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Evaluation guide
How to evaluate AI and ML platforms
This guide is for data science, ML engineering and platform leads choosing where models will be built, tuned, governed and served. It suits teams that have working notebooks and pilots and now need a platform that risk, security and finance will sign off for production.
In short
Use this guide to compare AI and machine learning platforms on what decides whether models reach production: model choice and portability, testing on your own data, governance and lineage, serving cost, data access controls and where workloads can run. Then shortlist three to five companies on Techarda and test them on one model your team already needs to ship.
What’s changing in AI & ML Platforms
Before you compare anyone, it helps to know what has shifted in the market, because it changes which questions matter.
Most teams adapt models rather than train them
Much of the work has moved from training new models to choosing, tuning and testing existing ones. Platforms built around classic training pipelines are adding model catalogues, fine-tuning and retrieval, with uneven depth.
Open-weight models are a real option
Capable models with downloadable weights let you run inference in your own cloud account or data centre. That helps with residency and cost control, and moves patching, scaling and safety testing onto your team.
Governance is expected from the first model
Registries, lineage and test records used to be added late. Standards such as ISO/IEC 42001 and the NIST AI Risk Management Framework have set expectations that boards and auditors now apply from the first production model.
Serving is where the bill grows
Training is a project cost. Inference runs every day and grows with use, so accelerator utilisation, batching and model size usually decide the long-run bill more than the training run does.
What to judge vendors on
Those shifts shape the criteria below. Each one says why it matters and what to ask, so you can take it straight into a vendor call.
| Criterion | Why it matters | What to ask vendors |
|---|---|---|
| 1Model choice and portability | The best model for a task changes often. A platform tied to one model family or one cloud limits what you can use next year. | “Which proprietary and open-weight models can we use, can we bring our own, and what would it take to move a model we tuned off your platform?” |
| 2Testing on your own data | Scores on public benchmarks say little about your task. You need repeatable tests on your own data that run before every release. | “How do we build test sets from our own data, compare model versions against them, and block a deployment that falls below a threshold we set?” |
| 3Governance, lineage and registry | When a model gives a bad result, you need to show which data, code and version produced it. Regulators and auditors will ask the same thing. | “Can we trace any deployed model back to its training data, code and approvals, and export that record to our own systems?” |
| 4Data access and security controls | Training and retrieval pull from sensitive sources. A platform that copies data into its own store creates a second copy to secure and govern. | “Can pipelines read data where it lives under our existing access controls, or do you need copies, and if so how are they secured and deleted?” |
| 5Serving cost and performance | Latency targets and request volumes decide the hardware, and the hardware decides the cost. Idle accelerators are expensive. | “What would serving a model of our size cost at our expected volume, can capacity scale to zero or be shared, and what spending limits can we set?” |
| 6Where training and inference can run | Residency rules and data gravity may require inference in a specific region, in your own cloud account or on your own hardware. | “Can training and inference run in our cloud account, in a named region or on our own hardware, and which features are missing in each?” |
| 7Monitoring in production | Models degrade as data and user behaviour shift. Without drift and quality monitoring, you hear about it from customers first. | “How do you detect drift, quality drops and unsafe outputs in production, and can alerts go to our existing monitoring and incident tools?” |
| 8Fit with how your team works | A platform your data scientists dislike gets bypassed with notebooks on laptops, and your governance goes with it. | “Which languages, frameworks and notebook tools do you support natively, and how much of our current code would need to change?” |
Trade-offs to settle early
No vendor does well on every criterion, and some pull against each other. Decide where you stand on these before the demos start, or the demos will decide for you.
End-to-end platform or assembled components
One platform gives you a single registry, security model and bill. Assembling open-source and specialist tools gives you more choice at each layer, and your team owns the integration and the upgrades.
Hosted models or self-hosted open weights
Hosted model APIs are quick to start with and someone else runs them. Self-hosting open-weight models gives you control over data, versions and steady-state cost, and makes capacity, patching and safety testing your job.
Your data platform or a separate ML platform
Building models where your data already lives cuts copying and access work. A dedicated ML platform may go deeper on experiments and serving, at the cost of another integration and another set of permissions.
