Postgresml
Run LLM inference, embeddings, and ML training directly inside PostgreSQL using SQL functions, or use the Korvus SDK for RAG pipelines.
PostgresML is the pragmatic pick if your application data already lives in Postgres and you are tired of stitching a vector database to an embedding API and an inference service. The colocation story is concrete: pgml.embed() plus HNSW indexing means no separate vector-database bill, and pgml.transform() serves Llama 3.1, Mixtral, and Mistral on the same infrastructure that holds your tables. The catch is SQL fluency — if your team writes Python all day and never touches pgml.*, look at a managed RAG framework or a standalone vector database instead. Self-hosters need their own GPUs. Serverless billing runs by query hour, not per token.
Verified 1d ago · liveness 72/100 · cite: rightaichoice.com/tools/postgresml
- PostgreSQL users adding ML without new microservices
- Developers building RAG chatbots on existing Postgres data
- Data scientists wanting GPU-accelerated embeddings in-database
- Teams consolidating vector search, embeddings, and inference into one deployment
- Teams not running PostgreSQL
- Self-hosted deployments without access to GPU hardware
- Users wanting a no-code AI tool rather than SQL
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip PostgresML if your application data does not live in PostgreSQL or your team has no SQL fluency, since every workflow runs through pgml.* functions or the Korvus SDK.
Past the included $100 credit, serverless bills queries by CPU/GPU millisecond at $7.50 per query hour, so a slow 405B query costs far more wall-clock time than a fast 1B one.
Self-hosted open source is $0/mo plus your own GPUs; serverless starts at $7.50 per query hour with storage at $0.25/GB per month and $100 free credits; dedicated runs from $0.60 per instance hour with committed-use discounts; Enterprise VPC pricing is custom. It undercuts a Pinecone-plus-OpenAI stack on the vendor's own estimator, but teams that want predictable per-token bills should compare against a hosted model API.
In short
Postgresml — Run LLM inference, embeddings, and ML training directly inside PostgreSQL using SQL functions, or use the Korvus SDK for RAG pipelines. Best for PostgreSQL users adding ML without new microservices, Developers building RAG chatbots on existing Postgres data, Data scientists wanting GPU-accelerated embeddings in-database. Free to start; paid plans from $0.6.
What's new in Postgresml
Checked 2 days agoAcross the latest 1 update: 1 news mention.
What people actually say about Postgresml — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
10 mentions across 1 source (Hacker News) · researched Jul 3, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +GPU-accelerated ML models run directly in Postgres via SQL.
- +Simplifies AI stack by colocating data and compute.
- +Built-in embedding generation with open-source models like Llama and Mistral.
- +Supports vector search (KNN/ANN) with HNSW and IVFFlat indexing.
- +Fine-tuning LLMs on custom data without moving data out.
- −Project is abandoned — no active development or support.
- −Uncertain future for security patches and bug fixes.
- −Naming caused confusion and backlash from Postgres community.
- −Accused of copying prior art without proper credit.
- −Limited community size and shrinking fast since closure.
- • GPU hardware or cloud GPU instances not included
- • Storage and compute for vector indexes can balloon costs
- • No support for cloud tier if project is dead
Viability Score
How well maintained and how widely used is Postgresml? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- SQL API for ML operations: pgml.embed(), pgml.transform(), pgml.train(), pgml.predict()
- Text generation with Meta-Llama-3.1-8B/70B/405B-Instruct and Llama-3.2-1B/3B-Instruct
- Mixtral-8x7B-Instruct-v0.1 and Mistral-7B-Instruct-v0.2 inference
- Embeddings via e5-small-v2, gte-base-en-v1.5, gte-large-en-v1.5, mxbai-embed-large-v1
- HNSW and IVFFlat vector indexes for KNN and ANN search
- Fine-tune open-source LLMs on your own data with pgml.tune()
- Supervised learning: regression, classification, clustering, decomposition via pgml.train()
- Batch and online prediction with pgml.predict(), reusing training preprocessors
- Model deployment versioning and rollback with pgml.deploy()
- Streaming inference with pgml.transform_stream()
- Built-in data preprocessors for splitting and chunking
- Korvus SDK for JavaScript, Python, C, and Rust RAG pipelines
- Colocated data and compute: embed, serve, and store in one process
- Self-hosted open source plus serverless, dedicated, and VPC cloud deployments
- Integration with dbt, Apache Airflow, Dagster, and Kafka for data pipelines
About Postgresml
PostgresML is an open-source PostgreSQL extension plus a managed cloud that moves machine learning and AI compute into your database. Instead of running a separate vector database, an embedding API, and an inference service alongside your application data, you install PostgresML into Postgres and call functions like pgml.embed(), pgml.transform(), pgml.train(), pgml.tune(), pgml.predict(), pgml.deploy(), and pgml.chunk() straight from SQL. The extension generates embeddings with models including intfloat/e5-small-v2, mixedbread-ai/mxbai-embed-large-v1, Alibaba-NLP/gte-base-en-v1.5, and Alibaba-NLP/gte-large-en-v1.5, and indexes them with HNSW or IVFFlat for fast KNN and ANN search (the vendor published a March 2026 engineering guide comparing the two index types for production RAG). For text work it runs meta-llama/Meta-Llama-3.1-8B, 70B, and 405B-Instruct, Llama-3.2-1B and 3B-Instruct, mistralai/Mixtral-8x7B-Instruct-v0.1, mistralai/Mistral-7B-Instruct-v0.2, microsoft/Phi-3-mini-128k-instruct, and google/pegasus-xsum for summarization, with text-generation, embeddings, summarization, and translation as first-class tasks. Beyond LLMs, pgml.train() covers regression, classification, clustering, decomposition, and hyperparameter search across roughly 50 algorithms. The Korvus SDK wraps these primitives in Collections and Pipelines for JavaScript, Python, C, and Rust, so you can build RAG without hand-writing SQL for every step. Deployment ranges from self-hosted open source, which needs your own GPU hardware, to serverless cloud billed at $7.50 per query hour and $0.25/GB storage per month, dedicated instances from $0.60 per instance hour, and custom Enterprise VPC deployments with multiple GPUs and SLAs. New accounts get $100 in free usage credits. It fits teams already on Postgres who want vectors, models, and application data in one GPU-backed process.
