Postgresml
Run GPU-accelerated machine learning and AI inside PostgreSQL with SQL.
If you're already on Postgres and want to cut out the microservice sprawl of separate vector DB and LLM APIs, PostgresML is a strong, cost-effective choice. The SQL-centric workflow is a double-edged sword: powerful for SQL-savvy teams, a barrier for others. Just be sure you're comfortable with SQL and have a GPU plan if self-hosting.
Verified 4d ago · liveness 72/100 · cite: rightaichoice.com/tools/postgresml
- PostgreSQL users adding ML without introducing new microservices
- Developers building RAG chatbots on top of existing Postgres data
- Data scientists needing GPU-accelerated embeddings directly in the database
- Teams that want to simplify their AI stack by colocating data and compute
- Teams not using PostgreSQL (no other database support)
- Self-hosted deployments without access to GPU hardware
- Users looking for a no-code AI solution (requires SQL proficiency)
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Skip PostgresML if you are not using PostgreSQL, lack GPU resources for self-hosting, need a no-code solution, or rely on proprietary LLMs like GPT-4o.
Serverless storage is charged separately at $0.25/GB per month, which can add up if you store a lot of vector or document data.
PostgresML's serverless pricing ($7.50/hour) is cheaper than assembling a Pinecone + OpenAI stack, but expensive for sporadic small workloads. Dedicated instances ($0.60/hour) suit consistent, high-volume use. Compared to managed vector DBs, PostgresML can save up to 42% on vector database costs.
In short
Postgresml — Run GPU-accelerated machine learning and AI inside PostgreSQL with SQL. Best for PostgreSQL users adding ML without introducing new microservices, Developers building RAG chatbots on top of existing Postgres data, Data scientists needing GPU-accelerated embeddings directly in the database. Free to start; paid plans from $0.6/mo.
What's new in Postgresml
Checked 5 days agoAcross the latest 1 update: 1 feature update.
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.
- +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: September 2026
How we score →Key Features
- SQL API for all ML operations (pgml.embed, pgml.transform, pgml.train, pgml.predict)
- Text generation with Llama 3.1 (8B, 70B, 405B), Llama 3.2, Mistral, Mixtral, Phi-3
- Embedding generation with e5-small-v2, gte-base-en-v1.5, gte-large-en-v1.5, mxbai-embed-large-v1
- Vector index with HNSW or IVFFlat for fast KNN and ANN search
- Fine-tune LLMs on your own data within PostgreSQL (pgml.tune)
- Supervised learning: regression, classification, clustering (pgml.train)
- Model deployment monitoring and versioning (pgml.deploy)
- Streaming inference via pgml.transform_stream()
- Built-in data preprocessors for splitting and chunking
- Colocate data and compute—embed, serve, and store in one process
- Python and JavaScript SDKs (Korvus) for RAG pipelines
- Self-hosted open-source deployment
- Serverless cloud with burst GPU capacity
- Dedicated instances on major cloud providers
- VPC deployments for enterprise
About Postgresml
PostgresML is a PostgreSQL extension that brings machine learning and AI capabilities directly into your database. Developers and data scientists who already use Postgres can train models, generate embeddings, run inferences, and fine-tune LLMs using SQL queries, all without introducing separate vector databases, embedding APIs, or inference services. By colocating data and compute in one GPU-backed database, PostgresML simplifies your architecture, reduces vendor sprawl, and keeps your data under one roof. What makes PostgresML powerful is its breadth. You can generate text, summarize, and translate using open-weights models like Llama 3.1 (8B to 405B), Mistral, and Mixtral, generate embeddings with models like e5-small-v2 or gte-large-en-v1.5, and index them with HNSW or IVFFlat for fast KNN and ANN search. The platform also supports supervised learning (regression, classification, clustering) and fine-tuning LLMs on your own data. All of this is accessible through a SQL API, with Python and JavaScript SDKs (Korvus) that simplify building RAG pipelines. Deployment options range from self-hosted open-source for full control to serverless cloud with pay-per-use pricing, dedicated instances for consistent workloads, and VPC deployments for enterprises with strict security requirements. Serverless pricing starts at $7.50 per query hour (plus $0.25/GB storage per month), dedicated instances run $0.60 per instance hour, and enterprise plans are custom. New users get $100 in free usage credits. Compared to assembling a stack like Pinecone plus OpenAI, PostgresML claims 4x faster RAG chatbots, 10x faster embedding generation, and 42% cost savings on vector database spend. For teams prioritizing data privacy and operational simplicity, keeping ML inside Postgres is a pragmatic move—though it requires SQL fluency and, for self-hosted deployments, access to GPU hardware.