Fine-tuning or retrieval
Fine-tuning can improve results on a narrow task but needs retesting each time the base model changes. Retrieval keeps answers current without retraining, and its quality depends on your search and your content. Platform support for each varies, so decide which you will rely on.
Speed to the first model or controls for the fiftieth
A platform that gets a pilot live in a week may lack the registry, approvals and access controls you need once many teams ship models. Weigh the second more heavily if the platform will be shared across the organisation.
Red flags
As the answers come back, watch for these. One on its own isn’t a deal breaker, but it deserves a follow-up question in writing.
- Accuracy claims based only on public benchmarks, with no offer to test on your data.
- No way to export tuned models, model artefacts or lineage records.
- Training or retrieval requires copying sensitive data into vendor storage, with no clear deletion terms.
- Serving quoted per hour of hardware, with no view of utilisation or cost per request.
- No clear answer when you ask whether your data, prompts or tuned models improve the vendor’s own models.
- No reference customer running models in production under rules like the ones you work under.
Build your shortlist
Pick one model your team needs in production, then use Techarda to narrow the field to three to five companies worth testing against it.
- Write down the use case, its data sources, its latency target and where it must run.
- Keep companies that support your data platform, cloud and residency needs today. Roadmap support does not count.
- Include your current data or cloud platform vendor and at least one specialist, so you weigh the depth a specialist adds against one more supplier.
- Read each company’s recent stories on Techarda for pricing changes, model licence terms and acquisitions. This category moves quickly.
- Take the open practitioner questions below into vendor calls, compare side by side, then test with no more than three.
Companies to consider in AI & ML Platforms
Listed by recent activity on Techarda (what people are reading, following and discussing). The order says nothing about quality, market share or fit for your needs.
Latest AI & ML Platforms stories
Once you have names, recent news is where pricing changes, acquisitions and outages show up first. These are the five newest stories in this category.
What practitioners are asking
News tells you what vendors announced. These open questions show what teams are still trying to work out, with the least-answered first.
New
What does your generative AI cost per useful answer look like?
Token spend, retrieval cost, caching, model routing. How are you measuring and reducing cost without hurting quality?
New
What does your model evaluation pipeline actually look like in production?
Offline evals, golden sets, LLM-as-judge, human review: what are you running before a model or prompt change ships, and what has caught real regressions?
Where evidence is thin
A shortlist is only as good as the evidence behind it, and ours is still growing category by category. Here is what’s missing in AI & ML Platforms right now.
20 of 20 companies have no published reviews
If you’ve run one of these in production, a short review helps the next team decide. Reviews are moderated before they appear.
3 questions with no answers yet
An answer from someone who has made the same decision is often more useful than any guide. Share what worked, what didn’t and what you’d check next time.
Answer a AI & ML Platforms questionFrequently asked questions
What should I ask AI & ML Platforms vendors about model choice and portability?
The best model for a task changes often. A platform tied to one model family or one cloud limits what you can use next year. Ask: Which proprietary and open-weight models can we use, can we bring our own, and what would it take to move a model we tuned off your platform?
What should I ask AI & ML Platforms vendors about testing on your own data?
Scores on public benchmarks say little about your task. You need repeatable tests on your own data that run before every release. Ask: How do we build test sets from our own data, compare model versions against them, and block a deployment that falls below a threshold we set?
What should I ask AI & ML Platforms vendors about governance, lineage and registry?
When a model gives a bad result, you need to show which data, code and version produced it. Regulators and auditors will ask the same thing. Ask: Can we trace any deployed model back to its training data, code and approvals, and export that record to our own systems?
What should I ask AI & ML Platforms vendors about serving cost and performance?
Latency targets and request volumes decide the hardware, and the hardware decides the cost. Idle accelerators are expensive. Ask: What would serving a model of our size cost at our expected volume, can capacity scale to zero or be shared, and what spending limits can we set?
New to some of the terms in this guide? The glossary explains AI guardrails, Embeddings, Fine-tuning, Inference, Large language model (LLM) and 2 more AI & ML Platforms terms.
This guide is written by Techarda editors to our editorial standards. No vendor paid to appear in it or saw it before publication. See our methodology, the trust dashboard or go back to the AI & ML Platforms hub.