Cohere, Databricks, Together AI, Fireworks AI, and Replicate come up in accounts when a CIO has already decided they want AI capabilities but hasn't committed to a hyperscaler platform — or has committed and hit a wall. The wall is usually specific: data that can't move to AWS, a cost ceiling that Bedrock's pricing won't clear, a latency requirement that matters for agentic workflows, or a data platform relationship that already exists and makes adding another vendor feel redundant. Knowing which specialty provider fits which constraint, and why, is what keeps you in the conversation when the buyer's technical team starts naming alternatives you haven't profiled.
Profiles
Cohere
What it is: An enterprise AI company that builds text understanding and generation models for business deployment, with a particular emphasis on retrieval-augmented generation and private hosting.
What it does: Cohere's product line maps cleanly onto the RAG stack: Command for generation, Embed for producing vector representations of documents and queries, and Rerank for scoring retrieved results by relevance before they reach the model. An organization building a document retrieval system — a policy search tool, a contract analysis workflow, an internal knowledge base — can assemble that stack entirely from Cohere's catalog. The company also offers fine-tuning on Command and supports deployment inside a customer's own cloud VPC or on-premises infrastructure.
Who's behind it: Cohere was founded in 2019 by Aidan Gomez, Nick Frosst, and Ivan Zhang. Gomez is a co-author of "Attention Is All You Need," the 2017 paper that introduced the transformer architecture. The company is headquartered in Toronto and has raised approximately $445 million across multiple rounds, with investors including Nvidia, Oracle, and Salesforce Ventures.
What makes it distinct: Cohere is the only provider in this group that treats private deployment as a first-class product rather than a premium add-on. When an agency's data classification requirements prohibit sending queries to a shared inference endpoint — even one hosted by a major cloud provider — Cohere's architecture is designed to accommodate that constraint. The model runs inside the customer's boundary. VPC and on-premises deployment are documented, current options, not roadmap items.
Databricks
What it is: A data and AI platform company built around the lakehouse architecture, which combines data warehouse and data lake capabilities in a single governed environment.
What it does: Databricks entered the model market through two routes: DBRX, an open-weight model the company trained and released in early 2024, and Mosaic AI, its platform for model serving, fine-tuning, and evaluation. Both run inside the Databricks environment, which means they operate against data governed by Unity Catalog, the company's unified data governance layer. An organization that has already structured its data in Unity Catalog can run inference against that data without building a separate pipeline to move it to an external model endpoint.
Who's behind it: Databricks was founded in 2013 by Ali Ghodsi and several co-founders from the AMPLab at UC Berkeley, the same group that created Apache Spark. The company is headquartered in San Francisco and was valued at approximately $62 billion in its most recent funding round. Its customer base skews heavily toward organizations with mature data engineering practices: financial services, healthcare, and large federal agencies with established data lake infrastructure.
What makes it distinct: The integration point is the differentiator. DBRX is competitive but not exceptional. What Databricks sells is the elimination of a data movement step — the model lives where the data lives, governed by the same access controls that already exist in Unity Catalog. For an organization that has spent three years building a governed data environment, that's a meaningful argument.
Together AI
What it is: A cloud inference platform that hosts open-weight models via API, with pricing and throughput positioned below hyperscaler rates.
What it does: Together hosts the major open-weight model families — Llama 3, Mistral, Qwen, Gemma, and others — and serves them through a standard API that's compatible with the OpenAI client library. It also offers fine-tuning on supported models and a dedicated endpoint option for teams that need reserved capacity. Together's inference cluster is purpose-built for open-weight model serving, which means it doesn't carry the overhead of a general-purpose cloud platform.
Who's behind it: Together AI was founded in 2022 by Vipul Ved Prakash and a team with backgrounds in distributed systems and machine learning infrastructure. The company is based in San Francisco and has raised approximately $220 million, with investors including Salesforce Ventures, Nvidia, and Kleiner Perkins.