Behind the Verdict
PostgresML's core bet is architectural: stop shipping your data out to models, and instead bring the models to the database. That is why the vendor can claim 4x faster RAG than HuggingFace plus Pinecone and 10x faster embeddings than OpenAI in their own benchmarks, and why storage is just $0.25/GB per month rather than a separate vector-store line item. The strongest part of the product is the pgml.* function surface. pgml.embed() converts text to vectors using intfloat/e5-small-v2, mixedbread-ai/mxbai-embed-large-v1, or gte-base/large-en-v1.5, and you index them with HNSW or IVFFlat for KNN and ANN search. pgml.transform() runs text-generation, summarization, translation, and other NLP tasks over the same tables. pgml.train() handles tabular ML — regression, classification, clustering, decomposition, hyperparameter search — across roughly 50 algorithms through the same SQL interface. pgml.tune() fine-tunes open-source LLMs on your own rows, and pgml.deploy() versions the result so you can roll back if quality drops. That chain from embedding to fine-tuning to deployment inside one process is genuinely hard to replicate piecemeal. The Korvus SDK is the on-ramp for teams who do not want to write raw SQL for every retrieval step. Collections and Pipelines in JavaScript, Python, C, and Rust package the same primitives, including splitter configuration and model selection, into a higher-level API. OpenSourceAI example apps and additional code examples are available in the repository. Where PostgresML bites is operational. Self-hosting needs GPU hardware to be worth it, and the docs treat GPU support, distributed training, and replication as separate setup chores; building from source, running behind PgCat, and configuring backups are all distinct guide sections. Serverless billing runs on query hour and storage rather than per token, so a slow, large-model query costs more wall-clock compute than a fast small-model one — that can make monthly spend harder to predict than a flat per-token rate, even though every model listed on the pricing page is bundled into the query charge. And everything is tied to PostgreSQL: there is no support for other databases. Team fit is narrow and clear. If you are already on Postgres, comfortable in SQL, and want vectors, models, and application data in one GPU-backed process with one bill, PostgresML is worth a serious evaluation. If you want a no-code AI tool, or your organization is standardized on proprietary hosted LLMs, this is not the right door.
Researching Postgresml? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Postgresml actually fits — and what changes day-one when you adopt it.
Add a RAG chatbot over product documentation already stored in Postgres, calling pgml.embed() to generate vectors and indexing them with HNSW for KNN search.
Outcome: One database serves both transactional queries and semantic retrieval, removing a separate vector-database service and its sync job.
Use pgml.train() to run regression and classification experiments across roughly 50 algorithms, then pgml.deploy() to release the model once quality metrics improve.
Outcome: Models are trained, versioned, and served without exporting data to a separate ML platform.
Run pgml.transform() over text columns with google/pegasus-xsum or Meta-Llama-3.1-8B-Instruct, and orchestrate the job with dbt, Apache Airflow, or Dagster.
Outcome: Batch summarization executes inside the same database that holds the source records, replacing a separate inference service and its data egress.
Use Cases
- Build a RAG chatbot that retrieves facts from tables already stored in your Postgres database
- Generate embeddings for millions of documents with GPU-accelerated models
- Fine-tune an open-source LLM on proprietary data without leaving PostgreSQL
- Run summarization and translation on text columns using pgml.transform()
- Train classification or regression models on tabular data in place with pgml.train()
- Index and search vectors with HNSW or IVFFlat for high-performance retrieval
- Perform semantic search across existing database content
Models Under the Hood
as of 2026-10-18
Limitations
- PostgresML assumes comfort with SQL and machine learning concepts; pgml.train(), pgml.tune(), and pgml.deploy() are not point-and-click, so non-technical users will struggle.
- Self-hosted installs need GPU hardware to perform well, and the docs treat GPU support, distributed training, and replication as separate setup work.