Behind the Verdict
PostgresML earns its keep for Postgres teams that are tired of duct-taping together a vector database, an embedding service, and an LLM API. The appeal is real: you keep everything in the database you already run in production, you avoid shipping data across a dozen vendors, and you get solid performance gains—the 10x faster embedding generation claim we can't verify independently, but the architecture makes sense. Where it shines: teams building RAG chatbots on existing Postgres data, data scientists who want to train models without exporting data, and any organization that needs to keep sensitive data on-premises. The SDKs (Korvus) for Python and JavaScript make it approachable if you're not a SQL purist, though SQL fluency is still the core requirement. Where it bites: if you're not on Postgres, this is a non-starter. There's no support for other databases. Self-hosting demands GPU hardware, which not every team has. And you're limited to open-source models—if you're locked into proprietary LLMs like GPT-4o, PostgresML won't help. The serverless pricing is transparent ($7.50 per query hour plus storage), but those costs can creep up if you run heavy workloads; the cost estimator helps, but it's rough. Compared to a stack like Pinecone plus OpenAI, PostgresML trades the flexibility of point solutions for simplicity. You get fewer moving parts, but you also get locked into one vendor's ecosystem. If your team already lives in Postgres, that's a fair trade. If you're DB-agnostic, you might be better off with a purpose-built vector DB. One caveat: the company claims 4x faster RAG chatbots and 42% savings over Pinecone+OpenAI—take those with a grain of salt, as they're vendor benchmarks. But even if the real numbers are half that, you'd still save meaningful cost and
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Real-world workflow fit
Concrete scenarios for the personas Postgresml actually fits — and what changes day-one when you adopt it.
You need to generate embeddings for 10 million customer feedback records stored in PostgreSQL to enable semantic search.
Outcome: Use pgml.embed() with a GPU-accelerated model like gte-large-en-v1.5 to generate embeddings in a single SQL query, then index with HNSW for fast retrieval.
You want to add a RAG chatbot to your app, but you're tired of managing Pinecone, OpenAI, and a bunch of glue code.
Outcome: Use Korvus SDK (JS or Python) to build a full RAG pipeline with PostgresML in one place. You store vectors and run models in the same PostgreSQL database, cutting your stack down to one service.
You need to fine-tune a Llama model on your proprietary data for a domain-specific text generation task.
Outcome: Use pgml.tune() to fine-tune the model directly on your data stored in Postgres, then deploy it with pgml.deploy() to serve inference with SQL.
Use Cases
- Build a RAG chatbot that retrieves facts from your database using vector search.
- Generate embeddings for millions of documents using GPU-accelerated models.
- Fine-tune an LLM on your proprietary data directly inside PostgreSQL.
- Run summarization and translation tasks on text stored in your database.
- Train classification or regression models on tabular data without moving it.
- Index and search vectors with HNSW or IVFFlat for high-performance retrieval.
- Perform semantic search across your database content.
Models Under the Hood
as of 2026-08-28
Limitations
- PostgresML is primarily designed for users comfortable with SQL and machine learning concepts, which may present a learning curve.
- Self-hosted deployments require GPU hardware for optimal performance.
- Serverless pricing is based on per-query-hour and storage, which may be variable.
- The platform supports open-source models, and dedicated plans allow use of any model on HuggingFace.
as of 2026-08-23
Verification history
We have re-verified Postgresml 7 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-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
- — 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 7 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
Organizations with existing PostgreSQL infrastructure and GPU hardware who want full control over ML workloads without per-query costs.
What this tier adds
Free, open-source extension; requires self-managed infrastructure and GPUs, so no per-query fees.
Serverless
From $7.50/query hour
Ideal for
Startups and developers building RAG apps who want to scale automatically and pay only for what they use.
What this tier adds
Pay-per-query-hour ($7.50/hr) with burst GPU capacity, including access to curated models and $100 free credits.
Dedicated
$0.60/instance hour
Ideal for
Teams with consistent production workloads that need dedicated hardware and support for any HuggingFace model.
What this tier adds
Flat $0.60 per instance hour with committed use discounts, dedicated support, and no per-query charges.
Enterprise
Custom
Ideal for
Large organizations with advanced security, compliance, or scale requirements needing VPC deployment and custom SLAs.
What this tier adds
Custom pricing includes VPC deployment, multiple GPUs, custom SLAs, and premium support.
Where the pricing makes sense
The company stage and team size where Postgresml's pricing actually pencils out — and where peers do it cheaper.
PostgresML's serverless pricing ($7.50/hour) is cheaper than assembling a Pinecone + OpenAI stack, but expensive for sporadic small workloads. Dedicated instances ($0.60/hour) suit consistent, high-volume use. Compared to managed vector DBs, PostgresML can save up to 42% on vector database costs.
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.
For developers already using Postgres, you can connect to a cloud database or self-host in under an hour. Full RAG pipelines with Korvus may take a few hours to a day, depending on your familiarity with the SDK and ML concepts.
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: Move vector data into PostgreSQL and use pgml.embed() to generate embeddings in-database, eliminating the separate vector DB.
- ↗To OpenAI/Pinecone stack: If you need proprietary models or a multi-vendor setup, you can export your data and use API-based embeddings and vector DBs, but you'll lose in-database ML integration.
Integrations
Resources & Guides
Tutorials & Learning
![What is PostgresML? [1 min demo]](https://img.youtube.com/vi/vpZ4l8Ym47M/mqdefault.jpg)
Official links
Tools that pair well with Postgresml
Common stack mates teams adopt alongside Postgresml, with the specific reason each pairing earns its keep.
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.
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