What makes it distinct: Pricing. Together's published per-token rates for open-weight models run roughly 30–50% below comparable Bedrock pricing on the same model families. [Pricing figures are illustrative benchmarks; verify against current published rates before use in a customer conversation.] For a workload running millions of tokens per day, that gap is a budget line item, not a rounding error.
Fireworks AI
What it is: An inference optimization company that hosts open-weight models with a focus on minimizing latency, particularly time-to-first-token.
What it does: Fireworks serves open-weight models through an optimized inference stack built by engineers from Meta's PyTorch team. The company publishes benchmark data comparing its time-to-first-token against other providers on the same models, and the numbers are consistently faster. It also offers FireFunction, a set of models fine-tuned specifically for structured output and function calling — relevant for agentic workflows where the model needs to reliably return JSON or invoke tools. Fireworks supports compound AI system architectures, meaning it's designed to handle the multi-step, multi-call patterns that agents generate.
Who's behind it: Fireworks AI was founded in 2022 by Lin Qiao, who led the PyTorch team at Meta, along with colleagues from the same organization. The company is based in San Francisco and has raised approximately $77 million.
What makes it distinct: Latency, specifically for agentic use cases. When a model is being called fifteen times per task rather than once per query, time-to-first-token compounds. Fireworks' published benchmarks show time-to-first-token in the 200–300ms range for Llama 3 70B under moderate load, compared to 600–900ms for the same model on hyperscaler endpoints. [Benchmark figures are illustrative; verify against current published data before use.] Speed, not price, is the argument.
Replicate
What it is: A model hosting platform with pay-per-second billing and a catalog of thousands of community and commercial models.
What it does: Replicate runs models in containerized environments using Cog, its open-source model packaging tool. The catalog spans text generation, image generation, audio, video, and specialized models that don't appear on hyperscaler platforms. Billing is per second of compute time with no minimum spend, which means a team can run five different models in an afternoon for a few dollars. Replicate also supports model deployment from the catalog into a private endpoint if a team decides to productionize something they found through exploration.
Who's behind it: Replicate was founded in 2019 by Ben Firshman, Andreas Jansson, and Zeke Sikelianos, with backgrounds in developer tooling and infrastructure. The company is based in San Francisco and has raised approximately $40 million.
What makes it distinct: Catalog breadth and zero commitment. Replicate hosts models that aren't available anywhere else in this list — niche image models, speech-to-text variants, specialized code models — and charges nothing until you run something. For a team in evaluation mode, trying to determine which model fits a specific task before committing to a platform, Replicate removes the procurement step entirely.
Comparison Strategy
Scenario mapping is the right structure here. The organizing question for this field isn't "which provider is best" — it's "which provider wins under which conditions." Clustering by technical trait would obscure the decision logic; a flat table would suggest false equivalence. Every provider appears below; no provider is omitted from a relevant scenario.
Scenario 1: Data classification requirements prevent external inference
Winner: Cohere. When an agency's data can't leave a defined boundary — because of classification level, contractual obligation, or regulatory requirement — the relevant question is where inference actually runs. Cohere's VPC and on-premises deployment options are documented and in production use. The hyperscalers offer private endpoint options, but they route through shared infrastructure in ways that don't satisfy every classification requirement. Databricks can also compete here if the data is already in a Databricks environment, but Cohere is the cleaner answer when the requirement is model isolation rather than data platform integration.
Scenario 2: The data is already in the platform
Winner: Databricks. If an agency has invested in a Databricks lakehouse and Unity Catalog governs its data access, adding Mosaic AI inference is an extension of an existing relationship, not a new vendor evaluation. The data movement problem disappears. The alternative is building a pipeline to extract data, send it to an external endpoint, and manage the security and latency implications of that transfer. For organizations with mature Databricks deployments, that's a real cost that Mosaic AI eliminates.