- Serverless billing is by query hour and storage rather than per token, so a slow large-model query costs more wall-clock compute than a fast small-model one, making monthly spend harder to predict than a flat token rate.
- Everything is tied to PostgreSQL: there is no support for other databases.
as of 2026-10-10
Verification history
We have re-verified Postgresml 9 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Postgresml tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Self-hosted (Open Source)
$0/mo
Ideal for
Teams with their own GPU hardware who want full control over the deployment and no vendor bill.
What this tier adds
Starting tier — free extension, but you supply GPU hardware and rely on community support.
Serverless
From $7.50/query hour
Ideal for
Teams prototyping or with variable load who want to start without provisioning GPUs, aided by $100 in free credits.
What this tier adds
Adds managed burst GPU capacity, curated models, and pay-per-use billing at $7.50 per query hour plus $0.25/GB per month storage.
Dedicated
From $0.60/instance hour
Ideal for
Organizations with established, steady inference and retrieval workloads that need predictable hardware.
What this tier adds
Adds dedicated hardware, any HuggingFace model, multi-cloud regional deployment, committed-use discounts, and private Slack or MS Teams support.
Enterprise
Custom
Ideal for
Large teams with advanced security needs that require VPC isolation and contractual SLAs.
What this tier adds
Adds VPC deployments, multiple GPUs, custom SLAs, premium onboarding, and priority feature requests on pay-as-you-go or committed-use pricing.
Where the pricing makes sense
The company stage and team size where Postgresml's pricing actually pencils out — and where peers do it cheaper.
Self-hosted open source is $0/mo plus your own GPUs; serverless starts at $7.50 per query hour with storage at $0.25/GB per month and $100 free credits; dedicated runs from $0.60 per instance hour with committed-use discounts; Enterprise VPC pricing is custom. It undercuts a Pinecone-plus-OpenAI stack on the vendor's own estimator, but teams that want predictable per-token bills should compare against a hosted model API.
Setup time & first value
How long it actually takes to get something useful out of Postgresml — broken out by persona, not the marketing-page minute.
Serverless: create a database, connect your app, and import data via logical replication, foreign data wrappers, COPY, or pg_dump — most teams reach a first pgml.embed() call within an afternoon. Self-hosted open source: budget days rather than hours, since you install the extension onto a GPU-backed Postgres, then separately configure GPU support, replication, and backups per the self-hosting
Switching to or from Postgresml
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Pinecone: run pgml.embed() over your source rows in Postgres and index with HNSW or IVFFlat, then use the cost estimator to compare against your current bill.
- →From OpenAI embeddings API: swap your vector-generation calls for pgml.embed() against e5-small-v2 or gte-base-en-v1.5, which are bundled into serverless query charges.
- →From a standalone ML platform: move tabular training into pgml.train() and keep source data in place rather than exporting it.
- ↗To Pinecone plus a hosted model API: export embeddings produced by pgml.embed() into the vector store and re-point inference calls at the API.
- ↗To a managed RAG framework: rebuild Korvus Pipelines as framework chains, since the underlying primitives are documented in the PostgresML API reference.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Postgresml”, and we withheld 6: 6 could not be judged, because “Postgresml” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Postgresml.
Official links
Tools that pair well with Postgresml
Common stack mates teams adopt alongside Postgresml, with the specific reason each pairing earns its keep.
BlazeSQL
BlazeSQL is an AI data analyst that answers plain-English questions directly against your SQL database.
TablePlus
TablePlus is a native database GUI for MySQL, PostgreSQL and SQLite with a one-time license.
LanceDB
LanceDB is an open-source multimodal lakehouse that keeps curation, feature engineering, hybrid search, and training on one Lance table.
Featured Head-to-Head Comparisons
Postgresml vs Spider Cloud
Choose Spider Cloud if your priority is fetching live web data for AI agents (RAG, scraping) with pay-as-you-go pricing and recent Browser AI commands. Choose PostgresML if you already use PostgreSQL and want GPU-accelerated ML (embeddings, LLM generation) directly in your database without new infrastructure.
Postgresml vs Screenplayiq
These tools serve completely different domains. ScreenplayIQ is a niche AI screenplay analyzer for feature film marketability, while PostgresML is a general-purpose ML extension for PostgreSQL. Choose ScreenplayIQ if you need script-level box office predictions; choose PostgresML if you want to run ML models directly on your database. They are not interchangeable.
Postgresml vs Temporal Ai
Before you choose, ask: do you need reliability for long-running AI agents, or ML inside your existing PostgreSQL database? Temporal AI excels for mission-critical workflows with automatic recovery and state persistence, while PostgresML is ideal if you're already on Postgres and want to run embeddings and LLM inference without leaving the database. For most teams building agentic workflows, Temporal's durable execution is the safer bet; for data scientists requiring colocated compute, PostgresML simplifies the stack.
Alternatives to Postgresml
View all
Frequently Asked Questions
Best-of guides
Used Postgresml? Help shape our editorial sentiment research.