgres. The differentiator has to be an extension or setting your workload actually requires. Crunchy Data Warehouse is available only on AWS and runs as a separate service. Managed logical replication applies changes in 30-second batches, with typical end-to-end lag below 60 seconds. Replication also adds source-side WAL and replication work.

10. Aiven for PostgreSQL for AI agents #

Best for #

Teams that want one vendor for a broad portfolio of managed open-source infrastructure, such as PostgreSQL, Kafka, Flink, OpenSearch, ClickHouse, Valkey, and MySQL.

How it works #

Aiven deploys and manages these services across major cloud providers. It offers a consistent control plane, but each database, stream processor, search service, and integration remains a separate component that your team has to size, secure, monitor, and pay for.

What stands out #

• Portfolio breadth: Teams can procure several open-source data services from one provider.
• Vector retrieval: Supports pgvector for exact and approximate nearest-neighbor indexes.
• Transactional state: Standard managed Postgres handles state appends and mutable checkpoints.
• Concurrency and latency: Managed PgBouncer is available on eligible Startup plans and above. Infrastructure and storage choices vary by cloud, region, and plan.
• Operational integration: Terraform handles provisioning, while Aiven's MCP server can run SQL and retrieve database metrics and logs. Read-only mode is opt-in.
• OLAP and workload isolation: You can combine Aiven services into an analytical path, but your team owns the cross-service design and operation.

Where it falls short #

The only strong reason to choose Aiven here is that you need several managed open-source projects from one vendor and accept the resulting complexity. Your team still owns integration, networking, access control, recovery design, and cost management across those services. Plans, infrastructure, and storage options also vary by cloud and region, so benchmark the exact configuration.

11. DigitalOcean Managed PostgreSQL for AI agents #

Best for #

Smaller production agent deployments that value a simple operational surface alongside DigitalOcean's AI-oriented platform and inference tooling.

How it works #

DigitalOcean integrates managed Postgres into its standard control plane, and clusters support up to three nodes.

What stands out #

• Vector retrieval: Supports pgvector. HNSW index size remains bounded by the memory available on the selected plan.
• Transactional state: Standard managed Postgres handles modest agent-state workloads.
• Concurrency and latency: Employs PgBouncer. A cluster supports up to 21 PgBouncer pools and about 1,000 pooled clients, while direct backend connection limits are separate and plan-dependent.
• Operational integration: Accessible through the DigitalOcean API, Terraform, and an MCP server for database lifecycle operations.
• Controls and monitoring: Exposes autovacuum, memory, WAL, worker, logging, and PgBouncer settings. It also provides Postgres-specific metrics and a Prometheus endpoint.

Where it falls short #

DigitalOcean doesn't provide superuser access. Extension, compute, memory, and connection ceilings limit headroom on larger workloads. Native alerting on PostgreSQL-specific metrics isn't available. If telemetry analytics outgrow the primary or read-only nodes, you need an analytical service from another provider.

Performance and workload separation for AI agent Postgres hosting #

Agent calls stack latency. Serial database calls add latency to the step, while fanned-out calls increase the chance that one tail event determines when the step finishes. A storage delay, a connection queue, or one analytical scan can slow the entire user-facing run.

How storage architecture affects Postgres concurrency and p99 latency #

For I/O-bound Postgres workloads, storage latency and provisioned IOPS can dominate throughput and tail latency. Compute-colocated local NVMe removes the network-storage hop, while network-attached storage offers different durability and independent-scaling characteristics. Local-NVMe results also depend on the selected standby and replication mode.

ClickHouse publishes PostgresBench, a vendor-run pgbench comparison using a TPC-B-like workload. In the pinned repository snapshot, the approximately 500 GB tests used 256 clients, provider-default settings, and high availability disabled. Most services had 64 GB of memory, but Aurora used 128 GB because AWS didn't offer the targeted 1:4 CPU-to-memory ratio. Pricing wasn't normalized.

The interactive leaderboard displays the best run; this table uses the three-run arithmetic mean. The services use different storage and durability architectures, and Aurora had twice the memory. Treat the results as one vendor-authored observation about this configuration and workload, not as proof of an overall provider ranking.

At 4 vCPUs and 16 GB, the difference mostly disappeared in the three-run means. Crunchy Bridge measured 6,320.00 TPS, and ClickHouse Managed Postgres measured 6,012.88 TPS. Reproduce the test with your production schema, transaction mix, connection count, durability mode, and region.

How workload separation protects AI agent telemetry performance #

Agent telemetry analytics aggregate the same data that the runtime is actively writing. On a single Postgres primary, these scans consume CPU, I/O, and buffer cache needed by live agents.

Read replicas and read pools can isolate query compute for moderate reporting, even though they remain row-oriented and share storage or replication paths with the writer. Federation is different: it can simplify access while still sending query work to the source. CDC copies data to dedicated analytical compute, where heavy scans run separately, but the source still performs logical decoding, retains WAL, and sends changes.

AWS offers zero-ETL integrations with Redshift, Azure offers Fabric Mirroring, GCP offers Datastream, and Crunchy Bridge replicates into a separate warehouse. BigQuery federation queries Cloud SQL directly and doesn't isolate source load. AlloyDB can run its columnar engine on a read pool, while Tiger Cloud can use read replicas to isolate query compute.

ClickHouse Managed Postgres follows the principle of moving analytics off the primary. Configured ClickPipes replicate selected Postgres data into ClickHouse Cloud with eventual consistency. pg_clickhouse pushes supported work against imported foreign tables into ClickHouse Cloud while preserving the Postgres interface. Offloaded scans don't consume the Postgres primary's query compute, but CDC and any local or non-pushed work still affect Postgres.

Freshness needs, replication overhead, and isolation depth vary across all of these approaches, so run a proof of concept on your own data and workload before making a provider decision.

How to choose Postgres providers for AI agents #

ClickHouse Managed Postgres is the best overall choice for sustained production AI applications that need fast transactional Postgres and dedicated analytics. Start with Postgres alone if it meets both the transactional and reporting targets. Choose a narrower platform when scale-to-zero for smaller workloads, provider-native integration, time-series features, or a required extension is the deciding requirement.

Conclusion #

Evaluate the options against one architectural question: what stays on the transactional Postgres path, what runs on isolated analytical compute, and how much integration you have to assemble yourself. You can keep both workloads in Postgres while it meets their targets. Once analytics creates measurable contention, ClickHouse Managed Postgres provides the integrated route we recommend for moving that work into ClickHouse Cloud.

Get started with ClickHouse Managed Postgres to run Postgres for transactions and ClickHouse Cloud for analytics in one managed experience.