Seldon vs Snorkel AI

Only Seldon

• Seldon Core 2 declares models and pipelines as Kubernetes custom resources
• Automatic inference server selection, scaling, monitoring and audit logging from one manifest
• Composable data-centric pipelines connecting models, processing steps, custom logic and monitors over Kafka
• MLServer lightweight multi-framework inference server with REST and gRPC support
• Open Inference Protocol compatibility across model types
• A/B tests, canary deployments, shadow deployments and multi-armed bandits for production routing
• Multi-model serving with LRU memory swapping and overcommit to provision more models than hardware allows
• Real-time observability with every prediction logged and auditable via Prometheus, Grafana and custom dashboards

Only Snorkel AI

• Snorkel Data Series: curriculum-structured datasets with rubrics, reviewer guidance, difficulty tiers, and eval slices
• Custom data development for bespoke datasets, evals, and benchmark expansions targeting a named failure surface
• Specialized agents built on expert data and evaluated in real workflows with pass/fail criteria
• Well-specified expert-level task specs with target distributions, acceptance criteria, and verifier definitions written before data work begins
• Calibrated expert review, with reviewers trained against gold sets authored by Snorkel researchers and scored for agreement and bias
• Rubrics distilled into programmatic graders and fine-tuned evaluator models rather than human spot-checks alone
• Author, multi-reviewer, and final-adjudicator pipeline with full audit trails and label-level provenance
• Edge-case coverage across difficulty bands and failure modes via expert-authored seeds and templated generation

Only Seldon

• Serving real-time ML inference inside a regulated bank's own Kubernetes cluster
• Running drift and outlier detection alongside live predictions in pharmaceutical model pipelines
• Promoting a challenger model through canary or shadow deployment without downtime
• Consolidating many small models onto shared inference servers to reduce GPU spend
• Adding explainability to every prediction for audit and compliance review
• Deploying generative AI workflows with prompt orchestration and guardrails on existing Kubernetes infrastructure
• Standardizing model handoff between data science teams and platform engineering
• Keeping inference and data on-premise where cloud egress is not permitted

Only Snorkel AI

• Building post-training and RL datasets for coding agents evaluated on senior-level software engineering tasks
• Constructing runnable environments so computer-use agents can be trained and scored on long-horizon desktop and web workflows
• Designing rubrics and programmatic graders for open-ended outputs where correctness is hard to define
• Standing up environment-first evaluation for agents in regulated work such as insurance underwriting or financial reasoning
• Sourcing legal research evaluation data through partnerships like BigLaw Bench: Research with Harvey
• Deploying a specialized enterprise agent grounded in a company's own tools, codebase, corpus, and data permissions
• Diagnosing why an evaluation rubric breaks down using the RIFT failure mode taxonomy from Snorkel AI research

Only Seldon

• Prometheus
• Grafana
• Kafka
• Jaeger
• Elasticsearch
• Triton Inference Server
• MLflow
• Weights & Biases

Only Snorkel AI

Nothing exclusive in this list.

Seldon

• Open Source (Seldon Core 2, MLServer, Alibi Detect, Alibi Explain) $0/mo or $0/yr

  Seldon Core 2 Kubernetes-native MLOps and LLMOps deployment engine · MLServer multi-framework inference server with REST, gRPC and Open Inference Protocol · Alibi Detect for outlier, adversarial and drift detection · Alibi Explain for local, global, black-box and white-box explanation methods · Self-hosted on your own Kubernetes cluster

• LLM Module Custom quote

  Gen AI workflow deployment with prompt orchestration · Built-in and configurable guardrails for LLM deployment · Observability and production-ready scaling for generative workloads · Priced through a scheduled platform briefing; no public figure published

• MPM Module Custom quote

  Model Performance Metrics for classification and regression models · Real-time quality insights on production models · Detection of performance degradation before business impact · Priced through a scheduled platform briefing; no public figure published

• Enterprise Platform Custom quote

  Oversight and governance for ML and LLM deployments at scale · Enhanced authentication and team controls · Audit trails for regulated industries · Priced through a scheduled platform briefing; no public figure published

Snorkel AI

• Custom engagement (quote-based) Contact sales

  No pricing page is published on snorkel.ai and no list price appears on the homepage, company page, or llms.txt · Homepage calls to action are "Request dataset samples" and "Talk to our team" rather than a signup or free trial · Scope is negotiated per dataset, benchmark, evaluation harness, environment, or specialized agent program · Snorkel Data Series, custom data development, and specialized agents are all sold through the same sales conversation · Free resources exist outside the commercial contract, including public benchmark leaderboards, research papers, and the Open Benchmarks Grants program