Scenario 3: Open-weight models at lower cost or lower latency than hyperscalers
Winners: Together AI and Fireworks AI, for different buyers. These two providers are competing for the same workload — open-weight model inference at scale — and the differentiation between them is narrower than either company's marketing suggests. If the primary constraint is cost, Together's pricing structure is the argument. If the primary constraint is latency, particularly for agentic workflows with high call frequency, Fireworks' inference optimization is the argument. An organization that needs both will have to benchmark against their specific model and call pattern; there's no clean answer that holds across workloads.
Scenario 4: Evaluation and prototyping without infrastructure commitment
Winner: Replicate. A team that needs to evaluate six different models before committing to a platform doesn't want to stand up infrastructure for each one. Replicate's catalog and billing model are designed for exactly this phase. The limitation is that Replicate isn't the right answer for production workloads at scale — the per-second billing model and shared infrastructure don't optimize for throughput the way Together or Fireworks do. Replicate is where you find the model; one of the others is where you run it in production.
Field Language Guide
| Don't say | Do say | Why it matters |
|---|---|---|
| "Open-source models" | "Open-weight models" | The weights are public; the training data and code often aren't. Buyers who know the difference will notice. |
| "Cohere is more private than Bedrock" | "Cohere offers VPC and on-premises deployment options that keep inference within your defined boundary" | "More private" is unverifiable. The deployment architecture is a documented fact. |
| "Together and Fireworks are cheaper" | "Together publishes per-token pricing that runs below Bedrock rates for the same open-weight models" | Cheaper than what, for which model, at which volume? Make the comparison specific or don't make it. |
| "Fireworks is the fastest" | "Fireworks publishes time-to-first-token benchmarks for specific models; ask them for numbers on the model you're evaluating" | "Fastest" is a claim. Benchmarks are checkable. |
| "Databricks does AI now" | "If your data is already in Unity Catalog, Databricks inference removes the data movement step" | Lead with the integration, not the model. |
| "Replicate has everything" | "Replicate's catalog includes models that aren't available on hyperscaler platforms, with no minimum spend to evaluate them" | "Everything" is false and sounds like marketing. Catalog breadth plus zero commitment is the actual argument. |
| "You should check their SOC 2" | "Ask which deployment model they're proposing and whether inference stays within your compliance boundary" | SOC 2 covers the vendor's internal controls, not where your data goes during inference. |
| "These are alternatives to the big cloud providers" | "These providers win in specific circumstances where the hyperscaler architecture doesn't fit the requirement" | "Alternative" implies fallback. These are wins in their own right. |
| "Cohere is good for RAG" | "Cohere builds Embed and Rerank as first-class products alongside Command — the full retrieval stack from one vendor" | "Good for RAG" is vague. The product architecture is the argument. |
| "Fine-tuning is available" | "Together and Databricks both support fine-tuning on open-weight models; the question is whether you want that capability bundled with your data platform" | Fine-tuning availability is table stakes. Where it lives relative to the data is the decision point. |
| "It depends on your use case" | "The decision usually comes down to one of four things: where the data lives, whether it can leave your boundary, how many model calls your workflow makes, and whether you're evaluating or in production" | "It depends" ends the conversation. Four criteria give the buyer something to work with. |
Okta Concept Mapping
The closest IDAM analog for specialty provider selection is identity federation — specifically, the question of which IdP you trust and under what conditions. In federation, you're establishing a trust relationship with an authority outside your direct control; the protocol (SAML, OIDC) defines the terms of that trust and makes it auditable. Specialty AI provider selection follows similar logic: you're deciding which external system gets to process your organization's data, and the terms of that trust are defined by contracts, audit reports, and deployment architecture. Where the analogy breaks: federation trust is governed by a published protocol that both parties implement the same way. There's no equivalent standard for AI provider trust — "the model runs in your VPC" means different things depending on the vendor's architecture, and there's no shared specification to check it against. In a buyer conversation, skip "do they have SOC 2" and go straight to: show me the network diagram for where inference actually runs.